WorldWideScience

Sample records for human behavior recognition

  1. Comparison of Object Recognition Behavior in Human and Monkey

    Science.gov (United States)

    Rajalingham, Rishi; Schmidt, Kailyn

    2015-01-01

    Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to

  2. Human behavior recognition using a context-free grammar

    Science.gov (United States)

    Rosani, Andrea; Conci, Nicola; De Natale, Francesco G. B.

    2014-05-01

    Automatic recognition of human activities and behaviors is still a challenging problem for many reasons, including limited accuracy of the data acquired by sensing devices, high variability of human behaviors, and gap between visual appearance and scene semantics. Symbolic approaches can significantly simplify the analysis and turn raw data into chains of meaningful patterns. This allows getting rid of most of the clutter produced by low-level processing operations, embedding significant contextual information into the data, as well as using simple syntactic approaches to perform the matching between incoming sequences and models. We propose a symbolic approach to learn and detect complex activities through the sequences of atomic actions. Compared to previous methods based on context-free grammars, we introduce several important novelties, such as the capability to learn actions based on both positive and negative samples, the possibility of efficiently retraining the system in the presence of misclassified or unrecognized events, and the use of a parsing procedure that allows correct detection of the activities also when they are concatenated and/or nested one with each other. An experimental validation on three datasets with different characteristics demonstrates the robustness of the approach in classifying complex human behaviors.

  3. Human ear recognition by computer

    CERN Document Server

    Bhanu, Bir; Chen, Hui

    2010-01-01

    Biometrics deals with recognition of individuals based on their physiological or behavioral characteristics. The human ear is a new feature in biometrics that has several merits over the more common face, fingerprint and iris biometrics. Unlike the fingerprint and iris, it can be easily captured from a distance without a fully cooperative subject, although sometimes it may be hidden with hair, scarf and jewellery. Also, unlike a face, the ear is a relatively stable structure that does not change much with the age and facial expressions. ""Human Ear Recognition by Computer"" is the first book o

  4. Challenges in human behavior understanding

    NARCIS (Netherlands)

    Salah, A.A.; Gevers, T.; Sebe, N.; Vinciarelli, A.

    2010-01-01

    Recent advances in pattern recognition has allowed computer scientists and psychologists to jointly address automatic analysis of of human behavior via computers. The Workshop on Human Behavior Understanding at the International Conference on Pattern Recognition explores a number of different

  5. Towards human behavior recognition based on spatio temporal features and support vector machines

    Science.gov (United States)

    Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

    2017-03-01

    Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

  6. Suspicious Behavior Detection System for an Open Space Parking Based on Recognition of Human Elemental Actions

    Science.gov (United States)

    Inomata, Teppei; Kimura, Kouji; Hagiwara, Masafumi

    Studies for video surveillance applications for preventing various crimes such as stealing and violence have become a hot topic. This paper proposes a new video surveillance system that can detect suspicious behaviors such as a car break-in and vandalization in an open space parking, and that is based on image processing. The proposed system has the following features: it 1)deals time series data flow, 2)recognizes “human elemental actions” using statistic features, and 3)detects suspicious behavior using Subspace method and AdaBoost. We conducted the experiments to test the performance of the proposed system using open space parking scenes. As a result, we obtained about 10.0% for false positive rate, and about 4.6% for false negative rate.

  7. Human activity recognition and prediction

    CERN Document Server

    2016-01-01

    This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .

  8. Shopping Behavior Recognition using a Language Modeling Analogy

    NARCIS (Netherlands)

    Popa, M.C.; Rothkrantz, L.J.M.; Wiggers, P.; Shan, C.

    2012-01-01

    Automatic understanding and recognition of human shopping behavior has many potential applications, attracting an increasing interest inthe market- ing domain. The reliability and performance of the automatic recognition system is highly in uenced by the adopted theoretical model of behavior. In

  9. The coevolution of recognition and social behavior.

    Science.gov (United States)

    Smead, Rory; Forber, Patrick

    2016-05-26

    Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation.

  10. Smartphone-based human activity recognition

    OpenAIRE

    Reyes Ortiz, Jorge Luis

    2014-01-01

    Cotutela Universitat Politècnica de Catalunya i Università degli Studi di Genova Human Activity Recognition (HAR) is a multidisciplinary research field that aims to gather data regarding people's behavior and their interaction with the environment in order to deliver valuable context-aware information. It has nowadays contributed to develop human-centered areas of study such as Ambient Intelligence and Ambient Assisted Living, which concentrate on the improvement of people's Quality of Lif...

  11. A Review of Human Activity Recognition Methods

    Directory of Open Access Journals (Sweden)

    Michalis eVrigkas

    2015-11-01

    Full Text Available Recognizing human activities from video sequences or still images is a challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.

  12. Fiscal 1997 report on the introductory study. Human behavior recognition evaluation technology; 1997 nendo sentan kenkyu hokokusho. Ningen kodo ninchi hyoka gijutsu

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-03-01

    Importance of the human behavior technology was paid attention to to get true safety and comfortableness of life through adaptability of products/systems to all humans. In consideration of the social and technological background, the human behavior recognition evaluation technology avoids economic/social losses caused by accidents and troubles and also provides the safe life environment for living people including the aged. Further, the gist of the project was proposed to make it clear that it can give a course of making things from a viewpoint of development of products dealing with individuals (personal fit) and can give new added values and contribute to heightening of international competitiveness at the same time. Development is made of technology of dividing concrete behavior patterns into types and also accumulating them in usable forms at the time of product design and in emergency and of technology of measuring all the time information on human behaviors in daily life without restrictions and on site. Supporting technology is developed for making the most of behavior information of users for product design and in emergency. Effects of the spread are also estimated. 76 refs., 13 figs., 9 tabs.

  13. Human body contour data based activity recognition.

    Science.gov (United States)

    Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

    2013-01-01

    This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

  14. A Review on Video-Based Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Shian-Ru Ke

    2013-06-01

    Full Text Available This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.

  15. Behavioral model of visual perception and recognition

    Science.gov (United States)

    Rybak, Ilya A.; Golovan, Alexander V.; Gusakova, Valentina I.

    1993-09-01

    In the processes of visual perception and recognition human eyes actively select essential information by way of successive fixations at the most informative points of the image. A behavioral program defining a scanpath of the image is formed at the stage of learning (object memorizing) and consists of sequential motor actions, which are shifts of attention from one to another point of fixation, and sensory signals expected to arrive in response to each shift of attention. In the modern view of the problem, invariant object recognition is provided by the following: (1) separated processing of `what' (object features) and `where' (spatial features) information at high levels of the visual system; (2) mechanisms of visual attention using `where' information; (3) representation of `what' information in an object-based frame of reference (OFR). However, most recent models of vision based on OFR have demonstrated the ability of invariant recognition of only simple objects like letters or binary objects without background, i.e. objects to which a frame of reference is easily attached. In contrast, we use not OFR, but a feature-based frame of reference (FFR), connected with the basic feature (edge) at the fixation point. This has provided for our model, the ability for invariant representation of complex objects in gray-level images, but demands realization of behavioral aspects of vision described above. The developed model contains a neural network subsystem of low-level vision which extracts a set of primary features (edges) in each fixation, and high- level subsystem consisting of `what' (Sensory Memory) and `where' (Motor Memory) modules. The resolution of primary features extraction decreases with distances from the point of fixation. FFR provides both the invariant representation of object features in Sensor Memory and shifts of attention in Motor Memory. Object recognition consists in successive recall (from Motor Memory) and execution of shifts of attention and

  16. Behavioral biometrics for verification and recognition of malicious software agents

    Science.gov (United States)

    Yampolskiy, Roman V.; Govindaraju, Venu

    2008-04-01

    Homeland security requires technologies capable of positive and reliable identification of humans for law enforcement, government, and commercial applications. As artificially intelligent agents improve in their abilities and become a part of our everyday life, the possibility of using such programs for undermining homeland security increases. Virtual assistants, shopping bots, and game playing programs are used daily by millions of people. We propose applying statistical behavior modeling techniques developed by us for recognition of humans to the identification and verification of intelligent and potentially malicious software agents. Our experimental results demonstrate feasibility of such methods for both artificial agent verification and even for recognition purposes.

  17. Transfer Learning for Rodent Behavior Recognition

    NARCIS (Netherlands)

    Lorbach, M.T.; Poppe, R.W.; van Dam, Elsbeth; Veltkamp, R.C.; Noldus, Lucas

    2016-01-01

    Many behavior recognition systems are trained and tested on single datasets limiting their application to comparable datasets. While retraining the system with a novel dataset is possible, it involves laborious annotation effort. We propose to minimize the annotation effort by reusing the knowledge

  18. Making Activity Recognition Robust against Deceptive Behavior.

    Directory of Open Access Journals (Sweden)

    Sohrab Saeb

    Full Text Available Healthcare services increasingly use the activity recognition technology to track the daily activities of individuals. In some cases, this is used to provide incentives. For example, some health insurance companies offer discount to customers who are physically active, based on the data collected from their activity tracking devices. Therefore, there is an increasing motivation for individuals to cheat, by making activity trackers detect activities that increase their benefits rather than the ones they actually do. In this study, we used a novel method to make activity recognition robust against deceptive behavior. We asked 14 subjects to attempt to trick our smartphone-based activity classifier by making it detect an activity other than the one they actually performed, for example by shaking the phone while seated to make the classifier detect walking. If they succeeded, we used their motion data to retrain the classifier, and asked them to try to trick it again. The experiment ended when subjects could no longer cheat. We found that some subjects were not able to trick the classifier at all, while others required five rounds of retraining. While classifiers trained on normal activity data predicted true activity with ~38% accuracy, training on the data gathered during the deceptive behavior increased their accuracy to ~84%. We conclude that learning the deceptive behavior of one individual helps to detect the deceptive behavior of others. Thus, we can make current activity recognition robust to deception by including deceptive activity data from a few individuals.

  19. Sugar recognition by human galactokinase

    Directory of Open Access Journals (Sweden)

    Timson David J

    2003-11-01

    Full Text Available Abstract Background Galactokinase catalyses the first committed step of galactose catabolism in which the sugar is phosphorylated at the expense of MgATP. Recent structural studies suggest that the enzyme makes several contacts with galactose – five side chain and two main chain hydrogen bonds. Furthermore, it has been suggested that inhibition of galactokinase may help sufferers of the genetic disease classical galactosemia which is caused by defects in another enzyme of the pathway galactose-1-phosphate uridyl transferase. Galactokinases from different sources have a range of substrate specificities and a diversity of kinetic mechanisms. Therefore only studies on the human enzyme are likely to be of value in the design of therapeutically useful inhibitors. Results Using recombinant human galactokinase expressed in and purified from E. coli we have investigated the sugar specificity of the enzyme and the kinetic consequences of mutating residues in the sugar-binding site in order to improve our understanding of substrate recognition by this enzyme. D-galactose and 2-deoxy-D-galactose are substrates for the enzyme, but N-acetyl-D-galactosamine, L-arabinose, D-fucose and D-glucose are all not phosphorylated. Mutation of glutamate-43 (which forms a hydrogen bond to the hydroxyl group attached to carbon 6 of galactose to alanine results in only minor changes in the kinetic parameters of the enzyme. Mutation of this residue to glycine causes a ten-fold drop in the turnover number. In contrast, mutation of histidine 44 to either alanine or isoleucine results in insoluble protein following expression in E. coli. Alteration of the residue that makes hydrogen bonds to the hydroxyl attached to carbons 3 and 4 (aspartate 46 results in an enzyme that although soluble is essentially inactive. Conclusions The enzyme is tolerant to small changes at position 2 of the sugar ring, but not at positions 4 and 6. The results from site directed mutagenesis could

  20. Face Recognition in Humans and Machines

    Science.gov (United States)

    O'Toole, Alice; Tistarelli, Massimo

    The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

  1. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

    Directory of Open Access Journals (Sweden)

    Muhammad Hameed Siddiqi

    2013-12-01

    Full Text Available Over the last decade, human facial expressions recognition (FER has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER.

  2. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

    Science.gov (United States)

    Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

    2013-01-01

    Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

  3. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-07-05

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  4. Human recognition at a distance in video

    CERN Document Server

    Bhanu, Bir

    2010-01-01

    Most biometric systems employed for human recognition require physical contact with, or close proximity to, a cooperative subject. Far more challenging is the ability to reliably recognize individuals at a distance, when viewed from an arbitrary angle under real-world environmental conditions. Gait and face data are the two biometrics that can be most easily captured from a distance using a video camera. This comprehensive and logically organized text/reference addresses the fundamental problems associated with gait and face-based human recognition, from color and infrared video data that are

  5. Robust Behavior Recognition in Intelligent Surveillance Environments

    Directory of Open Access Journals (Sweden)

    Ganbayar Batchuluun

    2016-06-01

    Full Text Available Intelligent surveillance systems have been studied by many researchers. These systems should be operated in both daytime and nighttime, but objects are invisible in images captured by visible light camera during the night. Therefore, near infrared (NIR cameras, thermal cameras (based on medium-wavelength infrared (MWIR, and long-wavelength infrared (LWIR light have been considered for usage during the nighttime as an alternative. Due to the usage during both daytime and nighttime, and the limitation of requiring an additional NIR illuminator (which should illuminate a wide area over a great distance for NIR cameras during the nighttime, a dual system of visible light and thermal cameras is used in our research, and we propose a new behavior recognition in intelligent surveillance environments. Twelve datasets were compiled by collecting data in various environments, and they were used to obtain experimental results. The recognition accuracy of our method was found to be 97.6%, thereby confirming the ability of our method to outperform previous methods.

  6. Human action recognition with depth cameras

    CERN Document Server

    Wang, Jiang; Wu, Ying

    2014-01-01

    Action recognition technology has many real-world applications in human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. The commoditization of depth sensors has also opened up further applications that were not feasible before. This text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, includi

  7. Probabilistic recognition of human faces from video

    DEFF Research Database (Denmark)

    Zhou, Saohua; Krüger, Volker; Chellappa, Rama

    2003-01-01

    Recognition of human faces using a gallery of still or video images and a probe set of videos is systematically investigated using a probabilistic framework. In still-to-video recognition, where the gallery consists of still images, a time series state space model is proposed to fuse temporal...... of the identity variable produces the recognition result. The model formulation is very general and it allows a variety of image representations and transformations. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach for both still-to-video and video-to-video...... information in a probe video, which simultaneously characterizes the kinematics and identity using a motion vector and an identity variable, respectively. The joint posterior distribution of the motion vector and the identity variable is estimated at each time instant and then propagated to the next time...

  8. Average Gait Differential Image Based Human Recognition

    Directory of Open Access Journals (Sweden)

    Jinyan Chen

    2014-01-01

    Full Text Available The difference between adjacent frames of human walking contains useful information for human gait identification. Based on the previous idea a silhouettes difference based human gait recognition method named as average gait differential image (AGDI is proposed in this paper. The AGDI is generated by the accumulation of the silhouettes difference between adjacent frames. The advantage of this method lies in that as a feature image it can preserve both the kinetic and static information of walking. Comparing to gait energy image (GEI, AGDI is more fit to representation the variation of silhouettes during walking. Two-dimensional principal component analysis (2DPCA is used to extract features from the AGDI. Experiments on CASIA dataset show that AGDI has better identification and verification performance than GEI. Comparing to PCA, 2DPCA is a more efficient and less memory storage consumption feature extraction method in gait based recognition.

  9. Adaptive Self-Occlusion Behavior Recognition Based on pLSA

    Directory of Open Access Journals (Sweden)

    Hong-bin Tu

    2013-01-01

    Full Text Available Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA. Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

  10. A novel polar-based human face recognition computational model

    Directory of Open Access Journals (Sweden)

    Y. Zana

    2009-07-01

    Full Text Available Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.

  11. Advances in biometrics for secure human authentication and recognition

    CERN Document Server

    Kisku, Dakshina Ranjan; Sing, Jamuna Kanta

    2013-01-01

    GENERAL BIOMETRICSSecurity and Reliability Assessment for Biometric Systems; Gayatri MirajkarReview of Human Recognition Based on Retinal Images; Amin DehghaniADVANCED TOPICS IN BIOMETRICSVisual Speech as Behavioral Biometric; Preety Singh, Vijay Laxmi, and Manoj Singh GaurHuman Gait Signature for Biometric Authentication; Vijay JohnHand-Based Biometric for Personal Identification Using Correlation Filter Classifier; Mohammed Saigaa , Abdallah Meraoumia , Salim Chitroub, and Ahmed BouridaneOn Deciding the Dynamic Periocular Boundary for Human Recognition; Sambit Bakshi , Pankaj Kumar Sa, and Banshidhar MajhiRetention of Electrocardiogram Features Insignificantly Devalorized as an Effect of Watermarking for a Multimodal Biometric Authentication System; Nilanjan Dey, Bijurika Nandi, Poulami Das, Achintya Das, and Sheli Sinha ChaudhuriFacial Feature Point Extraction for Object Identification Using Discrete Contourlet Transform and Principal Component Analysis; N. G. Chitaliya and A. I. TrivediCASE STUDIES AND LA...

  12. The Improvement of Behavior Recognition Accuracy of Micro Inertial Accelerometer by Secondary Recognition Algorithm

    Directory of Open Access Journals (Sweden)

    Yu Liu

    2014-05-01

    Full Text Available Behaviors of “still”, “walking”, “running”, “jumping”, “upstairs” and “downstairs” can be recognized by micro inertial accelerometer of low cost. By using the features as inputs to the well-trained BP artificial neural network which is selected as classifier, those behaviors can be recognized. But the experimental results show that the recognition accuracy is not satisfactory. This paper presents secondary recognition algorithm and combine it with BP artificial neural network to improving the recognition accuracy. The Algorithm is verified by the Android mobile platform, and the recognition accuracy can be improved more than 8 %. Through extensive testing statistic analysis, the recognition accuracy can reach 95 % through BP artificial neural network and the secondary recognition, which is a reasonable good result from practical point of view.

  13. Bridging Humanism and Behaviorism.

    Science.gov (United States)

    Chu, Lily

    1980-01-01

    Humanistic behaviorism may provide the necessary bridge between behaviorism and humanism. Perhaps the most humanistic approach to teaching is to learn how certain changes will help students and how these changes can be accomplished. (Author/MLF)

  14. Humanism vs. Behaviorism

    Science.gov (United States)

    Hunter, Madeline

    1977-01-01

    Author argues that humanism and behaviorism are not necessarily exclusive of one another, and that principles of behaviorism, when thoughtfully applied, can lead to the achievement of humanistic goals. (RW)

  15. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  16. Object recognition in images by human vision and computer vision

    NARCIS (Netherlands)

    Chen, Q.; Dijkstra, J.; Vries, de B.

    2010-01-01

    Object recognition plays a major role in human behaviour research in the built environment. Computer based object recognition techniques using images as input are challenging, but not an adequate representation of human vision. This paper reports on the differences in object shape recognition

  17. Gender Recognition from Unconstrained and Articulated Human Body

    OpenAIRE

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, ho...

  18. Problems of Face Recognition in Patients with Behavioral Variant Frontotemporal Dementia.

    Science.gov (United States)

    Chandra, Sadanandavalli Retnaswami; Patwardhan, Ketaki; Pai, Anupama Ramakanth

    2017-01-01

    Faces are very special as they are most essential for social cognition in humans. It is partly understood that face processing in its abstractness involves several extra striate areas. One of the most important causes for caregiver suffering in patients with anterior dementia is lack of empathy. This apart from being a behavioral disorder could be also due to failure to categorize the emotions of the people around them. Inlusion criteria: DSM IV for Bv FTD Tested for prosopagnosia - familiar faces, famous face, smiling face, crying face and reflected face using a simple picture card (figure 1). Advanced illness and mixed causes. 46 patients (15 females, 31 males) 24 had defective face recognition. (mean age 51.5),10/15 females (70%) and 14/31males(47. Familiar face recognition defect was found in 6/10 females and 6/14 males. Total- 40%(6/15) females and 19.35%(6/31)males with FTD had familiar face recognition. Famous Face: 9/10 females and 7/14 males. Total- 60% (9/15) females with FTD had famous face recognition defect as against 22.6%(7/31) males with FTD Smiling face defects in 8/10 female and no males. Total- 53.33% (8/15) females. Crying face recognition defect in 3/10 female and 2 /14 males. Total- 20%(3/15) females and 6.5%(2/31) males. Reflected face recognition defect in 4 females. Famous face recognition and positive emotion recognition defect in 80%, only 20% comprehend positive emotions, Face recognition defects are found in only 45% of males and more common in females. Face recognition is more affected in females with FTD There is differential involvement of different aspects of the face recognition could be one of the important factor underlying decline in the emotional and social behavior of these patients. Understanding these pathological processes will give more insight regarding patient behavior.

  19. Physical Human Activity Recognition Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Ferhat Attal

    2015-12-01

    Full Text Available This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle. Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN, Support Vector Machines (SVM, Gaussian Mixture Models (GMM, and Random Forest (RF as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM and Hidden Markov Model (HMM, are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

  20. Physical Human Activity Recognition Using Wearable Sensors.

    Science.gov (United States)

    Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

    2015-12-11

    This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

  1. Pattern Recognition by Humans and Machines

    International Nuclear Information System (INIS)

    Versino, C.; )

    2015-01-01

    Data visualization is centred on new ways of processing and displaying large data sets to support pattern recognition by humans rather than by machines. The motivation for approaches based on data visualization is to encourage data exploration and curiosity by analysts. They should help formulating the right question more than addressing specific predefined issues or expectations. Translated into IAEA's terms, they should help verify the completeness of information declared to the IAEA more than their correctness. Data visualization contrasts with traditional information retrieval where one needs first to formulate a query in order to get to a narrow slice of data. Using traditional information retrieval, no one knows what is missed out. The system may fail to recall relevant data due to the way the query was formulated, or the query itself may not be the most relevant one to be asked in the first place. Examples of data visualizations relevant to safeguards will be illustrated, including new approaches for the review of surveillance images and for trade analysis. Common to these examples is the attempt to enlarge the view of the analyst on a universe of data, where context or detailed data is presented on-demand and by levels of abstraction. The paper will make reference to ongoing research and to enabling information technologies. (author)

  2. Simulation Analysis on Driving Behavior during Traffic Sign Recognition

    Directory of Open Access Journals (Sweden)

    Lishan Sun

    2011-05-01

    Full Text Available The traffic signs transfer trip information to drivers through vectors like words, graphs and numbers. Traffic sign with excessive information often makes the drivers have no time to read and understand, leading to risky driving. It is still a problem of how to clarify the relationship between traffic sign recognition and risky driving behavior. This paper presents a study that is reflective of such an effort. Twenty volunteers participated in the dynamic visual recognition experiment in driving simulator, and the data of several key indicators are obtained, including visual cognition time, vehicle acceleration and the offset distance from middle lane, etc. Correlations between each indicator above are discussed in terms of risky driving. Research findings directly show that drivers' behavior changes a lot during their traffic sign recognition.

  3. Consistency between recognition and behavior creates consciousness

    Directory of Open Access Journals (Sweden)

    Keita Inaba

    2004-08-01

    Full Text Available What is consciousness? Is it possible to create consciousness mechanically? Various studies have been performed in the fields of psychology and cerebral science to answer these questions. As of yet, however, no researchers have proposed a model capable of explaining the mind-body problem described by Descartes or replicating a consciousness as advanced as that of human beings. Ancient people believed that the consciousness resided in a Homunculus, a human in miniature who lived in the brain. It is no mystery that the ancients came up with such an idea; for consciousness has always been veiled in mystery, beyond the reach of our explorative powers. We can assert, however, that consciousness does not "live" in us, but "exists" in us. Insofar as the processes occurring inside the human brain are a product of the physical activity of the neurons that reside there, we believe that it should be possible to define consciousness systematically.

  4. Television and Human Behavior.

    Science.gov (United States)

    Comstock, George; And Others

    To compile a comprehensive review of English language scientific literature regarding the effects of television on human behavior, the authors of this book evaluated more than 2,500 books, articles, reports, and other documents. Rather than taking a traditional approach, the authors followed a new model for the retrieval and synthesis of…

  5. Window Size Impact in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Oresti Banos

    2014-04-01

    Full Text Available Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1–2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities.

  6. Human-assisted sound event recognition for home service robots.

    Science.gov (United States)

    Do, Ha Manh; Sheng, Weihua; Liu, Meiqin

    This paper proposes and implements an open framework of active auditory learning for a home service robot to serve the elderly living alone at home. The framework was developed to realize the various auditory perception capabilities while enabling a remote human operator to involve in the sound event recognition process for elderly care. The home service robot is able to estimate the sound source position and collaborate with the human operator in sound event recognition while protecting the privacy of the elderly. Our experimental results validated the proposed framework and evaluated auditory perception capabilities and human-robot collaboration in sound event recognition.

  7. Static human face recognition using artificial neural networks

    International Nuclear Information System (INIS)

    Qamar, R.; Shah, S.H.; Javed-ur-Rehman

    2003-01-01

    This paper presents a novel method of human face recognition using digital computers. A digital PC camera is used to take the BMP images of the human faces. An artificial neural network using Back Propagation Algorithm is developed as a recognition engine. The BMP images of the faces serve as the input patterns for this engine. A software 'Face Recognition' has been developed to recognize the human faces for which it is trained. Once the neural network is trained for patterns of the faces, the software is able to detect and recognize them with success rate of about 97%. (author)

  8. Human motion sensing and recognition a fuzzy qualitative approach

    CERN Document Server

    Liu, Honghai; Ji, Xiaofei; Chan, Chee Seng; Khoury, Mehdi

    2017-01-01

    This book introduces readers to the latest exciting advances in human motion sensing and recognition, from the theoretical development of fuzzy approaches to their applications. The topics covered include human motion recognition in 2D and 3D, hand motion analysis with contact sensors, and vision-based view-invariant motion recognition, especially from the perspective of Fuzzy Qualitative techniques. With the rapid development of technologies in microelectronics, computers, networks, and robotics over the last decade, increasing attention has been focused on human motion sensing and recognition in many emerging and active disciplines where human motions need to be automatically tracked, analyzed or understood, such as smart surveillance, intelligent human-computer interaction, robot motion learning, and interactive gaming. Current challenges mainly stem from the dynamic environment, data multi-modality, uncertain sensory information, and real-time issues. These techniques are shown to effectively address the ...

  9. A Hierarchical Representation for Human Activity Recognition with Noisy Labels

    NARCIS (Netherlands)

    Hu, N.; Englebienne, G.; Lou, Z.; Kröse, B.

    2015-01-01

    Human activity recognition is an essential task for robots to effectively and efficiently interact with the end users. Many machine learning approaches for activity recognition systems have been proposed recently. Most of these methods are built upon a strong assumption that the labels in the

  10. From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

    Science.gov (United States)

    Yildiz, Izzet B; von Kriegstein, Katharina; Kiebel, Stefan J

    2013-01-01

    Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

  11. From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

    Directory of Open Access Journals (Sweden)

    Izzet B Yildiz

    Full Text Available Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

  12. Gender Recognition from Unconstrained and Articulated Human Body

    Science.gov (United States)

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

  13. Gender recognition from unconstrained and articulated human body.

    Science.gov (United States)

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

  14. Recognition and Synthesis of Human Movements by Parametric HMMs

    DEFF Research Database (Denmark)

    Herzog, Dennis; Krüger, Volker

    2009-01-01

    The representation of human movements for recognition and synthesis is important in many application fields such as: surveillance, human-computer interaction, motion capture, and humanoid robots. Hidden Markov models (HMMs) are a common statistical framework in this context, since...... on the recognition and synthesis of human arm movements. Furthermore, we will show in various experiments the use of PHMMs for the control of a humanoid robot by synthesizing movements for relocating objects at arbitrary positions. In vision-based interaction experiments, PHMM are used for the recognition...... of pointing movements, where the recognized parameterization conveys to a robot the important information which object to relocate and where to put it. Finally, we evaluate the accuracy of recognition and synthesis for pointing and grasping arm movements and discuss that the precision of the synthesis...

  15. Episodic Reasoning for Vision-Based Human Action Recognition

    Directory of Open Access Journals (Sweden)

    Maria J. Santofimia

    2014-01-01

    Full Text Available Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning.

  16. Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition.

    Science.gov (United States)

    Scherf, K Suzanne; Elbich, Daniel B; Motta-Mena, Natalie V

    2017-01-01

    There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women.

  17. Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition

    Science.gov (United States)

    2017-01-01

    Abstract There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women. PMID:28497111

  18. [Terrorism and human behavior].

    Science.gov (United States)

    Leistedt, S J

    2018-04-01

    Theories of religion are essential for understanding current trends in terrorist activities. The aim of this work is to clarify religion's role in facilitating terror and outline in parallel with recent theoretical developments on terrorism and human behaviour. Several databases were used such as PubCentral, Scopus, Medline and Science Direct. The search terms "terrorism", "social psychology", "religion", "evolution", and "cognition" were used to identify relevant studies in the databases. This work examines, in a multidimensional way, how terrorists employ these features of religion to achieve their goals. In the same way, it describes how terrorists use rituals to conditionally associate emotions with sanctified symbols that are emotionally evocative and motivationally powerful, fostering group solidarity, trust, and cooperation. Religious beliefs, including promised rewards in the afterlife, further serve to facilitate cooperation by altering the perceived payoffs of costly actions, including suicide bombing. The adolescent pattern of brain development is unique, and young adulthood presents an ideal developmental stage to attract recruits and enlist them in high-risk behaviors. This work offers insights, based on this translational analysis, concerning the links between religion, terrorism and human behavior. Copyright © 2017 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

  19. Gender Recognition from Unconstrained and Articulated Human Body

    Directory of Open Access Journals (Sweden)

    Qin Wu

    2014-01-01

    human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

  20. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  1. Narrowing the gap between automatic and human word recognition

    NARCIS (Netherlands)

    Scharenborg, O.E.

    2005-01-01

    In everyday life, speech is all around us, on the radio, television, and in human-human interaction. We are continually confronted with novel utterances, and usually we have no problem recognising and understanding them. Several research fields investigate the speech recognition process. This thesis

  2. Pattern Recognition as a Human Centered non-Euclidean Problem

    NARCIS (Netherlands)

    Duin, R.P.W.

    2010-01-01

    Regularities in the world are human defined. Patterns in the observed phenomena are there because we define and recognize them as such. Automatic pattern recognition tries to bridge the gap between human judgment and measurements made by artificial sensors. This is done in two steps: representation

  3. Human Activity Recognition from Body Sensor Data using Deep Learning.

    Science.gov (United States)

    Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

    2018-04-16

    In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

  4. Towards discrete wavelet transform-based human activity recognition

    Science.gov (United States)

    Khare, Manish; Jeon, Moongu

    2017-06-01

    Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

  5. Genes, Environment, and Human Behavior.

    Science.gov (United States)

    Bloom, Mark V.; Cutter, Mary Ann; Davidson, Ronald; Dougherty, Michael J.; Drexler, Edward; Gelernter, Joel; McCullough, Laurence B.; McInerney, Joseph D.; Murray, Jeffrey C.; Vogler, George P.; Zola, John

    This curriculum module explores genes, environment, and human behavior. This book provides materials to teach about the nature and methods of studying human behavior, raise some of the ethical and public policy dilemmas emerging from the Human Genome Project, and provide professional development for teachers. An extensive Teacher Background…

  6. Human action recognition based on estimated weak poses

    Science.gov (United States)

    Gong, Wenjuan; Gonzàlez, Jordi; Roca, Francesc Xavier

    2012-12-01

    We present a novel method for human action recognition (HAR) based on estimated poses from image sequences. We use 3D human pose data as additional information and propose a compact human pose representation, called a weak pose, in a low-dimensional space while still keeping the most discriminative information for a given pose. With predicted poses from image features, we map the problem from image feature space to pose space, where a Bag of Poses (BOP) model is learned for the final goal of HAR. The BOP model is a modified version of the classical bag of words pipeline by building the vocabulary based on the most representative weak poses for a given action. Compared with the standard k-means clustering, our vocabulary selection criteria is proven to be more efficient and robust against the inherent challenges of action recognition. Moreover, since for action recognition the ordering of the poses is discriminative, the BOP model incorporates temporal information: in essence, groups of consecutive poses are considered together when computing the vocabulary and assignment. We tested our method on two well-known datasets: HumanEva and IXMAS, to demonstrate that weak poses aid to improve action recognition accuracies. The proposed method is scene-independent and is comparable with the state-of-art method.

  7. Human Activity Recognition Using Hierarchically-Mined Feature Constellations

    NARCIS (Netherlands)

    Oikonomopoulos, A.; Pantic, Maja

    In this paper we address the problem of human activity modelling and recognition by means of a hierarchical representation of mined dense spatiotemporal features. At each level of the hierarchy, the proposed method selects feature constellations that are increasingly discriminative and

  8. A survey on vision-based human action recognition

    NARCIS (Netherlands)

    Poppe, Ronald Walter

    Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human–computer interaction. The task is challenging due to variations in motion

  9. Surface imprinted beads for the recognition of human serum albumin.

    Science.gov (United States)

    Bonini, Francesca; Piletsky, Sergey; Turner, Anthony P F; Speghini, Adolfo; Bossi, Alessandra

    2007-04-15

    The synthesis of poly-aminophenylboronic acid (ABPA) imprinted beads for the recognition of the protein human serum albumin (HSA) is reported. In order to create homogeneous recognition sites, covalent immobilisation of the template HSA was exploited. The resulting imprinted beads were selective for HSA. The indirect imprinting factor (IF) calculated from supernatant was 1.6 and the direct IF, evaluated from the protein recovered from the beads, was 1.9. The binding capacity was 1.4 mg/g, which is comparable to commercially available affinity materials. The specificity of the HSA recognition was evaluated with competitive experiments, indicating a molar ratio 4.5/1 of competitor was necessary to displace half of the bound HSA. The recognition and binding of the imprinted beads was also tested with a complex sample, human serum and targeted removal of HSA without a loss of the other protein components was demonstrated. The easy preparation protocol of derivatised beads and a good protein recognition properties make the approach an attractive solution to analytical and bio-analytical problems in the field of biotechnology.

  10. Learning and Recognition of a Non-conscious Sequence of Events in Human Primary Visual Cortex.

    Science.gov (United States)

    Rosenthal, Clive R; Andrews, Samantha K; Antoniades, Chrystalina A; Kennard, Christopher; Soto, David

    2016-03-21

    Human primary visual cortex (V1) has long been associated with learning simple low-level visual discriminations [1] and is classically considered outside of neural systems that support high-level cognitive behavior in contexts that differ from the original conditions of learning, such as recognition memory [2, 3]. Here, we used a novel fMRI-based dichoptic masking protocol-designed to induce activity in V1, without modulation from visual awareness-to test whether human V1 is implicated in human observers rapidly learning and then later (15-20 min) recognizing a non-conscious and complex (second-order) visuospatial sequence. Learning was associated with a change in V1 activity, as part of a temporo-occipital and basal ganglia network, which is at variance with the cortico-cerebellar network identified in prior studies of "implicit" sequence learning that involved motor responses and visible stimuli (e.g., [4]). Recognition memory was associated with V1 activity, as part of a temporo-occipital network involving the hippocampus, under conditions that were not imputable to mechanisms associated with conscious retrieval. Notably, the V1 responses during learning and recognition separately predicted non-conscious recognition memory, and functional coupling between V1 and the hippocampus was enhanced for old retrieval cues. The results provide a basis for novel hypotheses about the signals that can drive recognition memory, because these data (1) identify human V1 with a memory network that can code complex associative serial visuospatial information and support later non-conscious recognition memory-guided behavior (cf. [5]) and (2) align with mouse models of experience-dependent V1 plasticity in learning and memory [6]. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Human Activity Recognition in AAL Environments Using Random Projections

    Directory of Open Access Journals (Sweden)

    Robertas Damaševičius

    2016-01-01

    Full Text Available Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject’s body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL, for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD data are presented.

  12. Human Activity Recognition in AAL Environments Using Random Projections.

    Science.gov (United States)

    Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

    2016-01-01

    Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

  13. Real-time Human Activity Recognition

    Science.gov (United States)

    Albukhary, N.; Mustafah, Y. M.

    2017-11-01

    The traditional Closed-circuit Television (CCTV) system requires human to monitor the CCTV for 24/7 which is inefficient and costly. Therefore, there’s a need for a system which can recognize human activity effectively in real-time. This paper concentrates on recognizing simple activity such as walking, running, sitting, standing and landing by using image processing techniques. Firstly, object detection is done by using background subtraction to detect moving object. Then, object tracking and object classification are constructed so that different person can be differentiated by using feature detection. Geometrical attributes of tracked object, which are centroid and aspect ratio of identified tracked are manipulated so that simple activity can be detected.

  14. A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

    2016-10-25

    Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

  15. MoCog1: A computer simulation of recognition-primed human decision making

    Science.gov (United States)

    Gevarter, William B.

    1991-01-01

    The results of the first stage of a research effort to develop a 'sophisticated' computer model of human cognitive behavior are described. Most human decision making is an experience-based, relatively straight-forward, largely automatic response to internal goals and drives, utilizing cues and opportunities perceived from the current environment. The development of the architecture and computer program (MoCog1) associated with such 'recognition-primed' decision making is discussed. The resultant computer program was successfully utilized as a vehicle to simulate earlier findings that relate how an individual's implicit theories orient the individual toward particular goals, with resultant cognitions, affects, and behavior in response to their environment.

  16. Adaboost-based algorithm for human action recognition

    KAUST Repository

    Zerrouki, Nabil

    2017-11-28

    This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.

  17. Adaboost-based algorithm for human action recognition

    KAUST Repository

    Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

    2017-01-01

    This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.

  18. Human and automatic speaker recognition over telecommunication channels

    CERN Document Server

    Fernández Gallardo, Laura

    2016-01-01

    This work addresses the evaluation of the human and the automatic speaker recognition performances under different channel distortions caused by bandwidth limitation, codecs, and electro-acoustic user interfaces, among other impairments. Its main contribution is the demonstration of the benefits of communication channels of extended bandwidth, together with an insight into how speaker-specific characteristics of speech are preserved through different transmissions. It provides sufficient motivation for considering speaker recognition as a criterion for the migration from narrowband to enhanced bandwidths, such as wideband and super-wideband.

  19. Effect of General Anesthesia in Infancy on Long-Term Recognition Memory in Humans and Rats

    Science.gov (United States)

    Stratmann, Greg; Lee, Joshua; Sall, Jeffrey W; Lee, Bradley H; Alvi, Rehan S; Shih, Jennifer; Rowe, Allison M; Ramage, Tatiana M; Chang, Flora L; Alexander, Terri G; Lempert, David K; Lin, Nan; Siu, Kasey H; Elphick, Sophie A; Wong, Alice; Schnair, Caitlin I; Vu, Alexander F; Chan, John T; Zai, Huizhen; Wong, Michelle K; Anthony, Amanda M; Barbour, Kyle C; Ben-Tzur, Dana; Kazarian, Natalie E; Lee, Joyce YY; Shen, Jay R; Liu, Eric; Behniwal, Gurbir S; Lammers, Cathy R; Quinones, Zoel; Aggarwal, Anuj; Cedars, Elizabeth; Yonelinas, Andrew P; Ghetti, Simona

    2014-01-01

    Anesthesia in infancy impairs performance in recognition memory tasks in mammalian animals, but it is unknown if this occurs in humans. Successful recognition can be based on stimulus familiarity or recollection of event details. Several brain structures involved in recollection are affected by anesthesia-induced neurodegeneration in animals. Therefore, we hypothesized that anesthesia in infancy impairs recollection later in life in humans and rats. Twenty eight children ages 6–11 who had undergone a procedure requiring general anesthesia before age 1 were compared with 28 age- and gender-matched children who had not undergone anesthesia. Recollection and familiarity were assessed in an object recognition memory test using receiver operator characteristic analysis. In addition, IQ and Child Behavior Checklist scores were assessed. In parallel, thirty three 7-day-old rats were randomized to receive anesthesia or sham anesthesia. Over 10 months, recollection and familiarity were assessed using an odor recognition test. We found that anesthetized children had significantly lower recollection scores and were impaired at recollecting associative information compared with controls. Familiarity, IQ, and Child Behavior Checklist scores were not different between groups. In rats, anesthetized subjects had significantly lower recollection scores than controls while familiarity was unaffected. Rats that had undergone tissue injury during anesthesia had similar recollection indices as rats that had been anesthetized without tissue injury. These findings suggest that general anesthesia in infancy impairs recollection later in life in humans and rats. In rats, this effect is independent of underlying disease or tissue injury. PMID:24910347

  20. Integration of human behavior expectations in training: human behavior simulator

    International Nuclear Information System (INIS)

    Obeso Torices, E.

    2012-01-01

    The analysis of operating experience in nuclear Sta Maria de Garona point to fundamental human factor. After evaluation of the Peer Review, reinforcing behavior expectations was identified as improvement area. The human behavior simulator aims at minimizing human error. Making teamwork practices ensures that the equipment itself reinforces their behavior and performance in the work of the Central. The scope of practice to perform on the simulator includes all phases of execution. The team should analyze the best way to run, the impact of it on the ground and interaction with other sections, being the simulator training environment the situation closer to reality.

  1. A Novel Energy-Efficient Approach for Human Activity Recognition.

    Science.gov (United States)

    Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

    2017-09-08

    In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

  2. Behavioral Biometrics in Assisted Living: A Methodology for Emotion Recognition

    Directory of Open Access Journals (Sweden)

    S. Xefteris

    2016-08-01

    Full Text Available Behavioral biometrics aim at providing algorithms for the automatic recognition of individual behavioral traits, stemming from a person’s actions, attitude, expressions and conduct. In the field of ambient assisted living, behavioral biometrics find an important niche. Individuals suffering from the early stages of neurodegenerative diseases (MCI, Alzheimer’s, dementia need supervision in their daily activities. In this context, an unobtrusive system to monitor subjects and alert formal and informal carers providing information on both physical and emotional status is of great importance and positively affects multiple stakeholders. The primary aim of this paper is to describe a methodology for recognizing the emotional status of a subject using facial expressions and to identify its uses, in conjunction with pre-existing risk-assessment methodologies, for its integration into the context of a smart monitoring system for subjects suffering from neurodegenerative diseases. Paul Ekman’s research provided the background on the universality of facial expressions as indicators of underlying emotions. The methodology then makes use of computational geometry, image processing and graph theory algorithms for the detection of regions of interest and then a neural network is used for the final classification. Findings are coupled with previous published work for risk assessment and alert generation in the context of an ambient assisted living environment based on Service oriented architecture principles, aimed at remote web-based estimation of the cognitive and physical status of MCI and dementia patients.

  3. Deep Recurrent Neural Networks for Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Abdulmajid Murad

    2017-11-01

    Full Text Available Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM and k-nearest neighbors (KNN. Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs and CNNs.

  4. Deep Recurrent Neural Networks for Human Activity Recognition.

    Science.gov (United States)

    Murad, Abdulmajid; Pyun, Jae-Young

    2017-11-06

    Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

  5. Chronic prenatal caffeine exposure impairs novel object recognition and radial arm maze behaviors in adult rats.

    Science.gov (United States)

    Soellner, Deborah E; Grandys, Theresa; Nuñez, Joseph L

    2009-12-14

    In this report, we demonstrate that chronic prenatal exposure to a moderate dose of caffeine disrupts novel object recognition and radial arm maze behaviors in adult male and female rats. Pregnant dams were administered either tap water or 75 mg/L caffeinated tap water throughout gestation. Oral self-administration in the drinking water led to an approximate maternal intake of 10mg/kg/day, equivalent to 2-3 cups of coffee/day in humans based on a metabolic body weight conversion. In adulthood, the offspring underwent testing on novel object recognition, radial arm maze, and Morris water maze tasks. Prenatal caffeine exposure was found to impair 24-h memory retention in the novel object recognition task and impair both working and reference memory in the radial arm maze. However, prenatal caffeine exposure did not alter Morris water maze performance in either a simple water maze procedure or in an advanced water maze procedure that included reversal and working memory paradigms. These findings demonstrate that chronic oral intake of caffeine throughout gestation can alter adult cognitive behaviors in rats.

  6. Irrational Human Behaviors

    Directory of Open Access Journals (Sweden)

    Orhan Şener

    2015-10-01

    Full Text Available Neo Classical economists used to posit that, since consumers are rational, they make decisions to maximize their pleasure (utility. Opposing to Neo Classical understanding, Behavioral Economists argue that, consumers are infect not rational, but prone to all sort of biases and habits that pull them being rational. For instance, there are too many irrational choices made by the Turkish consumers like; expensive wedding parties given by low income families; although riding bicycle is healthy and cheap, but people buy expensive cars; it is cheaper staying at a hotel or a timeshare, however people buy expensive summer houses, where they stayed only few weeks a year. These type of irrational behaviors adversely affect the decisions on savings, investments and economic growth. On the consumers irrationality, Tversky and Daniel Kahneman, winner of the 2002 Nobel Prize in Economics, wrote Prospect Theory. They developed a cognitive psychological model to explain divergences from neoclassical economics. They claimed that people take decisions under psychological, social, emotional and economic factors that affect market prices and resource allocation. In order to explain the irrational behaviors of Turkish consumers, I utilized some concepts such as conspicuous consumption (or keeping up with Johns, Veblen Effect, Bandwagon Effect, bounded rationality, 20 to 80 Law and ethical considerations developed by Behavioral Economists and Heterodox Economics. Thus, I came to conclusion that why the free market economic understanding fails in Turkey by giving some examples and economic reasons stated in the last section of this paper.

  7. Interactive human behavior analysis in open or public spaces

    NARCIS (Netherlands)

    Hung, H.; Odobez, J.-M.; Gavrila, D.; Keyson, D.V.; Maher, M.L.; Streitz, N.; Cheok, A.; Augusto, J.C.; Wichert, R.; Englebienne, G.; Aghajan, H.; Kröse, B.J.A.

    2011-01-01

    In the past years, efforts in surveillance and open space analysis have focused on traditional computer vision problems like scene modeling or object detection and tracking. Research on human behavior recognition have tended to work on predefined simple activities such as running, jumping or left

  8. Characterizing cognitive aging of recognition memory and related processes in animal models and in humans

    Directory of Open Access Journals (Sweden)

    Carol A Barnes

    2012-09-01

    Full Text Available Analyses of complex behaviors across the lifespan of animals can reveal the brain regions that are impacted by the normal aging process, thereby, elucidating potential therapeutic targets. Recent data from rats, monkeys and humans converge, all indicating that recognition memory and complex visual perception are impaired in advanced age. These cognitive processes are also disrupted in animals with lesions of the perirhinal cortex, indicating that the the functional integrity of this structure is disrupted in old age. This current review summarizes these data, and highlights current methodologies for assessing perirhinal cortex-dependent behaviors across the lifespan.

  9. The Consequences of Human Behavior

    Directory of Open Access Journals (Sweden)

    Derek Hodgson

    2012-12-01

    Full Text Available Human behavior is founded on a complex interaction of influences that derive from sources both extraneous and intrinsic to the brain. It is the ways these various influences worked together in the past to fashion modern human cognition that can help elucidate the probable course of future human endeavor. A particular concern of this chapter is the way cognition has been shaped and continues to depend on prevailing environmental and ecological conditions. Whether the human predicament can be regarded simply as another response to such conditions similar to that of other organisms or something special will also be addressed. More specifically, it will be shown that, although the highly artificial niche in which most humans now live has had profound effects on ways of thinking, constraints deriving from a shared evolutionary heritage continue to have substantial effects on behavior. The way these exigencies interact will be explored in order to understand the implications for the future wellbeing of humanity.

  10. Robust Indoor Human Activity Recognition Using Wireless Signals.

    Science.gov (United States)

    Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

    2015-07-15

    Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

  11. Robust Indoor Human Activity Recognition Using Wireless Signals

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2015-07-01

    Full Text Available Wireless signals–based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP and access points (AP. First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions’ CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

  12. Human action recognition using trajectory-based representation

    Directory of Open Access Journals (Sweden)

    Haiam A. Abdul-Azim

    2015-07-01

    Full Text Available Recognizing human actions in video sequences has been a challenging problem in the last few years due to its real-world applications. A lot of action representation approaches have been proposed to improve the action recognition performance. Despite the popularity of local features-based approaches together with “Bag-of-Words” model for action representation, it fails to capture adequate spatial or temporal relationships. In an attempt to overcome this problem, a trajectory-based local representation approaches have been proposed to capture the temporal information. This paper introduces an improvement of trajectory-based human action recognition approaches to capture discriminative temporal relationships. In our approach, we extract trajectories by tracking the detected spatio-temporal interest points named “cuboid features” with matching its SIFT descriptors over the consecutive frames. We, also, propose a linking and exploring method to obtain efficient trajectories for motion representation in realistic conditions. Then the volumes around the trajectories’ points are described to represent human actions based on the Bag-of-Words (BOW model. Finally, a support vector machine is used to classify human actions. The effectiveness of the proposed approach was evaluated on three popular datasets (KTH, Weizmann and UCF sports. Experimental results showed that the proposed approach yields considerable performance improvement over the state-of-the-art approaches.

  13. Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

    Directory of Open Access Journals (Sweden)

    Gaojing Wang

    2018-06-01

    Full Text Available Human activity recognition (HAR is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.

  14. Common polymorphism in the oxytocin receptor gene (OXTR) is associated with human social recognition skills.

    Science.gov (United States)

    Skuse, David H; Lori, Adriana; Cubells, Joseph F; Lee, Irene; Conneely, Karen N; Puura, Kaija; Lehtimäki, Terho; Binder, Elisabeth B; Young, Larry J

    2014-02-04

    The neuropeptides oxytocin and vasopressin are evolutionarily conserved regulators of social perception and behavior. Evidence is building that they are critically involved in the development of social recognition skills within rodent species, primates, and humans. We investigated whether common polymorphisms in the genes encoding the oxytocin and vasopressin 1a receptors influence social memory for faces. Our sample comprised 198 families, from the United Kingdom and Finland, in whom a single child had been diagnosed with high-functioning autism. Previous research has shown that impaired social perception, characteristic of autism, extends to the first-degree relatives of autistic individuals, implying heritable risk. Assessments of face recognition memory, discrimination of facial emotions, and direction of gaze detection were standardized for age (7-60 y) and sex. A common SNP in the oxytocin receptor (rs237887) was strongly associated with recognition memory in combined probands, parents, and siblings after correction for multiple comparisons. Homozygotes for the ancestral A allele had impairments in the range -0.6 to -1.15 SD scores, irrespective of their diagnostic status. Our findings imply that a critical role for the oxytocin system in social recognition has been conserved across perceptual boundaries through evolution, from olfaction in rodents to visual memory in humans.

  15. HUMAN RESOURCE MANAGEMENT IN TERMS OF BEHAVIORAL ECONOMICS

    Directory of Open Access Journals (Sweden)

    Ewa Mazanowska

    2015-01-01

    Full Text Available Behaviourists believe human capital is seen as the potential in people. They believe that the human resource in the organization are intangible assets embodied in the employees, not the people themselves. Behavioral economics emphasizes that people aren’t owned by the company, only their abilities and skills made available to the employer on the basis of certain legal relations which holds it to manage these assets in a rational way. Recognition of behavioral economics also highlights the aspects of development and human capital perspective, which appear in the may resource Staff in the future. These may be limited to: raise, awareness of capacity, internal aspirations, motives. Human capital management is nothing but a recognition of the relevant characteristics of the potential held within the company Staff and correct its use. As a consequence, it can bring tangible benefits to the organization.

  16. Recognition of human face images by the free flying wasp Vespula vulgaris

    Directory of Open Access Journals (Sweden)

    Aurore Avarguès-Weber

    2017-08-01

    Full Text Available The capacity to recognize perceptually similar complex visual stimuli such as human faces has classically been thought to require a large primate, and/or mammalian brain with neurobiological adaptations. However, recent work suggests that the relatively small brain of a paper wasp, Polistes fuscatus, possesses specialized face processing capabilities. In parallel, the honeybee, Apis mellifera, has been shown to be able to rely on configural learning for extensive visual learning, thus converging with primate visual processing. Therefore, the honeybee may be able to recognize human faces, and show sophisticated learning performance due to its foraging lifestyle involving visiting and memorizing many flowers. We investigated the visual capacities of the widespread invasive wasp Vespula vulgaris, which is unlikely to have any specialization for face processing. Freely flying individual wasps were trained in an appetitive-aversive differential conditioning procedure to discriminate between perceptually similar human face images from a standard face recognition test. The wasps could then recognize the target face from novel dissimilar or similar human faces, but showed a significant drop in performance when the stimuli were rotated by 180°, thus paralleling results acquired on a similar protocol with honeybees. This result confirms that a general visual system can likely solve complex recognition tasks, the first stage to evolve a visual expertise system to face recognition, even in the absence of neurobiological or behavioral specialization.

  17. Human and animal sounds influence recognition of body language.

    Science.gov (United States)

    Van den Stock, Jan; Grèzes, Julie; de Gelder, Beatrice

    2008-11-25

    In naturalistic settings emotional events have multiple correlates and are simultaneously perceived by several sensory systems. Recent studies have shown that recognition of facial expressions is biased towards the emotion expressed by a simultaneously presented emotional expression in the voice even if attention is directed to the face only. So far, no study examined whether this phenomenon also applies to whole body expressions, although there is no obvious reason why this crossmodal influence would be specific for faces. Here we investigated whether perception of emotions expressed in whole body movements is influenced by affective information provided by human and by animal vocalizations. Participants were instructed to attend to the action displayed by the body and to categorize the expressed emotion. The results indicate that recognition of body language is biased towards the emotion expressed by the simultaneously presented auditory information, whether it consist of human or of animal sounds. Our results show that a crossmodal influence from auditory to visual emotional information obtains for whole body video images with the facial expression blanked and includes human as well as animal sounds.

  18. Human-inspired sound environment recognition system for assistive vehicles

    Science.gov (United States)

    González Vidal, Eduardo; Fredes Zarricueta, Ernesto; Auat Cheein, Fernando

    2015-02-01

    Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance

  19. Human phoneme recognition depending on speech-intrinsic variability.

    Science.gov (United States)

    Meyer, Bernd T; Jürgens, Tim; Wesker, Thorsten; Brand, Thomas; Kollmeier, Birger

    2010-11-01

    The influence of different sources of speech-intrinsic variation (speaking rate, effort, style and dialect or accent) on human speech perception was investigated. In listening experiments with 16 listeners, confusions of consonant-vowel-consonant (CVC) and vowel-consonant-vowel (VCV) sounds in speech-weighted noise were analyzed. Experiments were based on the OLLO logatome speech database, which was designed for a man-machine comparison. It contains utterances spoken by 50 speakers from five dialect/accent regions and covers several intrinsic variations. By comparing results depending on intrinsic and extrinsic variations (i.e., different levels of masking noise), the degradation induced by variabilities can be expressed in terms of the SNR. The spectral level distance between the respective speech segment and the long-term spectrum of the masking noise was found to be a good predictor for recognition rates, while phoneme confusions were influenced by the distance to spectrally close phonemes. An analysis based on transmitted information of articulatory features showed that voicing and manner of articulation are comparatively robust cues in the presence of intrinsic variations, whereas the coding of place is more degraded. The database and detailed results have been made available for comparisons between human speech recognition (HSR) and automatic speech recognizers (ASR).

  20. Super Normal Vector for Human Activity Recognition with Depth Cameras.

    Science.gov (United States)

    Yang, Xiaodong; Tian, YingLi

    2017-05-01

    The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information. We then propose a general scheme of super normal vector (SNV) to aggregate the low-level polynormals into a discriminative representation, which can be viewed as a simplified version of the Fisher kernel representation. In order to globally capture the spatial layout and temporal order, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time cells. In the extensive experiments, the proposed approach achieves superior performance to the state-of-the-art methods on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.

  1. Human Activity Recognition Supported on Indoor Localization: A Systematic Review.

    Science.gov (United States)

    Cerón, Jesús; López, Diego M

    2018-01-01

    The number of older adults is growing worldwide. This has a social and economic impact in all countries because of the increased number of older adults affected by chronic diseases, health emergencies, and disabilities, representing at the end high cost for the health system. To face this problem, the Ambient Assisted Living (AAL) domain has emerged. Its main objective is to extend the time that older adults can live independently in their homes. AAL is supported by different fields and technologies, being Human Activity Recognition (HAR), control of vital signs and location tracking the three of most interest during the last years. To perform a systematic review about Human Activity Recognition (HAR) approaches supported on Indoor Localization (IL) and vice versa, describing the methods they have used, the accuracy they have obtained and whether they have been directed towards the AAL domain or not. A systematic review of six databases was carried out (ACM, IEEE Xplore, PubMed, Science Direct and Springer). 27 papers were found. They were categorised into three groups according their approach: paper focus on 1. HAR, 2. IL, 3. HAR and IL. A detailed analysis of the following factors was performed: type of methods and technologies used for HAR, IL and data fusion, as well as the precision obtained for them. This systematic review shows that the relationship between HAR and IL has been very little studied, therefore providing insights of its potential mutual support to provide AAL solutions.

  2. Skeleton-based human action recognition using multiple sequence alignment

    Science.gov (United States)

    Ding, Wenwen; Liu, Kai; Cheng, Fei; Zhang, Jin; Li, YunSong

    2015-05-01

    Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.

  3. A Self-Powered Insole for Human Motion Recognition

    Directory of Open Access Journals (Sweden)

    Yingzhou Han

    2016-09-01

    Full Text Available Biomechanical energy harvesting is a feasible solution for powering wearable sensors by directly driving electronics or acting as wearable self-powered sensors. A wearable insole that not only can harvest energy from foot pressure during walking but also can serve as a self-powered human motion recognition sensor is reported. The insole is designed as a sandwich structure consisting of two wavy silica gel film separated by a flexible piezoelectric foil stave, which has higher performance compared with conventional piezoelectric harvesters with cantilever structure. The energy harvesting insole is capable of driving some common electronics by scavenging energy from human walking. Moreover, it can be used to recognize human motion as the waveforms it generates change when people are in different locomotion modes. It is demonstrated that different types of human motion such as walking and running are clearly classified by the insole without any external power source. This work not only expands the applications of piezoelectric energy harvesters for wearable power supplies and self-powered sensors, but also provides possible approaches for wearable self-powered human motion monitoring that is of great importance in many fields such as rehabilitation and sports science.

  4. MoCog1: A computer simulation of recognition-primed human decision making, considering emotions

    Science.gov (United States)

    Gevarter, William B.

    1992-01-01

    The successful results of the first stage of a research effort to develop a versatile computer model of motivated human cognitive behavior are reported. Most human decision making appears to be an experience-based, relatively straightforward, largely automatic response to situations, utilizing cues and opportunities perceived from the current environment. The development, considering emotions, of the architecture and computer program associated with such 'recognition-primed' decision-making is described. The resultant computer program (MoCog1) was successfully utilized as a vehicle to simulate earlier findings that relate how an individual's implicit theories orient the individual toward particular goals, with resultant cognitions, affects, and behavior in response to their environment.

  5. A unique memory process modulated by emotion underpins successful odor recognition and episodic retrieval in humans

    Directory of Open Access Journals (Sweden)

    Anne-Lise eSaive

    2014-06-01

    Full Text Available We behaviorally explore the link between olfaction, emotion and memory by testing the hypothesis that the emotion carried by odors facilitates the memory of specific unique events. To investigate this idea, we used a novel behavioral approach inspired by a paradigm developed by our team to study episodic memory in a controlled and as ecological as possible way in humans. The participants freely explored three unique and rich laboratory episodes; each episode consisted of three unfamiliar odors (What positioned at three specific locations (Where within a visual context (Which context. During the retrieval test, which occurred 24 to 72 hours after the encoding, odors were used to trigger the retrieval of the complex episodes. The participants were proficient in recognizing the target odors among distractors and retrieving the visuospatial context in which they were encountered. The episodic nature of the task generated high and stable memory performances, which were accompanied by faster responses and slower and deeper breathing. Successful odor recognition and episodic memory were not related to differences in odor investigation at encoding. However, memory performances were influenced by the emotional content of the odors, regardless of odor valence, with both pleasant and unpleasant odors generating higher recognition and episodic retrieval than neutral odors. Finally, the present study also suggested that when the binding between the odors and the spatio-contextual features of the episode was successful, the odor recognition and the episodic retrieval collapsed into a unique memory process that began as soon as the participants smelled the odors.

  6. A unique memory process modulated by emotion underpins successful odor recognition and episodic retrieval in humans

    Science.gov (United States)

    Saive, Anne-Lise; Royet, Jean-Pierre; Ravel, Nadine; Thévenet, Marc; Garcia, Samuel; Plailly, Jane

    2014-01-01

    We behaviorally explore the link between olfaction, emotion and memory by testing the hypothesis that the emotion carried by odors facilitates the memory of specific unique events. To investigate this idea, we used a novel behavioral approach inspired by a paradigm developed by our team to study episodic memory in a controlled and as ecological as possible way in humans. The participants freely explored three unique and rich laboratory episodes; each episode consisted of three unfamiliar odors (What) positioned at three specific locations (Where) within a visual context (Which context). During the retrieval test, which occurred 24–72 h after the encoding, odors were used to trigger the retrieval of the complex episodes. The participants were proficient in recognizing the target odors among distractors and retrieving the visuospatial context in which they were encountered. The episodic nature of the task generated high and stable memory performances, which were accompanied by faster responses and slower and deeper breathing. Successful odor recognition and episodic memory were not related to differences in odor investigation at encoding. However, memory performances were influenced by the emotional content of the odors, regardless of odor valence, with both pleasant and unpleasant odors generating higher recognition and episodic retrieval than neutral odors. Finally, the present study also suggested that when the binding between the odors and the spatio-contextual features of the episode was successful, the odor recognition and the episodic retrieval collapsed into a unique memory process that began as soon as the participants smelled the odors. PMID:24936176

  7. Recognition

    DEFF Research Database (Denmark)

    Gimmler, Antje

    2017-01-01

    In this article, I shall examine the cognitive, heuristic and theoretical functions of the concept of recognition. To evaluate both the explanatory power and the limitations of a sociological concept, the theory construction must be analysed and its actual productivity for sociological theory mus...

  8. Mathematical models of human behavior

    DEFF Research Database (Denmark)

    Møllgaard, Anders Edsberg

    at the Technical University of Denmark. The data set includes face-to-face interaction (Bluetooth), communication (calls and texts), mobility (GPS), social network (Facebook), and general background information including a psychological profile (questionnaire). This thesis presents my work on the Social Fabric...... data set, along with work on other behavioral data. The overall goal is to contribute to a quantitative understanding of human behavior using big data and mathematical models. Central to the thesis is the determination of the predictability of different human activities. Upper limits are derived....... Evidence is provided, which implies that the asymmetry is caused by a self-enhancement in the initiation dynamics. These results have implications for the formation of social networks and the dynamics of the links. It is shown that the Big Five Inventory (BFI) representing a psychological profile only...

  9. Improving human object recognition performance using video enhancement techniques

    Science.gov (United States)

    Whitman, Lucy S.; Lewis, Colin; Oakley, John P.

    2004-12-01

    Atmospheric scattering causes significant degradation in the quality of video images, particularly when imaging over long distances. The principle problem is the reduction in contrast due to scattered light. It is known that when the scattering particles are not too large compared with the imaging wavelength (i.e. Mie scattering) then high spatial resolution information may be contained within a low-contrast image. Unfortunately this information is not easily perceived by a human observer, particularly when using a standard video monitor. A secondary problem is the difficulty of achieving a sharp focus since automatic focus techniques tend to fail in such conditions. Recently several commercial colour video processing systems have become available. These systems use various techniques to improve image quality in low contrast conditions whilst retaining colour content. These systems produce improvements in subjective image quality in some situations, particularly in conditions of haze and light fog. There is also some evidence that video enhancement leads to improved ATR performance when used as a pre-processing stage. Psychological literature indicates that low contrast levels generally lead to a reduction in the performance of human observers in carrying out simple visual tasks. The aim of this paper is to present the results of an empirical study on object recognition in adverse viewing conditions. The chosen visual task was vehicle number plate recognition at long ranges (500 m and beyond). Two different commercial video enhancement systems are evaluated using the same protocol. The results show an increase in effective range with some differences between the different enhancement systems.

  10. When moral identity symbolization motivates prosocial behavior: the role of recognition and moral identity internalization.

    Science.gov (United States)

    Winterich, Karen Page; Aquino, Karl; Mittal, Vikas; Swartz, Richard

    2013-09-01

    This article examines the role of moral identity symbolization in motivating prosocial behaviors. We propose a 3-way interaction of moral identity symbolization, internalization, and recognition to predict prosocial behavior. When moral identity internalization is low, we hypothesize that high moral identity symbolization motivates recognized prosocial behavior due to the opportunity to present one's moral characteristics to others. In contrast, when moral identity internalization is high, prosocial behavior is motivated irrespective of the level of symbolization and recognition. Two studies provide support for this pattern examining volunteering of time. Our results provide a framework for predicting prosocial behavior by combining the 2 dimensions of moral identity with the situational factor of recognition. PsycINFO Database Record (c) 2013 APA, all rights reserved

  11. Human Activity Recognition by Combining a Small Number of Classifiers.

    Science.gov (United States)

    Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

    2016-09-01

    We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

  12. Human face recognition ability is specific and highly heritable.

    Science.gov (United States)

    Wilmer, Jeremy B; Germine, Laura; Chabris, Christopher F; Chatterjee, Garga; Williams, Mark; Loken, Eric; Nakayama, Ken; Duchaine, Bradley

    2010-03-16

    Compared with notable successes in the genetics of basic sensory transduction, progress on the genetics of higher level perception and cognition has been limited. We propose that investigating specific cognitive abilities with well-defined neural substrates, such as face recognition, may yield additional insights. In a twin study of face recognition, we found that the correlation of scores between monozygotic twins (0.70) was more than double the dizygotic twin correlation (0.29), evidence for a high genetic contribution to face recognition ability. Low correlations between face recognition scores and visual and verbal recognition scores indicate that both face recognition ability itself and its genetic basis are largely attributable to face-specific mechanisms. The present results therefore identify an unusual phenomenon: a highly specific cognitive ability that is highly heritable. Our results establish a clear genetic basis for face recognition, opening this intensively studied and socially advantageous cognitive trait to genetic investigation.

  13. Nonlinear dynamics in human behavior

    Energy Technology Data Exchange (ETDEWEB)

    Huys, Raoul [Centre National de la Recherche Scientifique (CNRS), 13 - Marseille (France); Marseille Univ. (France). Movement Science Inst.; Jirsa, Viktor K. (eds.) [Centre National de la Recherche Scientifique (CNRS), 13 - Marseille (France); Marseille Univ. (France). Movement Science Inst.; Florida Atlantic Univ., Boca Raton, FL (United States). Center for Complex Systems and Brain Sciences

    2010-07-01

    Humans engage in a seemingly endless variety of different behaviors, of which some are found across species, while others are conceived of as typically human. Most generally, behavior comes about through the interplay of various constraints - informational, mechanical, neural, metabolic, and so on - operating at multiple scales in space and time. Over the years, consensus has grown in the research community that, rather than investigating behavior only from bottom up, it may be also well understood in terms of concepts and laws on the phenomenological level. Such top down approach is rooted in theories of synergetics and self-organization using tools from nonlinear dynamics. The present compendium brings together scientists from all over the world that have contributed to the development of their respective fields departing from this background. It provides an introduction to deterministic as well as stochastic dynamical systems and contains applications to motor control and coordination, visual perception and illusion, as well as auditory perception in the context of speech and music. (orig.)

  14. Differences in Word Recognition between Early Bilinguals and Monolinguals: Behavioral and ERP Evidence

    Science.gov (United States)

    Lehtonen, Minna; Hulten, Annika; Rodriguez-Fornells, Antoni; Cunillera, Toni; Tuomainen, Jyrki; Laine, Matti

    2012-01-01

    We investigated the behavioral and brain responses (ERPs) of bilingual word recognition to three fundamental psycholinguistic factors, frequency, morphology, and lexicality, in early bilinguals vs. monolinguals. Earlier behavioral studies have reported larger frequency effects in bilinguals' nondominant vs. dominant language and in some studies…

  15. Theory of mind in schizophrenia: correlation with clinical symptomatology, emotional recognition and ward behavior.

    Science.gov (United States)

    Lee, Woo Kyeong; Kim, Yong Kyu

    2013-09-01

    Several studies have suggested the presence of a theory of mind (ToM) deficit in schizophrenic disorders. This study examined the relationship of emotion recognition, theory of mind, and ward behavior in patients with schizophrenia. Fifty-five patients with chronic schizophrenia completed measures of emotion recognition, ToM, intelligence, Positive and Negative Syndrome Scale (PANSS) and Nurse's Observation Scale for Inpatient Evaluation (NOSIE). Theory of mind sum score correlated significantly with IQ, emotion recognition, and ward behavior. Ward behavior was linked to the duration of the illness, and even more so to theory of mind deficits. Theory of mind contributed a significant proportion of the amount of variance to explain social behavior on the ward. Considering our study results, impaired theory of mind contributes significantly to the understanding of social competence in patients with schizophrenia. Copyright © 2012 Wiley Publishing Asia Pty Ltd.

  16. The Control of Behavior: Human and Environmental

    Science.gov (United States)

    Burhoe, Ralph Wendell

    1972-01-01

    Theological perspective on human and environmental behavior, with a view toward man's ultimate concerns or longest range values and the ultimate controls of behavior. Maintains that all human behavior and destiny is ultimately in the hand of a transcendent power which prevails over any human errors.'' (LK)

  17. Gambling disorder and other behavioral addictions: recognition and treatment.

    Science.gov (United States)

    Yau, Yvonne H C; Potenza, Marc N

    2015-01-01

    Addiction professionals and the public are recognizing that certain nonsubstance behaviors--such as gambling, Internet use, video-game playing, sex, eating, and shopping--bear resemblance to alcohol and drug dependence. Growing evidence suggests that these behaviors warrant consideration as nonsubstance or "behavioral" addictions and has led to the newly introduced diagnostic category "Substance-Related and Addictive Disorders" in DSM-5. At present, only gambling disorder has been placed in this category, with insufficient data for other proposed behavioral addictions to justify their inclusion. This review summarizes recent advances in our understanding of behavioral addictions, describes treatment considerations, and addresses future directions. Current evidence points to overlaps between behavioral and substance-related addictions in phenomenology, epidemiology, comorbidity, neurobiological mechanisms, genetic contributions, responses to treatments, and prevention efforts. Differences also exist. Recognizing behavioral addictions and developing appropriate diagnostic criteria are important in order to increase awareness of these disorders and to further prevention and treatment strategies.

  18. Associations between facial emotion recognition and young adolescents' behaviors in bullying.

    Directory of Open Access Journals (Sweden)

    Tiziana Pozzoli

    Full Text Available This study investigated whether different behaviors young adolescents can act during bullying episodes were associated with their ability to recognize morphed facial expressions of the six basic emotions, expressed at high and low intensity. The sample included 117 middle-school students (45.3% girls; mean age = 12.4 years who filled in a peer nomination questionnaire and individually performed a computerized emotion recognition task. Bayesian generalized mixed-effects models showed a complex picture, in which type and intensity of emotions, students' behavior and gender interacted in explaining recognition accuracy. Results were discussed with a particular focus on negative emotions and suggesting a "neutral" nature of emotion recognition ability, which does not necessarily lead to moral behavior but can also be used for pursuing immoral goals.

  19. Associations between facial emotion recognition and young adolescents’ behaviors in bullying

    Science.gov (United States)

    Gini, Gianluca; Altoè, Gianmarco

    2017-01-01

    This study investigated whether different behaviors young adolescents can act during bullying episodes were associated with their ability to recognize morphed facial expressions of the six basic emotions, expressed at high and low intensity. The sample included 117 middle-school students (45.3% girls; mean age = 12.4 years) who filled in a peer nomination questionnaire and individually performed a computerized emotion recognition task. Bayesian generalized mixed-effects models showed a complex picture, in which type and intensity of emotions, students’ behavior and gender interacted in explaining recognition accuracy. Results were discussed with a particular focus on negative emotions and suggesting a “neutral” nature of emotion recognition ability, which does not necessarily lead to moral behavior but can also be used for pursuing immoral goals. PMID:29131871

  20. HMM Adaptation for Improving a Human Activity Recognition System

    Directory of Open Access Journals (Sweden)

    Rubén San-Segundo

    2016-09-01

    Full Text Available When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs. This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity, and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance.

  1. Human Action Recognition Using Ordinal Measure of Accumulated Motion

    Directory of Open Access Journals (Sweden)

    Kim Wonjun

    2010-01-01

    Full Text Available This paper presents a method for recognizing human actions from a single query action video. We propose an action recognition scheme based on the ordinal measure of accumulated motion, which is robust to variations of appearances. To this end, we first define the accumulated motion image (AMI using image differences. Then the AMI of the query action video is resized to a subimage by intensity averaging and a rank matrix is generated by ordering the sample values in the sub-image. By computing the distances from the rank matrix of the query action video to the rank matrices of all local windows in the target video, local windows close to the query action are detected as candidates. To find the best match among the candidates, their energy histograms, which are obtained by projecting AMI values in horizontal and vertical directions, respectively, are compared with those of the query action video. The proposed method does not require any preprocessing task such as learning and segmentation. To justify the efficiency and robustness of our approach, the experiments are conducted on various datasets.

  2. Recognition-by-Components: A Theory of Human Image Understanding.

    Science.gov (United States)

    Biederman, Irving

    1987-01-01

    The theory proposed (recognition-by-components) hypothesizes the perceptual recognition of objects to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components. Experiments on the perception of briefly presented pictures support the theory. (Author/LMO)

  3. Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

    Directory of Open Access Journals (Sweden)

    Hong Zhang

    2013-01-01

    Full Text Available With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognition.

  4. How hardwired is human behavior?

    Science.gov (United States)

    Nicholson, N

    1998-01-01

    Time and time again managers have tried to eliminate hierarchies, politics, and interorganizational rivalry--but to no avail. Why? Evolutionary psychologists would say that they are working against nature--emotional and behavioral "hardwiring" that is the legacy of our Stone Age ancestors. In this evolutionary psychology primer for executives, Nigel Nicholson explores many of the Science's central tenets. Of course, evolutionary psychology is still an emerging discipline, and its strong connection with the theory of natural selection has sparked significant controversy. But, as Nicholson suggests, evolutionary psychology is now well established enough that its insights into human instinct will prove illuminating to anyone seeking to understand why people act the way they do in organizational settings. Take gossip. According to evolutionary psychology, our Stone Age ancestors needed this skill to survive the socially unpredictable conditions of the Savannah Plain. Thus, over time, the propensity to gossip became part of our mental programming. Executives trying to eradicate gossip at work might as well try to change their employees' musical tastes. Better to put one's energy into making sure the "rumor mill" avoids dishonesty or unkindness as much as possible. Evolutionary psychology also explores the dynamics of the human group. Clans on the Savannah Plain, for example, appear to have had no more than 150 members. The message for managers? People will likely be most effective in small organizational units. As every executive knows, it pays to be an insightful student of human nature. Evolutionary psychology adds another important chapter to consider.

  5. Appearance-based human gesture recognition using multimodal features for human computer interaction

    Science.gov (United States)

    Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

    2011-03-01

    The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

  6. Gambling Disorder and Other Behavioral Addictions: Recognition and Treatment

    Science.gov (United States)

    Yau, Yvonne H. C.; Potenza, Marc N.

    2015-01-01

    Addiction professionals and the public are recognizing that certain nonsubstance behaviors—such as gambling, Internet use, video-game playing, sex, eating, and shopping—bear resemblance to alcohol and drug dependence. Growing evidence suggests that these behaviors warrant consideration as nonsubstance or “behavioral” addictions and has led to the newly introduced diagnostic category “Substance-Related and Addictive Disorders” in DSM-5. At present, only gambling disorder has been placed in this category, with insufficient data for other proposed behavioral addictions to justify their inclusion. This review summarizes recent advances in our understanding of behavioral addictions, describes treatment considerations, and addresses future directions. Current evidence points to overlaps between behavioral and substance-related addictions in phenomenology, epidemiology, comorbidity, neurobiological mechanisms, genetic contributions, responses to treatments, and prevention efforts. Differences also exist. Recognizing behavioral addictions and developing appropriate diagnostic criteria are important in order to increase awareness of these disorders and to further prevention and treatment strategies. PMID:25747926

  7. Fracture behavior of human molars.

    Science.gov (United States)

    Keown, Amanda J; Lee, James J-W; Bush, Mark B

    2012-12-01

    Despite the durability of human teeth, which are able to withstand repeated loading while maintaining form and function, they are still susceptible to fracture. We focus here on longitudinal fracture in molar teeth-channel-like cracks that run along the enamel sidewall of the tooth between the gum line (cemento-enamel junction-CEJ) and the occlusal surface. Such fractures can often be painful and necessitate costly restorative work. The following study describes fracture experiments made on molar teeth of humans in which the molars are placed under axial compressive load using a hard indenting plate in order to induce longitudinal cracks in the enamel. Observed damage modes include fractures originating in the occlusal region ('radial-median cracks') and fractures emanating from the margin of the enamel in the region of the CEJ ('margin cracks'), as well as 'spalling' of enamel (the linking of longitudinal cracks). The loading conditions that govern fracture behavior in enamel are reported and observations made of the evolution of fracture as the load is increased. Relatively low loads were required to induce observable crack initiation-approximately 100 N for radial-median cracks and 200 N for margin cracks-both of which are less than the reported maximum biting force on a single molar tooth of several hundred Newtons. Unstable crack growth was observed to take place soon after and occurred at loads lower than those calculated by the current fracture models. Multiple cracks were observed on a single cusp, their interactions influencing crack growth behavior. The majority of the teeth tested in this study were noted to exhibit margin cracks prior to compression testing, which were apparently formed during the functional lifetime of the tooth. Such teeth were still able to withstand additional loading prior to catastrophic fracture, highlighting the remarkable damage containment capabilities of the natural tooth structure.

  8. Stages of processing in associative recognition: evidence from behavior, EEG, and classification.

    Science.gov (United States)

    Borst, Jelmer P; Schneider, Darryl W; Walsh, Matthew M; Anderson, John R

    2013-12-01

    In this study, we investigated the stages of information processing in associative recognition. We recorded EEG data while participants performed an associative recognition task that involved manipulations of word length, associative fan, and probe type, which were hypothesized to affect the perceptual encoding, retrieval, and decision stages of the recognition task, respectively. Analyses of the behavioral and EEG data, supplemented with classification of the EEG data using machine-learning techniques, provided evidence that generally supported the sequence of stages assumed by a computational model developed in the Adaptive Control of Thought-Rational cognitive architecture. However, the results suggested a more complex relationship between memory retrieval and decision-making than assumed by the model. Implications of the results for modeling associative recognition are discussed. The study illustrates how a classifier approach, in combination with focused manipulations, can be used to investigate the timing of processing stages.

  9. Human activity recognition based on feature selection in smart home using back-propagation algorithm.

    Science.gov (United States)

    Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

    2014-09-01

    In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Recognition of periodic behavioral patterns from streaming mobility data

    NARCIS (Netherlands)

    Baratchi, Mitra; Meratnia, Nirvana; Havinga, Paul J.M.; Stojmenovic, Ivan; Cheng, Zixue; Guo, Song

    2014-01-01

    Ubiquitous location-aware sensing devices have facilitated collection of large volumes of mobility data streams from moving entities such as people and animals, among others. Extraction of various types of periodic behavioral patterns hidden in such large volume of mobility data helps in

  11. Human face recognition ability is specific and highly heritable

    OpenAIRE

    Wilmer, Jeremy B.; Germine, Laura; Chabris, Christopher F.; Chatterjee, Garga; Williams, Mark; Loken, Eric; Nakayama, Ken; Duchaine, Bradley

    2010-01-01

    Compared with notable successes in the genetics of basic sensory transduction, progress on the genetics of higher level perception and cognition has been limited. We propose that investigating specific cognitive abilities with well-defined neural substrates, such as face recognition, may yield additional insights. In a twin study of face recognition, we found that the correlation of scores between monozygotic twins (0.70) was more than double the dizygotic twin correlation (0.29), evidence fo...

  12. Human Rights and Behavior Modification

    Science.gov (United States)

    Roos, Philip

    1974-01-01

    Criticisms of behavior modification, which charge that it violates ethical and legal principles, are discussed and reasons are presented to explain behavior modification's susceptibility to attack. (GW)

  13. Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

    Science.gov (United States)

    Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

    2012-01-01

    Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

  14. University Student Awareness of Skin Cancer: Behaviors, Recognition, and Prevention.

    Science.gov (United States)

    Trad, Megan; Estaville, Lawrence

    2017-03-01

    Skin cancer is the most common cancer, and it often is preventable. The authors sought to evaluate behavior and knowledge regarding skin cancer among students at a Texas university. The authors recruited a diverse group of students in terms of sex, age, and ethnicity to participate in a survey regarding knowledge of skin cancer signs, use of tanning beds, and performance of self-assessment for skin cancer. Participating students could complete surveys in classrooms, at health fairs, or online via Survey Monkey. The authors examined data for the 3 variables in relation to sex, ethnicity, and age. A total of 512 responses were completed. Female students completed 371 (72.46%) surveys, and male students completed 141 (27.54%). The ethnicity of student participants was nearly evenly split among whites, African Americans, and Hispanics. Ethnicity was the most significant factor influencing the knowledge of skin cancer and behaviors to prevent it. Specifically, Hispanic and African American students possessed a lower level of skin cancer awareness. More female students than male students used tanning beds, and although use was self-reported as infrequent, the results imply that 4500 of the university's students might use tanning beds, which is concerning if extrapolated to other university student populations in Texas. Behavioral intervention is critical in reducing students' risk of skin cancer in later years, and university students must acquire knowledge to increase their awareness of skin health and to minimize their risk of developing skin cancer. Radiation therapists are uniquely positioned to share knowledge of skin cancer. ©2017 American Society of Radiologic Technologists.

  15. Human Gait Recognition Based on Multiview Gait Sequences

    Directory of Open Access Journals (Sweden)

    Xiaxi Huang

    2008-05-01

    Full Text Available Most of the existing gait recognition methods rely on a single view, usually the side view, of the walking person. This paper investigates the case in which several views are available for gait recognition. It is shown that each view has unequal discrimination power and, therefore, should have unequal contribution in the recognition process. In order to exploit the availability of multiple views, several methods for the combination of the results that are obtained from the individual views are tested and evaluated. A novel approach for the combination of the results from several views is also proposed based on the relative importance of each view. The proposed approach generates superior results, compared to those obtained by using individual views or by using multiple views that are combined using other combination methods.

  16. A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks

    Directory of Open Access Journals (Sweden)

    Alejandra García-Hernández

    2017-11-01

    Full Text Available Human Activity Recognition (HAR is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location.

  17. [-25]A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks.

    Science.gov (United States)

    García-Hernández, Alejandra; Galván-Tejada, Carlos E; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Velasco-Elizondo, Perla; Cárdenas-Vargas, Rogelio

    2017-11-21

    Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location.

  18. Human Wearable Attribute Recognition Using Probability-Map-Based Decomposition of Thermal Infrared Images

    OpenAIRE

    KRESNARAMAN, Brahmastro; KAWANISHI, Yasutomo; DEGUCHI, Daisuke; TAKAHASHI, Tomokazu; MEKADA, Yoshito; IDE, Ichiro; MURASE, Hiroshi

    2017-01-01

    This paper addresses the attribute recognition problem, a field of research that is dominated by studies in the visible spectrum. Only a few works are available in the thermal spectrum, which is fundamentally different from the visible one. This research performs recognition specifically on wearable attributes, such as glasses and masks. Usually these attributes are relatively small in size when compared with the human body, on top of a large intra-class variation of the human body itself, th...

  19. Recognition for Positive Behavior as a Critical Youth Development Construct: Conceptual Bases and Implications on Youth Service Development

    Directory of Open Access Journals (Sweden)

    Ben M. F. Law

    2012-01-01

    Full Text Available Recognition for positive behavior is an appropriate response of the social environment to elicit desirable external behavior among the youth. Such positive responses, rendered from various social systems, include tangible and intangible reinforcements. The following theories are used to explain the importance of recognizing positive behavior: operational conditioning, observational learning, self-determination, and humanistic perspective. In the current work, culturally and socially desirable behaviors are discussed in detail with reference to Chinese adolescents. Positive behavior recognition is especially important to adolescent development because it promotes identity formation as well as cultivates moral reasoning and social perspective thinking from various social systems. The significance of recognizing positive behavior is illustrated through the support, tutorage, invitation, and subsidy provided by Hong Kong’s social systems in recognition of adolescent volunteerism. The practical implications of positive behavior recognition on youth development programs are also discussed in this work.

  20. Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition

    Directory of Open Access Journals (Sweden)

    Jiasong Zhu

    2018-06-01

    Full Text Available Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. Unlike traditional traffic analysis methods which rely on low-resolution videos captured by road cameras, we capture 4K ( 3840 × 2178 traffic videos at a busy road intersection of a modern megacity by flying a unmanned aerial vehicle (UAV during the rush hours. We then manually annotate locations and types of road vehicles. The proposed method consists of the following three steps: (1 vehicle detection and type recognition based on deep neural networks; (2 vehicle tracking by data association and vehicle trajectory modeling; (3 vehicle behavior recognition by nearest neighbor search and by bidirectional long short-term memory network, respectively. This paper also presents experimental results of the proposed framework in comparison with state-of-the-art approaches on the 4K testing traffic video, which demonstrated the effectiveness and superiority of the proposed method.

  1. Parents’ Emotion-Related Beliefs, Behaviors, and Skills Predict Children's Recognition of Emotion

    Science.gov (United States)

    Castro, Vanessa L.; Halberstadt, Amy G.; Lozada, Fantasy T.; Craig, Ashley B.

    2015-01-01

    Children who are able to recognize others’ emotions are successful in a variety of socioemotional domains, yet we know little about how school-aged children's abilities develop, particularly in the family context. We hypothesized that children develop emotion recognition skill as a function of parents’ own emotion-related beliefs, behaviors, and skills. We examined parents’ beliefs about the value of emotion and guidance of children's emotion, parents’ emotion labeling and teaching behaviors, and parents’ skill in recognizing children's emotions in relation to their school-aged children's emotion recognition skills. Sixty-nine parent-child dyads completed questionnaires, participated in dyadic laboratory tasks, and identified their own emotions and emotions felt by the other participant from videotaped segments. Regression analyses indicate that parents’ beliefs, behaviors, and skills together account for 37% of the variance in child emotion recognition ability, even after controlling for parent and child expressive clarity. The findings suggest the importance of the family milieu in the development of children's emotion recognition skill in middle childhood, and add to accumulating evidence suggesting important age-related shifts in the relation between parental emotion socialization and child emotional development. PMID:26005393

  2. Characteristics of the nurse manager's recognition behavior and its relation to sense of coherence of staff nurses in Japan.

    Science.gov (United States)

    Miyata, Chiharu; Arai, Hidenori; Suga, Sawako

    2015-01-01

    The recognition behaviors strongly influence the job satisfaction of staff nurses and an extremely important factor for the prevention of burnout and the promotion of retention. Additionally, among internal factors that may affect worker's mental health, a sense of coherence (SOC) is an important concept from the view of the salutogenic theory and stress recognition style. Individual's SOC increases in relation to recognition behavior. However, in Japan, few studies have examined the effect of recognition behaviors on the SOC of staff nurses. The purpose of this study was to investigate how staff nurses perceive recognition behaviors of the nurse manager and to determine the relationship between recognition behaviors and the staff nurses' SOC. This quantitative, cross-sectional study involved 10 hospitals in Japan. A total of 1425 nurses completed the questionnaire. As a result, the perceptions of nurse manager's recognition behaviors by staff nurses were evaluated by presentation and report, individual value and the transfer of responsibility, and professional development. The median score of staff nurse SOC-13 was 50 (IQR; 45-55). Significant differences in SOC scores were found in marital status, age, years of experience, and mental and physical health condition. In conclusion, recognition behaviors by the nurse manager can improve staff nurse's SOC and effectively support the mental health of the staff nurse.

  3. Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes.

    Science.gov (United States)

    Meyer, Bernd T; Brand, Thomas; Kollmeier, Birger

    2011-01-01

    The aim of this study is to quantify the gap between the recognition performance of human listeners and an automatic speech recognition (ASR) system with special focus on intrinsic variations of speech, such as speaking rate and effort, altered pitch, and the presence of dialect and accent. Second, it is investigated if the most common ASR features contain all information required to recognize speech in noisy environments by using resynthesized ASR features in listening experiments. For the phoneme recognition task, the ASR system achieved the human performance level only when the signal-to-noise ratio (SNR) was increased by 15 dB, which is an estimate for the human-machine gap in terms of the SNR. The major part of this gap is attributed to the feature extraction stage, since human listeners achieve comparable recognition scores when the SNR difference between unaltered and resynthesized utterances is 10 dB. Intrinsic variabilities result in strong increases of error rates, both in human speech recognition (HSR) and ASR (with a relative increase of up to 120%). An analysis of phoneme duration and recognition rates indicates that human listeners are better able to identify temporal cues than the machine at low SNRs, which suggests incorporating information about the temporal dynamics of speech into ASR systems.

  4. Ecological Environment in Terms of Human Behavior

    OpenAIRE

    Chen, Xiaogang; Zhou, Dehu; Lin, Hui

    2013-01-01

    In terms of human behavior, company and government policy, it is proposed that the ecological behavior of human being is the basis of influence on the ecological environment construction in Poyang Lake and measures to ensure the sustainable development of ecological environment in Poyang Lake.

  5. Mimesis: Linking Postmodern Theory to Human Behavior

    Science.gov (United States)

    Dybicz, Phillip

    2010-01-01

    This article elaborates mimesis as a theory of causality used to explain human behavior. Drawing parallels to social constructionism's critique of positivism and naturalism, mimesis is offered as a theory of causality explaining human behavior that contests the current dominance of Newton's theory of causality as cause and effect. The contestation…

  6. Rational Emotive Behavior Therapy: Humanism in Action.

    Science.gov (United States)

    Hill, Larry K.

    1996-01-01

    Claims that humanism, in both concept and philosophy, is encased in a literature that is predominantly abstract, making humanism difficult to translate into tangible day-to-day action. Argues that rational emotive behavior therapy (REBT), however, provides a detailed method for translating humanist concepts into humanist behavior. (RJM)

  7. Cognitive penetrability and emotion recognition in human facial expressions

    Directory of Open Access Journals (Sweden)

    Francesco eMarchi

    2015-06-01

    Full Text Available Do our background beliefs, desires, and mental images influence our perceptual experience of the emotions of others? In this paper, we will address the possibility of cognitive penetration of perceptual experience in the domain of social cognition. In particular, we focus on emotion recognition based on the visual experience of facial expressions. After introducing the current debate on cognitive penetration, we review examples of perceptual adaptation for facial expressions of emotion. This evidence supports the idea that facial expressions are perceptually processed as wholes. That is, the perceptual system integrates lower-level facial features, such as eyebrow orientation, mouth angle etc., into facial compounds. We then present additional experimental evidence showing that in some cases, emotion recognition on the basis of facial expression is sensitive to and modified by the background knowledge of the subject. We argue that such sensitivity is best explained as a difference in the visual experience of the facial expression, not just as a modification of the judgment based on this experience. The difference in experience is characterized as the result of the interference of background knowledge with the perceptual integration process for faces. Thus, according to the best explanation, we have to accept cognitive penetration in some cases of emotion recognition. Finally, we highlight a recent model of social vision in order to propose a mechanism for cognitive penetration used in the face-based recognition of emotion.

  8. Automatic sign language recognition inspired by human sign perception

    NARCIS (Netherlands)

    Ten Holt, G.A.

    2010-01-01

    Automatic sign language recognition is a relatively new field of research (since ca. 1990). Its objectives are to automatically analyze sign language utterances. There are several issues within the research area that merit investigation: how to capture the utterances (cameras, magnetic sensors,

  9. Human Activity Recognition in a Car with Embedded Devices

    Directory of Open Access Journals (Sweden)

    Danilo Burbano

    2015-11-01

    Full Text Available Detection and prediction of drowsiness is key for the implementation of intelligent vehicles aimed to prevent highway crashes. There are several approaches for such solution. In thispaper the computer vision approach will be analysed, where embedded devices (e.g.videocameras are used along with machine learning and pattern recognition techniques for implementing suitable solutions for detecting driver fatigue. Most of the research in computer vision systems focused on the analysis of blinks, this is a notable solution when it is combined with additional patterns like yawing or head motion for the recognition of drowsiness. The first step in this approach is the face recognition, where AdaBoost algorithm shows accurate results for the feature extraction, whereas regarding the detection of drowsiness the data-driven classifiers such as Support Vector Machine (SVM yields remarkable results. One underlying component for implementing a computer vision technology for detection of drowsiness is a database of spontaneous images from the Facial Action Coding System (FACS, where the classifier can be trained accordingly. This paper introduces a straightforward prototype for detection of drowsiness, where the Viola-Jones method is used for face recognition and cascade classifier is used for the detection of a contiguous sequence of eyes closed, which a reconsidered as drowsiness.

  10. Jet behaviors and ejection mode recognition of electrohydrodynamic direct-write

    Science.gov (United States)

    Zheng, Jianyi; Zhang, Kai; Jiang, Jiaxin; Wang, Xiang; Li, Wenwang; Liu, Yifang; Liu, Juan; Zheng, Gaofeng

    2018-01-01

    By introducing image recognition and micro-current testing, jet behavior research was conducted, in which the real-time recognition of ejection mode was realized. To study the factors influencing ejection modes and the current variation trends under different modes, an Electrohydrodynamic Direct-Write (EDW) system with functions of current detection and ejection mode recognition was firstly built. Then a program was developed to recognize the jet modes. As the voltage applied to the metal tip increased, four jet ejection modes in EDW occurred: droplet ejection mode, Taylor cone ejection mode, retractive ejection mode and forked ejection mode. In this work, the corresponding relationship between the ejection modes and the effect on fiber deposition as well as current was studied. The real-time identification of ejection mode and detection of electrospinning current was realized. The results in this paper are contributed to enhancing the ejection stability, providing a good technical basis to produce continuous uniform nanofibers controllably.

  11. Nest marking behavior and chemical composition of olfactory cues involved in nest recognition in Megachile rotundata.

    Science.gov (United States)

    Guédot, Christelle; Buckner, James S; Hagen, Marcia M; Bosch, Jordi; Kemp, William P; Pitts-Singer, Theresa L

    2013-08-01

    In-nest observations of the solitary bee, Megachile rotundata (F.), revealed that nesting females apply olfactory cues to nests for nest recognition. On their way in and out of the nest, females drag the abdomen along the entire length of the nest, and sometimes deposit fluid droplets from the tip of the abdomen. The removal of bee-marked sections of the nest resulted in hesitation and searching behavior by females, indicating the loss of olfactory cues used for nest recognition. Chemical analysis of female cuticles and the deposits inside marked nesting tubes revealed the presence of hydrocarbons, wax esters, fatty aldehydes, and fatty alcohol acetate esters. Chemical compositions were similar across tube samples, but proportionally different from cuticular extracts. These findings reveal the importance of lipids as chemical signals for nest recognition and suggest that the nest-marking cues are derived from a source in addition to, or other than, the female cuticle.

  12. HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

    Directory of Open Access Journals (Sweden)

    Linlin Guo

    2018-01-01

    Full Text Available The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.

  13. Human hippocampal and parahippocampal activity during visual associative recognition memory for spatial and nonspatial stimulus configurations.

    Science.gov (United States)

    Düzel, Emrah; Habib, Reza; Rotte, Michael; Guderian, Sebastian; Tulving, Endel; Heinze, Hans-Jochen

    2003-10-15

    Evidence from animal studies points to the importance of the parahippocampal region (PHR) [including entorhinal, perirhinal, and parahippocampal (PHC) cortices] for recognition of visual stimuli. Recent findings in animals suggest that PHR may also be involved in visual associative recognition memory for configurations of stimuli. Thus far, however, such involvement has not been demonstrated in humans. In fact, it has been argued that associative recognition in humans is critically dependent on the hippocampal formation (HF). To better understand the division of function between HF and PHR during recognition memory in humans, we measured the activity of both areas in healthy young adults during an associative recognition memory task using functional magnetic resonance imaging. To more precisely characterize the nature of the associations that might be coded by the HF and PHR during recognition, subjects were required to learn and were later tested for associations based on either the spatial arrangements of two stimuli or the identity of two stimuli (a face and a tool). An area in the PHC was found to be more active for recognized old configurations than new configurations in both the spatial and identity conditions. The HF, on the other hand, was more active for recognition of new configurations than old configurations and also more active in the spatial than the identity condition. These data highlight the involvement of PHR in the long-term coding of associative relationships between stimuli and help to clarify the nature of its functional distinction from the HF.

  14. Modeling guidance and recognition in categorical search: Bridging human and computer object detection

    OpenAIRE

    Zelinsky, Gregory J.; Peng, Yifan; Berg, Alexander C.; Samaras, Dimitris

    2013-01-01

    Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of ...

  15. Human face recognition using eigenface in cloud computing environment

    Science.gov (United States)

    Siregar, S. T. M.; Syahputra, M. F.; Rahmat, R. F.

    2018-02-01

    Doing a face recognition for one single face does not take a long time to process, but if we implement attendance system or security system on companies that have many faces to be recognized, it will take a long time. Cloud computing is a computing service that is done not on a local device, but on an internet connected to a data center infrastructure. The system of cloud computing also provides a scalability solution where cloud computing can increase the resources needed when doing larger data processing. This research is done by applying eigenface while collecting data as training data is also done by using REST concept to provide resource, then server can process the data according to existing stages. After doing research and development of this application, it can be concluded by implementing Eigenface, recognizing face by applying REST concept as endpoint in giving or receiving related information to be used as a resource in doing model formation to do face recognition.

  16. Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

    Science.gov (United States)

    Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

    2017-06-01

    Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

  17. Real-Time Multiview Recognition of Human Gestures by Distributed Image Processing

    Directory of Open Access Journals (Sweden)

    Sato Kosuke

    2010-01-01

    Full Text Available Since a gesture involves a dynamic and complex motion, multiview observation and recognition are desirable. For the better representation of gestures, one needs to know, in the first place, from which views a gesture should be observed. Furthermore, it becomes increasingly important how the recognition results are integrated when larger numbers of camera views are considered. To investigate these problems, we propose a framework under which multiview recognition is carried out, and an integration scheme by which the recognition results are integrated online and in realtime. For performance evaluation, we use the ViHASi (Virtual Human Action Silhouette public image database as a benchmark and our Japanese sign language (JSL image database that contains 18 kinds of hand signs. By examining the recognition rates of each gesture for each view, we found gestures that exhibit view dependency and the gestures that do not. Also, we found that the view dependency itself could vary depending on the target gesture sets. By integrating the recognition results of different views, our swarm-based integration provides more robust and better recognition performance than individual fixed-view recognition agents.

  18. Driving behavior recognition using EEG data from a simulated car-following experiment.

    Science.gov (United States)

    Yang, Liu; Ma, Rui; Zhang, H Michael; Guan, Wei; Jiang, Shixiong

    2017-11-23

    Driving behavior recognition is the foundation of driver assistance systems, with potential applications in automated driving systems. Most prevailing studies have used subjective questionnaire data and objective driving data to classify driving behaviors, while few studies have used physiological signals such as electroencephalography (EEG) to gather data. To bridge this gap, this paper proposes a two-layer learning method for driving behavior recognition using EEG data. A simulated car-following driving experiment was designed and conducted to simultaneously collect data on the driving behaviors and EEG data of drivers. The proposed learning method consists of two layers. In Layer I, two-dimensional driving behavior features representing driving style and stability were selected and extracted from raw driving behavior data using K-means and support vector machine recursive feature elimination. Five groups of driving behaviors were classified based on these two-dimensional driving behavior features. In Layer II, the classification results from Layer I were utilized as inputs to generate a k-Nearest-Neighbor classifier identifying driving behavior groups using EEG data. Using independent component analysis, a fast Fourier transformation, and linear discriminant analysis sequentially, the raw EEG signals were processed to extract two core EEG features. Classifier performance was enhanced using the adaptive synthetic sampling approach. A leave-one-subject-out cross validation was conducted. The results showed that the average classification accuracy for all tested traffic states was 69.5% and the highest accuracy reached 83.5%, suggesting a significant correlation between EEG patterns and car-following behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Vomeronasal organ lesion disrupts social odor recognition, behaviors and fitness in golden hamsters.

    Science.gov (United States)

    Liu, Yingjuan; Zhang, Jinhua; Liu, Dingzhen; Zhang, Jianxu

    2014-06-01

    Most studies support the viewpoint that the vomeronasal organ has a profound effect on conspecific odor recognition, scent marking and mating behavior in the golden hamster (Mesocricetus auratus). However, the role of the vomeronasal organ in social odor recognition, social interaction and fitness is not well understood. Therefore, we conducted a series of behavioral and physiological tests to examine the referred points in golden hamster. We found that male hamsters with vomeronasal organ lesion showed no preference between a predator odor (the anal gland secretion of the Siberian weasels (Mustela sibirica) and putative female pheromone components (myristic acid and palmitic acid), but were still able to discriminate between these 2 kinds of odors. In behavioral tests of anxiety, we found that vomeronasal organ removal causes female hamsters to spend much less time in center grids and to cross fewer center grids and males to make fewer crossings between light and dark boxes than sham-operated controls. This indicates that a chronic vomeronasal organ lesion induced anxious responses in females. In aggressive behavioral tests, we found that a chronic vomeronasal organ lesion decreased agonistic behavior in female hamsters but not in males. The pup growth and litter size show no differences between the 2 groups. All together, our data suggested that vomeronasal organ ablation disrupted the olfactory recognition of social chemosignals in males, and induced anxiety-like and aggressive behavior changes in females. However, a vomeronasal organ lesion did not affect the reproductive capacity and fitness of hamsters. Our studies may have important implications concerning the role of the vomeronasal organ in golden hamsters and also in rodents. © 2013 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd.

  20. Machine Understanding of Human Behavior

    NARCIS (Netherlands)

    Pantic, Maja; Pentland, Alex; Nijholt, Antinus; Huang, Thomas

    2007-01-01

    A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should

  1. Emotion Recognition in Animated Compared to Human Stimuli in Adolescents with Autism Spectrum Disorder

    Science.gov (United States)

    Brosnan, Mark; Johnson, Hilary; Grawmeyer, Beate; Chapman, Emma; Benton, Laura

    2015-01-01

    There is equivocal evidence as to whether there is a deficit in recognising emotional expressions in Autism spectrum disorder (ASD). This study compared emotion recognition in ASD in three types of emotion expression media (still image, dynamic image, auditory) across human stimuli (e.g. photo of a human face) and animated stimuli (e.g. cartoon…

  2. Sitting is the new smoking : online complex human activity recognition with smartphones and wearables

    NARCIS (Netherlands)

    Shoaib, Muhammad

    2017-01-01

    Human activity recognition plays an important role in fitness tracking, health monitoring, context-aware feedback and self-management of smartphones and wearable devices. These devices are equipped with different sensors which can be used to recognize various human activities. A significant amount of

  3. A survey on using domain and contextual knowledge for human activity recognition in video streams

    NARCIS (Netherlands)

    Onofri, L.; Soda, P.; Pechenizkiy, M.; Iannello, G.

    2016-01-01

    Human activity recognition has gained an increasing relevance in computer vision and it can be tackled with either non-hierarchical or hierarchical approaches. The former, also known as single-layered approaches, are those that represent and recognize human activities directly from the extracted

  4. Multi-stream CNN: Learning representations based on human-related regions for action recognition

    NARCIS (Netherlands)

    Tu, Zhigang; Xie, Wei; Qin, Qianqing; Poppe, R.W.; Veltkamp, R.C.; Li, Baoxin; Yuan, Junsong

    2018-01-01

    The most successful video-based human action recognition methods rely on feature representations extracted using Convolutional Neural Networks (CNNs). Inspired by the two-stream network (TS-Net), we propose a multi-stream Convolutional Neural Network (CNN) architecture to recognize human actions. We

  5. Capturing specific abilities as a window into human individuality: the example of face recognition.

    Science.gov (United States)

    Wilmer, Jeremy B; Germine, Laura; Chabris, Christopher F; Chatterjee, Garga; Gerbasi, Margaret; Nakayama, Ken

    2012-01-01

    Proper characterization of each individual's unique pattern of strengths and weaknesses requires good measures of diverse abilities. Here, we advocate combining our growing understanding of neural and cognitive mechanisms with modern psychometric methods in a renewed effort to capture human individuality through a consideration of specific abilities. We articulate five criteria for the isolation and measurement of specific abilities, then apply these criteria to face recognition. We cleanly dissociate face recognition from more general visual and verbal recognition. This dissociation stretches across ability as well as disability, suggesting that specific developmental face recognition deficits are a special case of a broader specificity that spans the entire spectrum of human face recognition performance. Item-by-item results from 1,471 web-tested participants, included as supplementary information, fuel item analyses, validation, norming, and item response theory (IRT) analyses of our three tests: (a) the widely used Cambridge Face Memory Test (CFMT); (b) an Abstract Art Memory Test (AAMT), and (c) a Verbal Paired-Associates Memory Test (VPMT). The availability of this data set provides a solid foundation for interpreting future scores on these tests. We argue that the allied fields of experimental psychology, cognitive neuroscience, and vision science could fuel the discovery of additional specific abilities to add to face recognition, thereby providing new perspectives on human individuality.

  6. Discriminative Vision-Based Recovery and Recognition of Human Motion

    NARCIS (Netherlands)

    Poppe, Ronald Walter

    2009-01-01

    The automatic analysis of human motion from images opens up the way for applications in the domains of security and surveillance, human-computer interaction, animation, retrieval and sports motion analysis. In this dissertation, the focus is on robust and fast human pose recovery and action

  7. Prenatal flavor exposure affects flavor recognition and stress-related behavior of piglets.

    Science.gov (United States)

    Oostindjer, Marije; Bolhuis, J Elizabeth; van den Brand, Henry; Kemp, Bas

    2009-11-01

    Exposure to flavors in the amniotic fluid and mother's milk derived from the maternal diet has been shown to modulate food preferences and neophobia of young animals of several species. Aim of the experiment was to study the effects of pre- and postnatal flavor exposure on behavior of piglets during (re)exposure to this flavor. Furthermore, we investigated whether varying stress levels, caused by different test settings, affected behavior of animals during (re)exposure. Piglets were exposed to anisic flavor through the maternal diet during late gestation and/or during lactation or never. Piglets that were prenatally exposed to the flavor through the maternal diet behaved differently compared with unexposed pigs during reexposure to the flavor in several tests, suggesting recognition of the flavor. The differences between groups were more pronounced in tests with relatively high stress levels. This suggests that stress levels, caused by the design of the test, can affect the behavior shown in the presence of the flavor. We conclude that prenatal flavor exposure affects behaviors of piglets that are indicative of recognition and that these behaviors are influenced by stress levels during (re)exposure.

  8. A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

    Directory of Open Access Journals (Sweden)

    Minglin Wu

    2016-10-01

    Full Text Available In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.

  9. A general framework for sensor-based human activity recognition.

    Science.gov (United States)

    Köping, Lukas; Shirahama, Kimiaki; Grzegorzek, Marcin

    2018-04-01

    Today's wearable devices like smartphones, smartwatches and intelligent glasses collect a large amount of data from their built-in sensors like accelerometers and gyroscopes. These data can be used to identify a person's current activity and in turn can be utilised for applications in the field of personal fitness assistants or elderly care. However, developing such systems is subject to certain restrictions: (i) since more and more new sensors will be available in the future, activity recognition systems should be able to integrate these new sensors with a small amount of manual effort and (ii) such systems should avoid high acquisition costs for computational power. We propose a general framework that achieves an effective data integration based on the following two characteristics: Firstly, a smartphone is used to gather and temporally store data from different sensors and transfer these data to a central server. Thus, various sensors can be integrated into the system as long as they have programming interfaces to communicate with the smartphone. The second characteristic is a codebook-based feature learning approach that can encode data from each sensor into an effective feature vector only by tuning a few intuitive parameters. In the experiments, the framework is realised as a real-time activity recognition system that integrates eight sensors from a smartphone, smartwatch and smartglasses, and its effectiveness is validated from different perspectives such as accuracies, sensor combinations and sampling rates. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. Face identity recognition in autism spectrum disorders: a review of behavioral studies.

    Science.gov (United States)

    Weigelt, Sarah; Koldewyn, Kami; Kanwisher, Nancy

    2012-03-01

    Face recognition--the ability to recognize a person from their facial appearance--is essential for normal social interaction. Face recognition deficits have been implicated in the most common disorder of social interaction: autism. Here we ask: is face identity recognition in fact impaired in people with autism? Reviewing behavioral studies we find no strong evidence for a qualitative difference in how facial identity is processed between those with and without autism: markers of typical face identity recognition, such as the face inversion effect, seem to be present in people with autism. However, quantitatively--i.e., how well facial identity is remembered or discriminated--people with autism perform worse than typical individuals. This impairment is particularly clear in face memory and in face perception tasks in which a delay intervenes between sample and test, and less so in tasks with no memory demand. Although some evidence suggests that this deficit may be specific to faces, further evidence on this question is necessary. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. Fast human behavior analysis for scene understanding

    NARCIS (Netherlands)

    Lao, W.

    2011-01-01

    Human behavior analysis has become an active topic of great interest and relevance for a number of applications and areas of research. The research in recent years has been considerably driven by the growing level of criminal behavior in large urban areas and increase of terroristic actions. Also,

  12. Comparison of Forced-Alignment Speech Recognition and Humans for Generating Reference VAD

    DEFF Research Database (Denmark)

    Kraljevski, Ivan; Tan, Zheng-Hua; Paola Bissiri, Maria

    2015-01-01

    This present paper aims to answer the question whether forced-alignment speech recognition can be used as an alternative to humans in generating reference Voice Activity Detection (VAD) transcriptions. An investigation of the level of agreement between automatic/manual VAD transcriptions and the ......This present paper aims to answer the question whether forced-alignment speech recognition can be used as an alternative to humans in generating reference Voice Activity Detection (VAD) transcriptions. An investigation of the level of agreement between automatic/manual VAD transcriptions...... and the reference ones produced by a human expert was carried out. Thereafter, statistical analysis was employed on the automatically produced and the collected manual transcriptions. Experimental results confirmed that forced-alignment speech recognition can provide accurate and consistent VAD labels....

  13. Enhanced bag of words using multilevel k-means for human activity recognition

    Directory of Open Access Journals (Sweden)

    Motasem Elshourbagy

    2016-07-01

    Full Text Available This paper aims to enhance the bag of features in order to improve the accuracy of human activity recognition. In this paper, human activity recognition process consists of four stages: local space time features detection, feature description, bag of features representation, and SVMs classification. The k-means step in the bag of features is enhanced by applying three levels of clustering: clustering per video, clustering per action class, and clustering for the final code book. The experimental results show that the proposed method of enhancement reduces the time and memory requirements, and enables the use of all training data in the k-means clustering algorithm. The evaluation of accuracy of action classification on two popular datasets (KTH and Weizmann has been performed. In addition, the proposed method improves the human activity recognition accuracy by 5.57% on the KTH dataset using the same detector, descriptor, and classifier.

  14. Simulating human behavior for national security human interactions.

    Energy Technology Data Exchange (ETDEWEB)

    Bernard, Michael Lewis; Hart, Dereck H.; Verzi, Stephen J.; Glickman, Matthew R.; Wolfenbarger, Paul R.; Xavier, Patrick Gordon

    2007-01-01

    This 3-year research and development effort focused on what we believe is a significant technical gap in existing modeling and simulation capabilities: the representation of plausible human cognition and behaviors within a dynamic, simulated environment. Specifically, the intent of the ''Simulating Human Behavior for National Security Human Interactions'' project was to demonstrate initial simulated human modeling capability that realistically represents intra- and inter-group interaction behaviors between simulated humans and human-controlled avatars as they respond to their environment. Significant process was made towards simulating human behaviors through the development of a framework that produces realistic characteristics and movement. The simulated humans were created from models designed to be psychologically plausible by being based on robust psychological research and theory. Progress was also made towards enhancing Sandia National Laboratories existing cognitive models to support culturally plausible behaviors that are important in representing group interactions. These models were implemented in the modular, interoperable, and commercially supported Umbra{reg_sign} simulation framework.

  15. Scaling behavior of online human activity

    Science.gov (United States)

    Zhao, Zhi-Dan; Cai, Shi-Min; Huang, Junming; Fu, Yan; Zhou, Tao

    2012-11-01

    The rapid development of the Internet technology enables humans to explore the web and record the traces of online activities. From the analysis of these large-scale data sets (i.e., traces), we can get insights about the dynamic behavior of human activity. In this letter, the scaling behavior and complexity of human activity in the e-commerce, such as music, books, and movies rating, are comprehensively investigated by using the detrended fluctuation analysis technique and the multiscale entropy method. Firstly, the interevent time series of rating behaviors of these three types of media show similar scaling properties with exponents ranging from 0.53 to 0.58, which implies that the collective behaviors of rating media follow a process embodying self-similarity and long-range correlation. Meanwhile, by dividing the users into three groups based on their activities (i.e., rating per unit time), we find that the scaling exponents of the interevent time series in the three groups are different. Hence, these results suggest that a stronger long-range correlations exist in these collective behaviors. Furthermore, their information complexities vary in the three groups. To explain the differences of the collective behaviors restricted to the three groups, we study the dynamic behavior of human activity at the individual level, and find that the dynamic behaviors of a few users have extremely small scaling exponents associated with long-range anticorrelations. By comparing the interevent time distributions of four representative users, we can find that the bimodal distributions may bring forth the extraordinary scaling behaviors. These results of the analysis of the online human activity in the e-commerce may not only provide insight into its dynamic behaviors but may also be applied to acquire potential economic interest.

  16. Human genetics and sleep behavior.

    Science.gov (United States)

    Shi, Guangsen; Wu, David; Ptáček, Louis J; Fu, Ying-Hui

    2017-06-01

    Why we sleep remains one of the greatest mysteries in science. In the past few years, great advances have been made to better understand this phenomenon. Human genetics has contributed significantly to this movement, as many features of sleep have been found to be heritable. Discoveries about these genetic variations that affect human sleep will aid us in understanding the underlying mechanism of sleep. Here we summarize recent discoveries about the genetic variations affecting the timing of sleep, duration of sleep and EEG patterns. To conclude, we also discuss some of the sleep-related neurological disorders such as Autism Spectrum Disorder (ASD) and Alzheimer's Disease (AD) and the potential challenges and future directions of human genetics in sleep research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Viscoelastic behavior of discrete human collagen fibrils

    DEFF Research Database (Denmark)

    Svensson, Rene; Hassenkam, Tue; P, Hansen

    2010-01-01

    Whole tendon and fibril bundles display viscoelastic behavior, but to the best of our knowledge this property has not been directly measured in single human tendon fibrils. In the present work an atomic force microscopy (AFM) approach was used for tensile testing of two human patellar tendon fibr...

  18. Atypical evening cortisol profile induces visual recognition memory deficit in healthy human subjects

    Directory of Open Access Journals (Sweden)

    Gilpin Heather

    2008-08-01

    Full Text Available Abstract Background Diurnal rhythm-mediated endogenous cortisol levels in humans are characterised by a peak in secretion after awakening that declines throughout the day to an evening trough. However, a significant proportion of the population exhibits an atypical cycle of diurnal cortisol due to shift work, jet-lag, aging, and mental illness. Results The present study has demonstrated a correlation between elevation of cortisol in the evening and deterioration of visual object recognition memory. However, high evening cortisol levels have no effect on spatial memory. Conclusion This study suggests that atypical evening salivary cortisol levels have an important role in the early deterioration of recognition memory. The loss of recognition memory, which is vital for everyday life, is a major symptom of the amnesic syndrome and early stages of Alzheimer's disease. Therefore, this study will promote a potential physiologic marker of early deterioration of recognition memory and a possible diagnostic strategy for Alzheimer's disease.

  19. ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS

    OpenAIRE

    W. Nakanishi; T. Fuse; T. Ishikawa

    2015-01-01

    This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation ...

  20. Dual Recognition of Human Telomeric G-quadruplex by Neomycin-anthraquinone Conjugate

    Science.gov (United States)

    Ranjan, Nihar; Davis, Erik; Xue, Liang

    2013-01-01

    The authors report the recognition of a G-quadruplex formed by four repeat human telomeric DNA with aminosugar intercalator conjugates. The recognition of G-quadruplex through dual binding mode ligands significantly increased the affinity of ligands for G-quadruplex. One such example is a neomycin-anthraquinone 2 which exhibited nanomolar affinity for the quadruplex, and the affinity of 2 is nearly 1000 fold higher for human telomeric G-quadruplex DNA than its constituent units, neomycin and anthraquinone. PMID:23698792

  1. Influence of human behavior on cholera dynamics.

    Science.gov (United States)

    Wang, Xueying; Gao, Daozhou; Wang, Jin

    2015-09-01

    This paper is devoted to studying the impact of human behavior on cholera infection. We start with a cholera ordinary differential equation (ODE) model that incorporates human behavior via modeling disease prevalence dependent contact rates for direct and indirect transmissions and infectious host shedding. Local and global dynamics of the model are analyzed with respect to the basic reproduction number. We then extend the ODE model to a reaction-convection-diffusion partial differential equation (PDE) model that accounts for the movement of both human hosts and bacteria. Particularly, we investigate the cholera spreading speed by analyzing the traveling wave solutions of the PDE model, and disease threshold dynamics by numerically evaluating the basic reproduction number of the PDE model. Our results show that human behavior can reduce (a) the endemic and epidemic levels, (b) cholera spreading speeds and (c) the risk of infection (characterized by the basic reproduction number). Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  2. Human Behavioral Pharmacology, Past, Present, and Future: Symposium Presented at the 50th Annual Meeting of the Behavioral Pharmacology Society

    Science.gov (United States)

    Comer, Sandra D.; Bickel, Warren K.; Yi, Richard; de Wit, Harriet; Higgins, Stephen T.; Wenger, Galen R.; Johanson, Chris-Ellyn; Kreek, Mary Jeanne

    2010-01-01

    A symposium held at the 50th annual meeting of the Behavioral Pharmacology Society in May 2007 reviewed progress in the human behavioral pharmacology of drug abuse. Studies on drug self-administration in humans are reviewed that assessed reinforcing and subjective effects of drugs of abuse. The close parallels observed between studies in humans and laboratory animals using similar behavioral techniques have broadened our understanding of the complex nature of the pharmacological and behavioral factors controlling drug self-administration. The symposium also addressed the role that individual differences, such as gender, personality, and genotype play in determining the extent of self-administration of illicit drugs in human populations. Knowledge of how these factors influence human drug self-administration has helped validate similar differences observed in laboratory animals. In recognition that drug self-administration is but one of many choices available in the lives of humans, the symposium addressed the ways in which choice behavior can be studied in humans. These choice studies in human drug abusers have opened up new and exciting avenues of research in laboratory animals. Finally, the symposium reviewed behavioral pharmacology studies conducted in drug abuse treatment settings and the therapeutic benefits that have emerged from these studies. PMID:20664330

  3. Face recognition: database acquisition, hybrid algorithms, and human studies

    Science.gov (United States)

    Gutta, Srinivas; Huang, Jeffrey R.; Singh, Dig; Wechsler, Harry

    1997-02-01

    One of the most important technologies absent in traditional and emerging frontiers of computing is the management of visual information. Faces are accessible `windows' into the mechanisms that govern our emotional and social lives. The corresponding face recognition tasks considered herein include: (1) Surveillance, (2) CBIR, and (3) CBIR subject to correct ID (`match') displaying specific facial landmarks such as wearing glasses. We developed robust matching (`classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET database. The hybrid classifier architecture consist of an ensemble of connectionist networks--radial basis functions-- and decision trees. The specific characteristics of our hybrid architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, and (b) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds. Experimental results, proving the feasibility of our approach, yield (i) 96% accuracy, using cross validation (CV), for surveillance on a data base consisting of 904 images (ii) 97% accuracy for CBIR tasks, on a database of 1084 images, and (iii) 93% accuracy, using CV, for CBIR subject to correct ID match tasks on a data base of 200 images.

  4. Matrix and Tensor Completion on a Human Activity Recognition Framework.

    Science.gov (United States)

    Savvaki, Sofia; Tsagkatakis, Grigorios; Panousopoulou, Athanasia; Tsakalides, Panagiotis

    2017-11-01

    Sensor-based activity recognition is encountered in innumerable applications of the arena of pervasive healthcare and plays a crucial role in biomedical research. Nonetheless, the frequent situation of unobserved measurements impairs the ability of machine learning algorithms to efficiently extract context from raw streams of data. In this paper, we study the problem of accurate estimation of missing multimodal inertial data and we propose a classification framework that considers the reconstruction of subsampled data during the test phase. We introduce the concept of forming the available data streams into low-rank two-dimensional (2-D) and 3-D Hankel structures, and we exploit data redundancies using sophisticated imputation techniques, namely matrix and tensor completion. Moreover, we examine the impact of reconstruction on the classification performance by experimenting with several state-of-the-art classifiers. The system is evaluated with respect to different data structuring scenarios, the volume of data available for reconstruction, and various levels of missing values per device. Finally, the tradeoff between subsampling accuracy and energy conservation in wearable platforms is examined. Our analysis relies on two public datasets containing inertial data, which extend to numerous activities, multiple sensing parameters, and body locations. The results highlight that robust classification accuracy can be achieved through recovery, even for extremely subsampled data streams.

  5. Human Iris Recognition System using Wavelet Transform and LVQ

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kwan Yong; Lim, Shin Young [Electronics and Telecommunications Research Institute (Korea); Cho, Seong Won [Hongik University (Korea)

    2000-07-01

    The popular methods to check the identity of individuals include passwords and ID cards. These conventional methods for user identification and authentication are not altogether reliable because they can be stolen and forgotten. As an alternative of the existing methods, biometric technology has been paid much attention for the last few decades. In this paper, we propose an efficient system for recognizing the identity of a living person by analyzing iris patterns which have a high level of stability and distinctiveness than other biometric measurements. The proposed system is based on wavelet transform and a competitive neural network with the improved mechanisms. After preprocessing the iris data acquired through a CCD camera, feature vectors are extracted by using Haar wavelet transform. LVQ(Learning Vector Quantization) is exploited to classify these feature vectors. We improve the overall performance of the proposed system by optimizing the size of feature vectors and by introducing an efficient initialization of the weight vectors and a new method for determining the winner in order to increase the recognition accuracy of LVQ. From the experiments, we confirmed that the proposed system has a great potential of being applied to real applications in an efficient and effective way. (author). 14 refs., 13 figs., 7 tabs.

  6. Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

    Directory of Open Access Journals (Sweden)

    Zhaoqin Peng

    2013-01-01

    Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

  7. A triboelectric motion sensor in wearable body sensor network for human activity recognition.

    Science.gov (United States)

    Hui Huang; Xian Li; Ye Sun

    2016-08-01

    The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

  8. Focus-of-attention for human activity recognition from UAVs

    NARCIS (Netherlands)

    Burghouts, G.J.; Eekeren, A.W.M. van; Dijk, J.

    2014-01-01

    This paper presents a system to extract metadata about human activities from full-motion video recorded from a UAV. The pipeline consists of these components: tracking, motion features, representation of the tracks in terms of their motion features, and classification of each track as one of the

  9. Human-Robot Interaction: Intention Recognition and Mutual Entrainment

    Science.gov (United States)

    2012-08-18

    bility, but only the human arm is modeled, with linear, low-pass-filter type transfer functions [16]. The coupled dynamics in pHRI has been intensively ...be located inside or on the edge of the polygon. IV. DISCUSSION A. Issues in Implementing LIPM on a Mobile Robot Instead of focusing on kinesiology

  10. Technological advances for studying human behavior

    Science.gov (United States)

    Roske-Hofstrand, Renate J.

    1990-01-01

    Technological advances for studying human behavior are noted in viewgraph form. It is asserted that performance-aiding systems are proliferating without a fundamental understanding of how they would interact with the humans who must control them. Two views of automation research, the hardware view and the human-centered view, are listed. Other viewgraphs give information on vital elements for human-centered research, a continuum of the research process, available technologies, new technologies for persistent problems, a sample research infrastructure, the need for metrics, and examples of data-link technology.

  11. Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

    Science.gov (United States)

    Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

    2018-02-26

    The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

  12. Recognition of human gait in oblique and frontal views using Kinect ...

    African Journals Online (AJOL)

    This study describes the recognition of human gait in the oblique and frontal views using novel gait features derived from the skeleton joints provided by Kinect. In D-joint, the skeleton joints were extracted directly from the Kinect, which generates the gait feature. On the other hand, H-joint distance is a feature of distance ...

  13. Analysis and recognition of 5 ' UTR intron splice sites in human pre-mRNA

    DEFF Research Database (Denmark)

    Eden, E.; Brunak, Søren

    2004-01-01

    Prediction of splice sites in non-coding regions of genes is one of the most challenging aspects of gene structure recognition. We perform a rigorous analysis of such splice sites embedded in human 5' untranslated regions (UTRs), and investigate correlations between this class of splice sites and...

  14. A connectionist model for the simulation of human spoken-word recognition

    NARCIS (Netherlands)

    Kuijk, D.J. van; Wittenburg, P.; Dijkstra, A.F.J.; Den Brinker, B.P.L.M.; Beek, P.J.; Brand, A.N.; Maarse, F.J.; Mulder, L.J.M.

    1999-01-01

    A new psycholinguistically motivated and neural network base model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons that pass on sensor

  15. An implicit spatiotemporal shape model for human activity localization and recognition

    NARCIS (Netherlands)

    Oikonomopoulos, A.; Patras, I.; Pantic, Maja

    2009-01-01

    In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization

  16. Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

    NARCIS (Netherlands)

    Shoaib, M.; Bosch, S.; Durmaz, O.; Scholten, Johan; Havinga, Paul J.M.

    2016-01-01

    The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such

  17. Compressed sensing method for human activity recognition using tri-axis accelerometer on mobile phone

    Institute of Scientific and Technical Information of China (English)

    Song Hui; Wang Zhongmin

    2017-01-01

    The diversity in the phone placements of different mobile users' dailylife increases the difficulty of recognizing human activities by using mobile phone accelerometer data.To solve this problem,a compressed sensing method to recognize human activities that is based on compressed sensing theory and utilizes both raw mobile phone accelerometer data and phone placement information is proposed.First,an over-complete dictionary matrix is constructed using sufficient raw tri-axis acceleration data labeled with phone placement information.Then,the sparse coefficient is evaluated for the samples that need to be tested by resolving L1 minimization.Finally,residual values are calculated and the minimum value is selected as the indicator to obtain the recognition results.Experimental results show that this method can achieve a recognition accuracy reaching 89.86%,which is higher than that of a recognition method that does not adopt the phone placement information for the recognition process.The recognition accuracy of the proposed method is effective and satisfactory.

  18. EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals

    Directory of Open Access Journals (Sweden)

    Jiaduo Zhao

    2016-01-01

    Full Text Available In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector’s false alarms.

  19. EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

    Science.gov (United States)

    Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

    2016-01-20

    In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms.

  20. Stress and recognition of humans in weanling piglets

    Directory of Open Access Journals (Sweden)

    Renato Irgang

    2007-12-01

    Full Text Available This study was aimed at investigating whether after weaning, piglets recognize persons that have handled them aversively during the lactation period, and whether such treatment intensifies the stress of weaning. Before weaning, five litters received aversive handling treatment involving an aggressive and intimidating voice; six litters were treated conventionally. After weaning, the piglets’ behavior was compared in a series of tests. Compared to day 10 after weaning, in the first two days after weaning higher frequencies of escape attempts, vocalizations, and standing and sitting, accompanied by a lower frequency of feeding (p<0.05, were observed in both treatments. The piglets handled aversively showed a higher frequency of escape attempts, walking, and interaction with other piglets (p<0.05. In a test carried out individually with the piglets of the aversive treatment, an unknown experimenter was able to approach the piglets closer than the aversive experimenter (p<0.001. In a further test, only 36 % of the piglets handled aversively approached the aversive experimenter spontaneously. In contrast, 61 % approached the unknown experimenter spontaneously (p < 0.02. In conclusion, at four to five weeks of age piglets can recognize a person that has handled them aversively during the lactation period. The behavior of piglets at weaning indicates that this management is a significant source of stress and that aversive handling treatment during lactation increases this effect.

  1. A Behavioral Theory of Human Capital Integration

    DEFF Research Database (Denmark)

    Christensen, Jesper

    design in fostering the integration and use of human capital is bounded by individual cognitive limitations that may lead employees to deviate from expected behavior, both individually and in collaboration. The thesis consists of three research papers relying on comprehensive longitudinal project data...... with one another. The overarching contribution of the thesis is to demonstrate, through the combination of psychological and organizational theory, how the ability of firms to properly activate and apply the knowledge held by their employees is fundamentally contingent on the interplay of cognitive...... of a behavioral theory of human capital integration....

  2. How cortical neurons help us see: visual recognition in the human brain

    OpenAIRE

    Blumberg, Julie; Kreiman, Gabriel

    2010-01-01

    Through a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us under...

  3. Research on operation and maintenance support system adaptive to human recognition and understanding in human-centered plant

    International Nuclear Information System (INIS)

    Numano, Masayoshi; Matsuoka, Takeshi; Mitomo, N.

    2004-01-01

    As a human-centered plant, advanced nuclear power plant needs appropriate role sharing between human and mobile intelligent agents. Human-machine cooperation for plant operation and maintenance activities is also required with an advanced interface. Plant's maintenance is programmed using mobile robots working under the radiation environments instead of human beings. Operation and maintenance support system adaptive to human recognition and understanding should be developed to establish adequate human and machine interface so as to induce human capabilities to the full and enable human to take responsibility for plan's operation. Plant's operation and maintenance can be cooperative activities between human and intelligent automonous agents having surveillance and control functions. Infrastructure of multi-agent simulation system for the support system has been investigated and developed based on work plans derived from the scheduler. (T. Tanaka)

  4. Functional inactivation of dorsal medial striatum alters behavioral flexibility and recognition process in mice.

    Science.gov (United States)

    Qiao, Yanhua; Wang, Xingyue; Ma, Lian; Li, Shengguang; Liang, Jing

    2017-10-01

    Deficits in behavioral flexibility and recognition memory are commonly observed in mental illnesses and neurodegenerative diseases. Abnormality of the striatum has been implicated in an association with the pathology of these diseases. However, the exact roles of striatal heterogeneous structures in these cognitive functions are still unknown. In the present study, we investigated the effects of suppressing neuronal activity in the dorsomedial striatum (DMStr) and nucleus accumbens core (NAcC) on reversal learning and novelty recognition in mice. In addition, the locomotor activity, anxiety-like behavior and social interaction were analyzed. Neuronal inactivation was performed by expressing lentivirus-mediated tetanus toxin (TeNT) in the target regions. The results showed that reversal learning was facilitated by neuronal inactivation in the DMStr but not the NAcC, which was attributable to accelerated extinction of acquired strategy but not to impaired memory retention. Furthermore, mice with NAcC inactivation spent more time exploring a novel object than a familiar one, comparable to control mice. In contrast, mice with DMStr inactivation exhibited no preference to a novel environment during the novel object or place recognition test. The DMStr mice also exhibited decreased anxiety level. No phenotypic effect was observed in the locomotion or social interaction in mice with either DMStr or NAcC inactivation. Altogether, these findings suggest that the DMStr but not the ventral area of the striatum plays a crucial role in learning and memory by coordinating spatial exploration as well as mediating information updating. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Human Guidance Behavior Decomposition and Modeling

    Science.gov (United States)

    Feit, Andrew James

    Trained humans are capable of high performance, adaptable, and robust first-person dynamic motion guidance behavior. This behavior is exhibited in a wide variety of activities such as driving, piloting aircraft, skiing, biking, and many others. Human performance in such activities far exceeds the current capability of autonomous systems in terms of adaptability to new tasks, real-time motion planning, robustness, and trading safety for performance. The present work investigates the structure of human dynamic motion guidance that enables these performance qualities. This work uses a first-person experimental framework that presents a driving task to the subject, measuring control inputs, vehicle motion, and operator visual gaze movement. The resulting data is decomposed into subspace segment clusters that form primitive elements of action-perception interactive behavior. Subspace clusters are defined by both agent-environment system dynamic constraints and operator control strategies. A key contribution of this work is to define transitions between subspace cluster segments, or subgoals, as points where the set of active constraints, either system or operator defined, changes. This definition provides necessary conditions to determine transition points for a given task-environment scenario that allow a solution trajectory to be planned from known behavior elements. In addition, human gaze behavior during this task contains predictive behavior elements, indicating that the identified control modes are internally modeled. Based on these ideas, a generative, autonomous guidance framework is introduced that efficiently generates optimal dynamic motion behavior in new tasks. The new subgoal planning algorithm is shown to generate solutions to certain tasks more quickly than existing approaches currently used in robotics.

  6. Human Activity Recognition as Time-Series Analysis

    Directory of Open Access Journals (Sweden)

    Hyesuk Kim

    2015-01-01

    Full Text Available We propose a system that can recognize daily human activities with a Kinect-style depth camera. Our system utilizes a set of view-invariant features and the hidden state conditional random field (HCRF model to recognize human activities from the 3D body pose stream provided by MS Kinect API or OpenNI. Many high-level daily activities can be regarded as having a hierarchical structure where multiple subactivities are performed sequentially or iteratively. In order to model effectively these high-level daily activities, we utilized a multiclass HCRF model, which is a kind of probabilistic graphical models. In addition, in order to get view-invariant, but more informative features, we extract joint angles from the subject’s skeleton model and then perform the feature transformation to obtain three different types of features regarding motion, structure, and hand positions. Through various experiments using two different datasets, KAD-30 and CAD-60, the high performance of our system is verified.

  7. A Generalized Pyramid Matching Kernel for Human Action Recognition in Realistic Videos

    Directory of Open Access Journals (Sweden)

    Wenjun Zhang

    2013-10-01

    Full Text Available Human action recognition is an increasingly important research topic in the fields of video sensing, analysis and understanding. Caused by unconstrained sensing conditions, there exist large intra-class variations and inter-class ambiguities in realistic videos, which hinder the improvement of recognition performance for recent vision-based action recognition systems. In this paper, we propose a generalized pyramid matching kernel (GPMK for recognizing human actions in realistic videos, based on a multi-channel “bag of words” representation constructed from local spatial-temporal features of video clips. As an extension to the spatial-temporal pyramid matching (STPM kernel, the GPMK leverages heterogeneous visual cues in multiple feature descriptor types and spatial-temporal grid granularity levels, to build a valid similarity metric between two video clips for kernel-based classification. Instead of the predefined and fixed weights used in STPM, we present a simple, yet effective, method to compute adaptive channel weights of GPMK based on the kernel target alignment from training data. It incorporates prior knowledge and the data-driven information of different channels in a principled way. The experimental results on three challenging video datasets (i.e., Hollywood2, Youtube and HMDB51 validate the superiority of our GPMK w.r.t. the traditional STPM kernel for realistic human action recognition and outperform the state-of-the-art results in the literature.

  8. Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.

    Science.gov (United States)

    Kheradpisheh, Saeed R; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call "variation level." We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

  9. Humans and deep networks largely agree on which kinds of variation make object recognition harder

    Directory of Open Access Journals (Sweden)

    Saeed Reza Kheradpisheh

    2016-08-01

    Full Text Available View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g. 3D rotations. Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN, which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth as well as their magnitude, which we call variation level. We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier. This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

  10. Deficits in facial emotion recognition indicate behavioral changes and impaired self-awareness after moderate to severe traumatic brain injury.

    Science.gov (United States)

    Spikman, Jacoba M; Milders, Maarten V; Visser-Keizer, Annemarie C; Westerhof-Evers, Herma J; Herben-Dekker, Meike; van der Naalt, Joukje

    2013-01-01

    Traumatic brain injury (TBI) is a leading cause of disability, specifically among younger adults. Behavioral changes are common after moderate to severe TBI and have adverse consequences for social and vocational functioning. It is hypothesized that deficits in social cognition, including facial affect recognition, might underlie these behavioral changes. Measurement of behavioral deficits is complicated, because the rating scales used rely on subjective judgement, often lack specificity and many patients provide unrealistically positive reports of their functioning due to impaired self-awareness. Accordingly, it is important to find performance based tests that allow objective and early identification of these problems. In the present study 51 moderate to severe TBI patients in the sub-acute and chronic stage were assessed with a test for emotion recognition (FEEST) and a questionnaire for behavioral problems (DEX) with a self and proxy rated version. Patients performed worse on the total score and on the negative emotion subscores of the FEEST than a matched group of 31 healthy controls. Patients also exhibited significantly more behavioral problems on both the DEX self and proxy rated version, but proxy ratings revealed more severe problems. No significant correlation was found between FEEST scores and DEX self ratings. However, impaired emotion recognition in the patients, and in particular of Sadness and Anger, was significantly correlated with behavioral problems as rated by proxies and with impaired self-awareness. This is the first study to find these associations, strengthening the proposed recognition of social signals as a condition for adequate social functioning. Hence, deficits in emotion recognition can be conceived as markers for behavioral problems and lack of insight in TBI patients. This finding is also of clinical importance since, unlike behavioral problems, emotion recognition can be objectively measured early after injury, allowing for early

  11. Deficits in facial emotion recognition indicate behavioral changes and impaired self-awareness after moderate to severe traumatic brain injury.

    Directory of Open Access Journals (Sweden)

    Jacoba M Spikman

    Full Text Available Traumatic brain injury (TBI is a leading cause of disability, specifically among younger adults. Behavioral changes are common after moderate to severe TBI and have adverse consequences for social and vocational functioning. It is hypothesized that deficits in social cognition, including facial affect recognition, might underlie these behavioral changes. Measurement of behavioral deficits is complicated, because the rating scales used rely on subjective judgement, often lack specificity and many patients provide unrealistically positive reports of their functioning due to impaired self-awareness. Accordingly, it is important to find performance based tests that allow objective and early identification of these problems. In the present study 51 moderate to severe TBI patients in the sub-acute and chronic stage were assessed with a test for emotion recognition (FEEST and a questionnaire for behavioral problems (DEX with a self and proxy rated version. Patients performed worse on the total score and on the negative emotion subscores of the FEEST than a matched group of 31 healthy controls. Patients also exhibited significantly more behavioral problems on both the DEX self and proxy rated version, but proxy ratings revealed more severe problems. No significant correlation was found between FEEST scores and DEX self ratings. However, impaired emotion recognition in the patients, and in particular of Sadness and Anger, was significantly correlated with behavioral problems as rated by proxies and with impaired self-awareness. This is the first study to find these associations, strengthening the proposed recognition of social signals as a condition for adequate social functioning. Hence, deficits in emotion recognition can be conceived as markers for behavioral problems and lack of insight in TBI patients. This finding is also of clinical importance since, unlike behavioral problems, emotion recognition can be objectively measured early after injury

  12. Integrating Humanism and Behaviorism: Toward Performance

    Science.gov (United States)

    Smith, Darrell

    1974-01-01

    The current emphasis on performance criteria in training programs and in professional services poses a threat to the humanistically oriented helper. This article suggests a behavioral humanism as the desired solution to the dilemma and proposes some guidelines for formulating and implementing such a synthetic system. (Author)

  13. Multi-task learning with group information for human action recognition

    Science.gov (United States)

    Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

    2018-04-01

    Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

  14. Emotional face recognition deficit in amnestic patients with mild cognitive impairment: behavioral and electrophysiological evidence

    Directory of Open Access Journals (Sweden)

    Yang L

    2015-08-01

    Full Text Available Linlin Yang, Xiaochuan Zhao, Lan Wang, Lulu Yu, Mei Song, Xueyi Wang Department of Mental Health, The First Hospital of Hebei Medical University, Hebei Medical University Institute of Mental Health, Shijiazhuang, People’s Republic of China Abstract: Amnestic mild cognitive impairment (MCI has been conceptualized as a transitional stage between healthy aging and Alzheimer’s disease. Thus, understanding emotional face recognition deficit in patients with amnestic MCI could be useful in determining progression of amnestic MCI. The purpose of this study was to investigate the features of emotional face processing in amnestic MCI by using event-related potentials (ERPs. Patients with amnestic MCI and healthy controls performed a face recognition task, giving old/new responses to previously studied and novel faces with different emotional messages as the stimulus material. Using the learning-recognition paradigm, the experiments were divided into two steps, ie, a learning phase and a test phase. ERPs were analyzed on electroencephalographic recordings. The behavior data indicated high emotion classification accuracy for patients with amnestic MCI and for healthy controls. The mean percentage of correct classifications was 81.19% for patients with amnestic MCI and 96.46% for controls. Our ERP data suggest that patients with amnestic MCI were still be able to undertake personalizing processing for negative faces, but not for neutral or positive faces, in the early frontal processing stage. In the early time window, no differences in frontal old/new effect were found between patients with amnestic MCI and normal controls. However, in the late time window, the three types of stimuli did not elicit any old/new parietal effects in patients with amnestic MCI, suggesting their recollection was impaired. This impairment may be closely associated with amnestic MCI disease. We conclude from our data that face recognition processing and emotional memory is

  15. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  16. Research opportunities in human behavior and performance

    Science.gov (United States)

    Christensen, J. M. (Editor); Talbot, J. M. (Editor)

    1985-01-01

    Extant information on the subject of psychological aspects of manned space flight are reviewed; NASA's psychology research program is examined; significant gaps in knowledge are identified; and suggestions are offered for future research program planning. Issues of human behavior and performance related to the United States space station, to the space shuttle program, and to both near and long term problems of a generic nature in applicable disciplines of psychology are considered. Topics covered include: (1) human performance requirements for a 90 day mission; (2) human perceptual, cognitive, and motor capabilities and limitations in space; (3) crew composition, individual competencies, crew competencies, selection criteria, and special training; (4) environmental factors influencing behavior; (5) psychosocial aspects of multiperson space crews in long term missions; (6) career determinants in NASA; (7) investigational methodology and equipment; and (8) psychological support.

  17. Human behavioral corollary on industrial workplace

    International Nuclear Information System (INIS)

    Fazil, I.; Kirmani, Z.U.; Hanif, M.; Saeed, A.; Khurshid, A.

    2009-01-01

    This paper highlights a number of initiatives taken for the introduction of behavior-based safety concepts and customized process control solutions to encourage and instill safe behavior in employees at Attock Refinery Limited (ARL), Morgah Rawalpindi, Pakistan. A Safety culture is entirely dependent on the attitude of employees towards safety. After all, those who actually perform the work are responsible for their safety as well as that of those around them, and also for any accident that occurs whilst they work. In 2005, ARL established a Health Safety Environment (HSE) Department reporting directly to the CEO and it now stands transformed into the HSEQ Department with Quality having been added to its portfolio, with the logic that it is the Quality of our systems and processes that also determines the possibility or otherwise of safe/unsafe behavior. The need was felt to measure, analyze and then control unsafe behavior at the workplace. In spite of providing safety systems and necessary hardware, incident data shows that the majority of misfortunes are triggered by employees' unsafe attitude, proclivity to take shortcuts and intuitive-based decisions, bypassing Standard Operating Procedures (SOPs). Human behavior is a very complex subject as it is linked not only to the workplace environment but has origins from home and upbringing as well. An attempt was, nevertheless, necessary to develop a tool of customized behavioral assessment tool in order to gauge the employees' behavior. On a scale of 1-100, marks were allocated to areas including safety attitude within the department(s), working conditions, supervisor's behavior towards worker safety, job loyalty, personal attitude towards job safety, seriousness towards safety, training and the employees' view about the HSEQ department. This study, based on one-on-one interviews with employees, yielded what we will term employees' potential towards unsafe behaviors, which would facilitate subsequent planning and

  18. Deficits in Facial Emotion Recognition Indicate Behavioral Changes and Impaired Self-Awareness after Moderate to Severe Traumatic Brain Injury

    OpenAIRE

    Spikman, Jacoba M.; Milders, Maarten V.; Visser-Keizer, Annemarie C.; Westerhof-Evers, Herma J.; Herben-Dekker, Meike; van der Naalt, Joukje

    2013-01-01

    Traumatic brain injury (TBI) is a leading cause of disability, specifically among younger adults. Behavioral changes are common after moderate to severe TBI and have adverse consequences for social and vocational functioning. It is hypothesized that deficits in social cognition, including facial affect recognition, might underlie these behavioral changes. Measurement of behavioral deficits is complicated, because the rating scales used rely on subjective judgement, often lack specificity and ...

  19. Synergistic Effects of Human Milk Nutrients in the Support of Infant Recognition Memory: An Observational Study

    Directory of Open Access Journals (Sweden)

    Carol L. Cheatham

    2015-11-01

    Full Text Available The aim was to explore the relation of human milk lutein; choline; and docosahexaenoic acid (DHA with recognition memory abilities of six-month-olds. Milk samples obtained three to four months postpartum were analyzed for fatty acids, lutein, and choline. At six months, participants were invited to an electrophysiology session. Recognition memory was tested with a 70–30 oddball paradigm in a high-density 128-lead event-related potential (ERP paradigm. Complete data were available for 55 participants. Data were averaged at six groupings (Frontal Right; Frontal Central; Frontal Left; Central; Midline; and Parietal for latency to peak, peak amplitude, and mean amplitude. Difference scores were calculated as familiar minus novel. Final regression models revealed the lutein X free choline interaction was significant for the difference in latency scores at frontal and central areas (p < 0.05 and p < 0.001; respectively. Higher choline levels with higher lutein levels were related to better recognition memory. The DHA X free choline interaction was also significant for the difference in latency scores at frontal, central, and midline areas (p < 0.01; p < 0.001; p < 0.05 respectively. Higher choline with higher DHA was related to better recognition memory. Interactions between human milk nutrients appear important in predicting infant cognition, and there may be a benefit to specific nutrient combinations.

  20. Human Skeleton Model Based Dynamic Features for Walking Speed Invariant Gait Recognition

    Directory of Open Access Journals (Sweden)

    Jure Kovač

    2014-01-01

    Full Text Available Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometrics can be captured at public places from a distance without subject's collaboration, awareness, and even consent. Although current approaches give encouraging results, we are still far from effective use in real-life applications. In general, methods set various constraints to circumvent the influence of covariate factors like changes of walking speed, view, clothing, footwear, and object carrying, that have negative impact on recognition performance. In this paper we propose a skeleton model based gait recognition system focusing on modelling gait dynamics and eliminating the influence of subjects appearance on recognition. Furthermore, we tackle the problem of walking speed variation and propose space transformation and feature fusion that mitigates its influence on recognition performance. With the evaluation on OU-ISIR gait dataset, we demonstrate state of the art performance of proposed methods.

  1. Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

    Science.gov (United States)

    2018-01-01

    Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

  2. Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

    Directory of Open Access Journals (Sweden)

    Alejandro Baldominos

    2018-04-01

    Full Text Available Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

  3. Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

    Science.gov (United States)

    Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

    2018-04-23

    Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

  4. Modeling guidance and recognition in categorical search: bridging human and computer object detection.

    Science.gov (United States)

    Zelinsky, Gregory J; Peng, Yifan; Berg, Alexander C; Samaras, Dimitris

    2013-10-08

    Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery.

  5. Entamoeba Clone-Recognition Experiments: Morphometrics, Aggregative Behavior, and Cell-Signaling Characterization.

    Science.gov (United States)

    Espinosa, Avelina; Paz-Y-Miño-C, Guillermo; Hackey, Meagan; Rutherford, Scott

    2016-05-01

    Studies on clone- and kin-discrimination in protists have proliferated during the past decade. We report clone-recognition experiments in seven Entamoeba lineages (E. invadens IP-1, E. invadens VK-1:NS, E. terrapinae, E. moshkovskii Laredo, E. moshkovskii Snake, E. histolytica HM-1:IMSS and E. dispar). First, we characterized morphometrically each clone (length, width, and cell-surface area) and documented how they differed statistically from one another (as per single-variable or canonical-discriminant analyses). Second, we demonstrated that amebas themselves could discriminate self (clone) from different (themselves vs. other clones). In mix-cell-line cultures between closely-related (E. invadens IP-1 vs. E. invadens VK-1:NS) or distant-phylogenetic clones (E. terrapinae vs. E. moshkovskii Laredo), amebas consistently aggregated with same-clone members. Third, we identified six putative cell-signals secreted by the amebas (RasGap/Ankyrin, coronin-WD40, actin, protein kinases, heat shock 70, and ubiquitin) and which known functions in Entamoeba spp. included: cell proliferation, cell adhesion, cell movement, and stress-induced encystation. To our knowledge, this is the first multi-clone characterization of Entamoeba spp. morphometrics, aggregative behavior, and cell-signaling secretion in the context of clone-recognition. Protists allow us to study cell-cell recognition from ecological and evolutionary perspectives. Modern protistan lineages can be central to studies about the origins and evolution of multicellularity. © 2016 The Author(s) Journal of Eukaryotic Microbiology © 2016 International Society of Protistologists.

  6. Megascale processes: Natural disasters and human behavior

    Science.gov (United States)

    Kieffer, S.W.; Barton, P.; Chesworth, W.; Palmer, A.R.; Reitan, P.; Zen, E.-A.

    2009-01-01

    Megascale geologic processes, such as earthquakes, tsunamis, volcanic eruptions, floods, and meteoritic impacts have occurred intermittently throughout geologic time, and perhaps on several planets. Unlike other catastrophes discussed in this volume, a unique process is unfolding on Earth, one in which humans may be the driving agent of megadisasters. Although local effects on population clusters may have been catastrophic in the past, human societies have never been interconnected globally at the scale that currently exists. We review some megascale processes and their effects in the past, and compare present conditions and possible outcomes. We then propose that human behavior itself is having effects on the planet that are comparable to, or greater than, these natural disasters. Yet, unlike geologic processes, human behavior is potentially under our control. Because the effects of our behavior threaten the stability, or perhaps even existence, of a civilized society, we call for the creation of a body to institute coherent global, credible, scientifi cally based action that is sensitive to political, economic, religious, and cultural values. The goal would be to institute aggressive monitoring, identify and understand trends, predict their consequences, and suggest and evaluate alternative actions to attempt to rescue ourselves and our ecosystems from catastrophe. We provide a template modeled after several existing national and international bodies. ?? 2009 The Geological Society of America.

  7. Individual recognition and odor in rat-like hamsters: behavioral responses and chemical properties.

    Science.gov (United States)

    Liu, Dingzhen; Huang, Ke-Jian; Zhang, Jian-Xu

    2011-11-01

    Individual recognition has been studied across a number of taxa and modalities; however, few attempts have been made to combine chemical and biological approaches and arrive at a more complete understanding of the use of secretions as signals. We combined behavioral habituation experiments with gas chromatography-mass spectrometry of glandular secretions from the left and right flank gland and midventral gland of the rat-like hamster, Tscheskia triton. We found that females became habituated to one scent and then could discriminate individuals via another scent source from the same individual only when familiar with the scent donor. However, this prior social interaction was not required for females to discriminate different individuals in single-stimulus habituation-dishabituation tests. Chemical analyses revealed a similarity in volatile compounds between the left and right flank gland and midventral gland scents. It appears that individually distinctive cues are integratively coded by a combination of both flank gland and midventral gland secretions, instead of a single scent, albeit animals show different preferences to the novel scent. Our results suggest that odors from the flank and midventral glands may provide information related to individuality and aid individual recognition in this species and confirm that prior interaction between individuals is a prerequisite for rat-like hamsters to form multi-odor memory of a particular conspecific.

  8. Psilocybin biases facial recognition, goal-directed behavior, and mood state toward positive relative to negative emotions through different serotonergic subreceptors.

    Science.gov (United States)

    Kometer, Michael; Schmidt, André; Bachmann, Rosilla; Studerus, Erich; Seifritz, Erich; Vollenweider, Franz X

    2012-12-01

    Serotonin (5-HT) 1A and 2A receptors have been associated with dysfunctional emotional processing biases in mood disorders. These receptors further predominantly mediate the subjective and behavioral effects of psilocybin and might be important for its recently suggested antidepressive effects. However, the effect of psilocybin on emotional processing biases and the specific contribution of 5-HT2A receptors across different emotional domains is unknown. In a randomized, double-blind study, 17 healthy human subjects received on 4 separate days placebo, psilocybin (215 μg/kg), the preferential 5-HT2A antagonist ketanserin (50 mg), or psilocybin plus ketanserin. Mood states were assessed by self-report ratings, and behavioral and event-related potential measurements were used to quantify facial emotional recognition and goal-directed behavior toward emotional cues. Psilocybin enhanced positive mood and attenuated recognition of negative facial expression. Furthermore, psilocybin increased goal-directed behavior toward positive compared with negative cues, facilitated positive but inhibited negative sequential emotional effects, and valence-dependently attenuated the P300 component. Ketanserin alone had no effects but blocked the psilocybin-induced mood enhancement and decreased recognition of negative facial expression. This study shows that psilocybin shifts the emotional bias across various psychological domains and that activation of 5-HT2A receptors is central in mood regulation and emotional face recognition in healthy subjects. These findings may not only have implications for the pathophysiology of dysfunctional emotional biases but may also provide a framework to delineate the mechanisms underlying psylocybin's putative antidepressant effects. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  9. Multimedia Content Development as a Facial Expression Datasets for Recognition of Human Emotions

    Science.gov (United States)

    Mamonto, N. E.; Maulana, H.; Liliana, D. Y.; Basaruddin, T.

    2018-02-01

    Datasets that have been developed before contain facial expression from foreign people. The development of multimedia content aims to answer the problems experienced by the research team and other researchers who will conduct similar research. The method used in the development of multimedia content as facial expression datasets for human emotion recognition is the Villamil-Molina version of the multimedia development method. Multimedia content developed with 10 subjects or talents with each talent performing 3 shots with each capturing talent having to demonstrate 19 facial expressions. After the process of editing and rendering, tests are carried out with the conclusion that the multimedia content can be used as a facial expression dataset for recognition of human emotions.

  10. The re-appreciation of the humanities in contemporary philosophy of science: From recognition to exaggeration?

    Directory of Open Access Journals (Sweden)

    Renato Coletto

    2013-06-01

    Full Text Available In the course of the centuries, the ‘reputation’ and status attributed to the humanities underwent different phases. One of their lowest moments can be traced during the positivist period. This article explored the reasons underlying the gradual re-evaluation of the scientific status and relevance of the humanities in the philosophy of science of the 20th century. On the basis of a historical analysis it was argued that on the one hand such recognition is positive because it abolishes an unjustified prejudice that restricted the status of ‘science’ to the natural sciences. On the other hand it was argued that the reasons behind such recognition might not always be sound and may be inspired by (and lead to a certain relativism harbouring undesired consequences. In the final part of this article (dedicated to Prof. J.J. [Ponti] Venter a brief ‘postscript’ sketched his evaluation of the role of philosophy.

  11. The Developmental Lexicon Project: A behavioral database to investigate visual word recognition across the lifespan.

    Science.gov (United States)

    Schröter, Pauline; Schroeder, Sascha

    2017-12-01

    With the Developmental Lexicon Project (DeveL), we present a large-scale study that was conducted to collect data on visual word recognition in German across the lifespan. A total of 800 children from Grades 1 to 6, as well as two groups of younger and older adults, participated in the study and completed a lexical decision and a naming task. We provide a database for 1,152 German words, comprising behavioral data from seven different stages of reading development, along with sublexical and lexical characteristics for all stimuli. The present article describes our motivation for this project, explains the methods we used to collect the data, and reports analyses on the reliability of our results. In addition, we explored developmental changes in three marker effects in psycholinguistic research: word length, word frequency, and orthographic similarity. The database is available online.

  12. Viscoelastic behavior of discrete human collagen fibrils

    DEFF Research Database (Denmark)

    Svensson, René; Hassenkam, Tue; Hansen, Philip

    2010-01-01

    Whole tendon and fibril bundles display viscoelastic behavior, but to the best of our knowledge this property has not been directly measured in single human tendon fibrils. In the present work an atomic force microscopy (AFM) approach was used for tensile testing of two human patellar tendon...... saline, cyclic testing was performed in the pre-yield region at different strain rates, and the elastic response was determined by a stepwise stress relaxation test. The elastic stress-strain response corresponded to a second-order polynomial fit, while the viscous response showed a linear dependence...

  13. Meta-Analysis of Facial Emotion Recognition in Behavioral Variant Frontotemporal Dementia: Comparison With Alzheimer Disease and Healthy Controls.

    Science.gov (United States)

    Bora, Emre; Velakoulis, Dennis; Walterfang, Mark

    2016-07-01

    Behavioral disturbances and lack of empathy are distinctive clinical features of behavioral variant frontotemporal dementia (bvFTD) in comparison to Alzheimer disease (AD). The aim of this meta-analytic review was to compare facial emotion recognition performances of bvFTD with healthy controls and AD. The current meta-analysis included a total of 19 studies and involved comparisons of 288 individuals with bvFTD and 329 healthy controls and 162 bvFTD and 147 patients with AD. Facial emotion recognition was significantly impaired in bvFTD in comparison to the healthy controls (d = 1.81) and AD (d = 1.23). In bvFTD, recognition of negative emotions, especially anger (d = 1.48) and disgust (d = 1.41), were severely impaired. Emotion recognition was significantly impaired in bvFTD in comparison to AD in all emotions other than happiness. Impairment of emotion recognition is a relatively specific feature of bvFTD. Routine assessment of social-cognitive abilities including emotion recognition can be helpful in better differentiating between cortical dementias such as bvFTD and AD. © The Author(s) 2016.

  14. Human gait recognition by pyramid of HOG feature on silhouette images

    Science.gov (United States)

    Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

    2013-03-01

    As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

  15. Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

    Science.gov (United States)

    Liu, Jun; Wang, Gang; Duan, Ling-Yu; Abdiyeva, Kamila; Kot, Alex C.

    2018-04-01

    Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.

  16. Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

    Science.gov (United States)

    Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

    2017-07-03

    Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

  17. A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

    Science.gov (United States)

    Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

    2016-01-01

    The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

  18. A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

    Directory of Open Access Journals (Sweden)

    Enea Cippitelli

    2016-01-01

    Full Text Available The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

  19. How cortical neurons help us see: visual recognition in the human brain

    Science.gov (United States)

    Blumberg, Julie; Kreiman, Gabriel

    2010-01-01

    Through a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us understand the computations performed by visual cortex. PMID:20811161

  20. Energy and Environmental Drivers of Stress and Conflict in Multi scale Models of Human Social Behavior

    Science.gov (United States)

    2017-10-31

    resolved by the recognition that cities are first and foremost self- organizing social networks embedded in space and enabled by urban infrastructure and...AUTHORS 7. PERFORMING ORGANIZATION NAMES AND ADDRESSES 15. SUBJECT TERMS b. ABSTRACT 2. REPORT TYPE 17. LIMITATION OF ABSTRACT 15. NUMBER OF PAGES 5d...Report: Energy and Environmental Drivers of Stress and Conflict in Multi-scale Models of Human Social Behavior The views, opinions and/or findings

  1. Recognition of lyso-phospholipids by human natural killer T lymphocytes.

    Directory of Open Access Journals (Sweden)

    Lisa M Fox

    2009-10-01

    Full Text Available Natural killer T (NKT cells are a subset of T lymphocytes with potent immunoregulatory properties. Recognition of self-antigens presented by CD1d molecules is an important route of NKT cell activation; however, the molecular identity of specific autoantigens that stimulate human NKT cells remains unclear. Here, we have analyzed human NKT cell recognition of CD1d cellular ligands. The most clearly antigenic species was lyso-phosphatidylcholine (LPC. Diacylated phosphatidylcholine and lyso-phosphoglycerols differing in the chemistry of the head group stimulated only weak responses from human NKT cells. However, lyso-sphingomyelin, which shares the phosphocholine head group of LPC, also activated NKT cells. Antigen-presenting cells pulsed with LPC were capable of stimulating increased cytokine responses by NKT cell clones and by freshly isolated peripheral blood lymphocytes. These results demonstrate that human NKT cells recognize cholinated lyso-phospholipids as antigens presented by CD1d. Since these lyso-phospholipids serve as lipid messengers in normal physiological processes and are present at elevated levels during inflammatory responses, these findings point to a novel link between NKT cells and cellular signaling pathways that are associated with human disease pathophysiology.

  2. Two-Stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction

    Directory of Open Access Journals (Sweden)

    Nhan Nguyen-Duc-Thanh

    2012-07-01

    Full Text Available Hidden Markov Model (HMM is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human-Robot Interaction (HRI. In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2-stages Hidden Markov Model. The 1st HMM is to recognize the prime command-like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.

  3. Automatic Human Facial Expression Recognition Based on Integrated Classifier From Monocular Video with Uncalibrated Camera

    Directory of Open Access Journals (Sweden)

    Yu Tao

    2017-01-01

    Full Text Available An automatic recognition framework for human facial expressions from a monocular video with an uncalibrated camera is proposed. The expression characteristics are first acquired from a kind of deformable template, similar to a facial muscle distribution. After associated regularization, the time sequences from the trait changes in space-time under complete expressional production are then arranged line by line in a matrix. Next, the matrix dimensionality is reduced by a method of manifold learning of neighborhood-preserving embedding. Finally, the refined matrix containing the expression trait information is recognized by a classifier that integrates the hidden conditional random field (HCRF and support vector machine (SVM. In an experiment using the Cohn–Kanade database, the proposed method showed a comparatively higher recognition rate than the individual HCRF or SVM methods in direct recognition from two-dimensional human face traits. Moreover, the proposed method was shown to be more robust than the typical Kotsia method because the former contains more structural characteristics of the data to be classified in space-time

  4. Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    Shirui Huo

    2017-01-01

    Full Text Available Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs and extracting deep convolutional neural network (DCNN features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

  5. A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Majid Janidarmian

    2017-03-01

    Full Text Available Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

  6. Energy-Efficient Real-Time Human Activity Recognition on Smart Mobile Devices

    Directory of Open Access Journals (Sweden)

    Jin Lee

    2016-01-01

    Full Text Available Nowadays, human activity recognition (HAR plays an important role in wellness-care and context-aware systems. Human activities can be recognized in real-time by using sensory data collected from various sensors built in smart mobile devices. Recent studies have focused on HAR that is solely based on triaxial accelerometers, which is the most energy-efficient approach. However, such HAR approaches are still energy-inefficient because the accelerometer is required to run without stopping so that the physical activity of a user can be recognized in real-time. In this paper, we propose a novel approach for HAR process that controls the activity recognition duration for energy-efficient HAR. We investigated the impact of varying the acceleration-sampling frequency and window size for HAR by using the variable activity recognition duration (VARD strategy. We implemented our approach by using an Android platform and evaluated its performance in terms of energy efficiency and accuracy. The experimental results showed that our approach reduced energy consumption by a minimum of about 44.23% and maximum of about 78.85% compared to conventional HAR without sacrificing accuracy.

  7. A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.

    Science.gov (United States)

    Janidarmian, Majid; Roshan Fekr, Atena; Radecka, Katarzyna; Zilic, Zeljko

    2017-03-07

    Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

  8. A developmental change of the visual behavior of the face recognition in the early infancy.

    Science.gov (United States)

    Konishi, Yukihiko; Okubo, Kensuke; Kato, Ikuko; Ijichi, Sonoko; Nishida, Tomoko; Kusaka, Takashi; Isobe, Kenichi; Itoh, Susumu; Kato, Masaharu; Konishi, Yukuo

    2012-10-01

    The purpose of this study was to examine developmental changes in visuocognitive function, particularly face recognition, in early infancy. In this study, we measured eye movement in healthy infants with a preference gaze problem, particularly eye movement between two face stimulations. We used the eye tracker system (Tobii1750, Tobii Technologies, Sweden) to measure eye movement in infants. Subjects were 17 3-month-old infants and 16 4-month-old infants. The subjects looked two types of face stimulation (upright face/scrambled face) at the same time and we measured their visual behavior (preference/looking/eye movement). Our results showed that 4-month-old infants looked at an upright face longer than 3-month infants, and exploratory behavior while comparing two face stimulations significantly increased. In this study, 4-month-old infants showed a preference towards an upright face. The numbers of eye movements between two face stimuli significantly increased in 4-month-old infants. These results suggest that eye movements may be an important index in face cognitive function during early infancy. Copyright © 2012 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  9. Law enforcers recognition level emerging threats based on physical appearance and behavior signs the enemy

    Directory of Open Access Journals (Sweden)

    R.M. Radzievskiy

    2015-02-01

    Full Text Available Purpose: examine the effectiveness of the training method of differential approach to the choice of means of influence on the action of law enforcers opponent with different levels of aggressiveness. Material : the experiment involved 15 students of the Kyiv National Academy of Internal Affairs and the 15 employees of the State Guard of Ukraine. Results : presented curriculum for special physical and tactical training. The program details the conceptual apparatus of THREATS and DANGERS manifestations of different levels of aggressiveness opponent (case analysis of its motor behavior. The study participants underwent 7 day course focused training. The basis of the course is an advanced theoretical base. The base is aimed at developing knowledge and skills of employees in determining the level of danger. Including threats from testing and modeling episodes of extreme situations the options cadets. Conclusions : In the simulated collision situations with aggressive opponent to the students significantly improved the adequacy of the response to the threat of execution time and within the legal grounds. Recognition was determined by the level of aggressiveness manifest manners enemy, his emotions, motivation, motor behavior, positional arrangement for 2 - 3 seconds. The program contributed to the development of qualities: attention, orientation, perception, motor lead.

  10. A preliminary analysis of human factors affecting the recognition accuracy of a discrete word recognizer for C3 systems

    Science.gov (United States)

    Yellen, H. W.

    1983-03-01

    Literature pertaining to Voice Recognition abounds with information relevant to the assessment of transitory speech recognition devices. In the past, engineering requirements have dictated the path this technology followed. But, other factors do exist that influence recognition accuracy. This thesis explores the impact of Human Factors on the successful recognition of speech, principally addressing the differences or variability among users. A Threshold Technology T-600 was used for a 100 utterance vocubalary to test 44 subjects. A statistical analysis was conducted on 5 generic categories of Human Factors: Occupational, Operational, Psychological, Physiological and Personal. How the equipment is trained and the experience level of the speaker were found to be key characteristics influencing recognition accuracy. To a lesser extent computer experience, time or week, accent, vital capacity and rate of air flow, speaker cooperativeness and anxiety were found to affect overall error rates.

  11. Nanoindentation creep behavior of human enamel.

    Science.gov (United States)

    He, Li-Hong; Swain, Michael V

    2009-11-01

    In this study, the indentation creep behavior of human enamel was investigated with a nanoindentation system and a Berkovich indenter at a force of 250 mN with one-step loading and unloading method. A constant hold period of 900 s was incorporated into each test at the maximum load as well at 5 mN minimum load during unloading. The indentation creep at the maximum load and creep recovery at the minimum load was described with a double exponential function and compared with other classic viscoelastic models (Debye/Maxwell and Kohlrausch-Williams-Watts). Indentation creep rate sensitivity, m, of human enamel was measured for the first time with a value of approximately 0.012. Enamel displayed both viscoelastic and viscoplastic behavior similar to that of bone. These results indicate that, associated with entrapment of particulates between teeth under functional loading and sliding wear conditions, the enamel may inelastically deform but recover upon its release. This behavior may be important in explaining the excellent wear resistance, antifatigue, and crack resistant abilities of natural tooth structure. (c) 2008 Wiley Periodicals, Inc.

  12. reCAPTCHA: human-based character recognition via Web security measures.

    Science.gov (United States)

    von Ahn, Luis; Maurer, Benjamin; McMillen, Colin; Abraham, David; Blum, Manuel

    2008-09-12

    CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words.

  13. Deep--deeper--deepest? Encoding strategies and the recognition of human faces.

    Science.gov (United States)

    Sporer, S L

    1991-03-01

    Various encoding strategies that supposedly promote deeper processing of human faces (e.g., character judgments) have led to better recognition than more shallow processing tasks (judging the width of the nose). However, does deeper processing actually lead to an improvement in recognition, or, conversely, does shallow processing lead to a deterioration in performance when compared with naturally employed encoding strategies? Three experiments systematically compared a total of 8 different encoding strategies manipulating depth of processing, amount of elaboration, and self-generation of judgmental categories. All strategies that required a scanning of the whole face were basically equivalent but no better than natural strategy controls. The consistently worst groups were the ones that rated faces along preselected physical dimensions. This can be explained by subjects' lesser task involvement as revealed by manipulation checks.

  14. Modeling the exergy behavior of human body

    International Nuclear Information System (INIS)

    Keutenedjian Mady, Carlos Eduardo; Silva Ferreira, Maurício; Itizo Yanagihara, Jurandir; Hilário Nascimento Saldiva, Paulo; Oliveira Junior, Silvio de

    2012-01-01

    Exergy analysis is applied to assess the energy conversion processes that take place in the human body, aiming at developing indicators of health and performance based on the concepts of exergy destroyed rate and exergy efficiency. The thermal behavior of the human body is simulated by a model composed of 15 cylinders with elliptical cross section representing: head, neck, trunk, arms, forearms, hands, thighs, legs, and feet. For each, a combination of tissues is considered. The energy equation is solved for each cylinder, being possible to obtain transitory response from the body due to a variation in environmental conditions. With this model, it is possible to obtain heat and mass flow rates to the environment due to radiation, convection, evaporation and respiration. The exergy balances provide the exergy variation due to heat and mass exchange over the body, and the exergy variation over time for each compartments tissue and blood, the sum of which leads to the total variation of the body. Results indicate that exergy destroyed and exergy efficiency decrease over lifespan and the human body is more efficient and destroys less exergy in lower relative humidities and higher temperatures. -- Highlights: ► In this article it is indicated an overview of the human thermal model. ► It is performed the energy and exergy analysis of the human body. ► Exergy destruction and exergy efficiency decreases with lifespan. ► Exergy destruction and exergy efficiency are a function of environmental conditions.

  15. Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

    Science.gov (United States)

    Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

    2015-01-01

    Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

  16. Behavior genetic modeling of human fertility

    DEFF Research Database (Denmark)

    Rodgers, J L; Kohler, H P; Kyvik, K O

    2001-01-01

    Behavior genetic designs and analysis can be used to address issues of central importance to demography. We use this methodology to document genetic influence on human fertility. Our data come from Danish twin pairs born from 1953 to 1959, measured on age at first attempt to get pregnant (First......Try) and number of children (NumCh). Behavior genetic models were fitted using structural equation modeling and DF analysis. A consistent medium-level additive genetic influence was found for NumCh, equal across genders; a stronger genetic influence was identified for FirstTry, greater for females than for males....... A bivariate analysis indicated significant shared genetic variance between NumCh and FirstTry....

  17. Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

    Science.gov (United States)

    Nakanishi, W.; Fuse, T.; Ishikawa, T.

    2015-05-01

    This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

  18. A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Allah Bux Sargano

    2017-01-01

    Full Text Available Human activity recognition (HAR is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

  19. Problems of Face Recognition in Patients with Behavioral Variant Frontotemporal Dementia

    OpenAIRE

    Chandra, Sadanandavalli Retnaswami; Patwardhan, Ketaki; Pai, Anupama Ramakanth

    2017-01-01

    Introduction: Faces are very special as they are most essential for social cognition in humans. It is partly understood that face processing in its abstractness involves several extra striate areas. One of the most important causes for caregiver suffering in patients with anterior dementia is lack of empathy. This apart from being a behavioral disorder could be also due to failure to categorize the emotions of the people around them. Patients and Methods: Inlusion criteria: DSM IV for Bv FTD ...

  20. Understanding human behavior in times of war.

    Science.gov (United States)

    Vetter, Stefan

    2007-12-01

    The Third Geneva Convention reflects on the values of humanism, declaring the rights of humaneness, honor, and protection before torture and final discharge of war prisoners after the end of a war. These days, the occurrences in Baghdad Central Detention Center (formerly known as Abu Ghraib Prison), the actions of British soldiers in Basra, and the inflamed public discussion of whether torture might be an appropriate method to obtain crucial information from terrorists put the Third Geneva Convention back in the spotlight. The aforementioned occurrences raise questions regarding the psychological mass phenomena that make us vulnerable to think and to act against our education, habits, and beliefs. Only an understanding of these phenomena will help us to act against behavior we condemn. This article is an attempt to show how cognition of societies and individuals slowly changes during longer conflicts. Furthermore, it tries to summarize the possibilities we have to confront these tendencies.

  1. Loss of molars early in life develops behavioral lateralization and impairs hippocampus-dependent recognition memory.

    Science.gov (United States)

    Kawahata, Masatsuna; Ono, Yumie; Ohno, Akinori; Kawamoto, Shoichi; Kimoto, Katsuhiko; Onozuka, Minoru

    2014-01-04

    Using senescence-accelerated mouse prone 8 (SAMP8), we examined whether reduced mastication from a young age affects hippocampal-dependent cognitive function. We anesthetized male SAMP8 mice at 8 weeks of age and extracted all maxillary molar teeth of half the animals. The other animals were treated similarly, except that molar teeth were not extracted. At 12 and 24 weeks of age, their general behavior and their ability to recognize novel objects were tested using the open-field test (OFT) and the object-recognition test (ORT), respectively. The body weight of molarless mice was reduced significantly compared to that of molar-intact mice after the extraction and did not recover to the weight of age-matched molar-intact mice throughout the experimental period. At 12 weeks of age, molarless mice showed significantly greater locomotor activity in the OFT than molar-intact mice. However, the ability of molarless mice to discriminate a novel object in the ORT was impaired compared to that of molar-intact mice. The ability of both molarless and molar-intact SAMP8 mice to recognize objects was impaired at 24 weeks of age. These results suggest that molarless SAMP8 mice develop a deficit of cognitive function earlier than molar-intact SAMP8 mice. Interestingly, both at 12 and 24 weeks of age, molarless mice showed a lateralized preference of object location in the encoding session of the ORT, in which two identical objects were presented. Their lateralized preference of object location was positively correlated with the rightward turning-direction preference, which reached statistical significance at 24 weeks of age. Loss of masticatory function in early life causes malnutrition and chronic stress and impairs the ability to recognize novel objects. Hyperactivation and lateralized rotational behavior are commonly observed with dysfunction of the dopaminergic system, therefore, reduced masticatory function may deplete the mesolimbic and mesocorticolimbic dopaminergic systems

  2. To fight or mate? Hormonal control of sex recognition, male sexual behavior and aggression in the gecko lizard.

    Science.gov (United States)

    Schořálková, Tereza; Kratochvíl, Lukáš; Kubička, Lukáš

    2018-01-01

    Squamate reptiles are a highly diversified vertebrate group with extensive variability in social behavior and sexual dimorphism. However, hormonal control of these traits has not previously been investigated in sufficient depth in many squamate lineages. Here, we studied the hormonal control of male sexual behavior, aggressiveness, copulatory organ (hemipenis) size and sex recognition in the gecko Paroedura picta, comparing ovariectomized females, ovariectomized females treated with exogenous dihydrotestosterone (DHT), ovariectomized females treated with exogenous testosterone (T), control females and males. The administration of both T and DHT led to the expression of male-typical sexual behavior in females. However, in contrast to T, increased circulating levels of DHT alone were not enough to initiate the full expression of male-typical offensive aggressive behavior and development of hemipenes in females. Ovariectomized females were as sexually attractive as control females, which does not support the need for the demasculinization of the cues used for sex recognition by ovarian hormones as suggested in other sauropsids. On the other hand, our results point to the masculinization of the sex recognition cues by male gonadal androgens. Previously, we also demonstrated that sexually dimorphic growth is controlled by ovarian hormones in P. picta. Overall, it appears that individual behavioral and morphological sexually-dimorphic traits are controlled by multiple endogenous pathways in this species. Variability in the endogenous control of particular traits could have permitted their disentangling during evolution and the occurrence of (semi)independent changes across squamate phylogeny. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Smart sensor: a platform for an interactive human physiological state recognition study

    Directory of Open Access Journals (Sweden)

    Andrej Gorochovik

    2013-03-01

    Full Text Available This paper describes a concept of making interactive human state recognition systems based on smart sensor design. The token measures on proper ADC signal processing had significantly lowered the interference level. A more reliable way of measuring human skin temperature was offered by using Maxim DS18B20 digital thermometers. They introduced a more sensible response to temperature changes compared to previously used analog LM35 thermometers. An adaptive HR measuring algorithm was introduced to suppress incorrect ECG signal readings caused by human muscular activities. User friendly interactive interface for touch sensitive GLCD screen was developed to present real time physiological data readings both in numerals and graphics. User was granted an ability to dynamically customize data processing methods according to his needs. Specific procedures were developed to simplify physiological state recording for further analysis. The introduced physiological data sampling and preprocessing platform was optimized to be compatible with “ATmega Oscilloscope” PC data collecting and visualizing software.

  4. Human Pose Estimation and Activity Recognition from Multi-View Videos

    DEFF Research Database (Denmark)

    Holte, Michael Boelstoft; Tran, Cuong; Trivedi, Mohan

    2012-01-01

    approaches which have been proposed to comply with these requirements. We report a comparison of the most promising methods for multi-view human action recognition using two publicly available datasets: the INRIA Xmas Motion Acquisition Sequences (IXMAS) Multi-View Human Action Dataset, and the i3DPost Multi......–computer interaction (HCI), assisted living, gesture-based interactive games, intelligent driver assistance systems, movies, 3D TV and animation, physical therapy, autonomous mental development, smart environments, sport motion analysis, video surveillance, and video annotation. Next, we review and categorize recent......-View Human Action and Interaction Dataset. To compare the proposed methods, we give a qualitative assessment of methods which cannot be compared quantitatively, and analyze some prominent 3D pose estimation techniques for application, where not only the performed action needs to be identified but a more...

  5. Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches

    KAUST Repository

    Jiang, Hanlun

    2016-12-06

    MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.

  6. Synergistic Effects of Human Milk Nutrients in the Support of Infant Recognition Memory: An Observational Study.

    Science.gov (United States)

    Cheatham, Carol L; Sheppard, Kelly Will

    2015-11-03

    The aim was to explore the relation of human milk lutein; choline; and docosahexaenoic acid (DHA) with recognition memory abilities of six-month-olds. Milk samples obtained three to four months postpartum were analyzed for fatty acids, lutein, and choline. At six months, participants were invited to an electrophysiology session. Recognition memory was tested with a 70-30 oddball paradigm in a high-density 128-lead event-related potential (ERP) paradigm. Complete data were available for 55 participants. Data were averaged at six groupings (Frontal Right; Frontal Central; Frontal Left; Central; Midline; and Parietal) for latency to peak, peak amplitude, and mean amplitude. Difference scores were calculated as familiar minus novel. Final regression models revealed the lutein X free choline interaction was significant for the difference in latency scores at frontal and central areas (p lutein levels were related to better recognition memory. The DHA X free choline interaction was also significant for the difference in latency scores at frontal, central, and midline areas (p milk nutrients appear important in predicting infant cognition, and there may be a benefit to specific nutrient combinations.

  7. Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics.

    Science.gov (United States)

    Komeili, Majid; Louis, Wael; Armanfard, Narges; Hatzinakos, Dimitrios

    2018-05-01

    Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

  8. CD56 Is a Pathogen Recognition Receptor on Human Natural Killer Cells.

    Science.gov (United States)

    Ziegler, Sabrina; Weiss, Esther; Schmitt, Anna-Lena; Schlegel, Jan; Burgert, Anne; Terpitz, Ulrich; Sauer, Markus; Moretta, Lorenzo; Sivori, Simona; Leonhardt, Ines; Kurzai, Oliver; Einsele, Hermann; Loeffler, Juergen

    2017-07-21

    Aspergillus (A.) fumigatus is an opportunistic fungal mold inducing invasive aspergillosis (IA) in immunocompromised patients. Although antifungal activity of human natural killer (NK) cells was shown in previous studies, the underlying cellular mechanisms and pathogen recognition receptors (PRRs) are still unknown. Using flow cytometry we were able to show that the fluorescence positivity of the surface receptor CD56 significantly decreased upon fungal contact. To visualize the interaction site of NK cells and A. fumigatus we used SEM, CLSM and dSTORM techniques, which clearly demonstrated that NK cells directly interact with A. fumigatus via CD56 and that CD56 is re-organized and accumulated at this interaction site time-dependently. The inhibition of the cytoskeleton showed that the receptor re-organization was an active process dependent on actin re-arrangements. Furthermore, we could show that CD56 plays a role in the fungus mediated NK cell activation, since blocking of CD56 surface receptor reduced fungal mediated NK cell activation and reduced cytokine secretion. These results confirmed the direct interaction of NK cells and A. fumigatus, leading to the conclusion that CD56 is a pathogen recognition receptor. These findings give new insights into the functional role of CD56 in the pathogen recognition during the innate immune response.

  9. A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data

    Directory of Open Access Journals (Sweden)

    Alessandro Manzi

    2017-05-01

    Full Text Available Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM, trained with Sequential Minimal Optimization (SMO. The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60 and the Telecommunication Systems Team (TST Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

  10. A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

    Science.gov (United States)

    Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

    2017-05-11

    Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

  11. Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches.

    Science.gov (United States)

    Jiang, Hanlun; Zhu, Lizhe; Héliou, Amélie; Gao, Xin; Bernauer, Julie; Huang, Xuhui

    2017-01-01

    MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.

  12. Neuroanatomical Markers of Social Hierarchy Recognition in Humans: A Combined ERP/MRI Study.

    Science.gov (United States)

    Santamaría-García, Hernando; Burgaleta, Miguel; Sebastián-Gallés, Nuria

    2015-07-29

    Social hierarchy is an ubiquitous principle of social organization across animal species. Although some progress has been made in our understanding of how humans infer hierarchical identity, the neuroanatomical basis for perceiving key social dimensions of others remains unexplored. Here, we combined event-related potentials and structural MRI to reveal the neuroanatomical substrates of early status recognition. We designed a covertly simulated hierarchical setting in which participants performed a task either with a superior or with an inferior player. Participants showed higher amplitude in the N170 component when presented with a picture of a superior player compared with an inferior player. Crucially, the magnitude of this effect correlated with brain morphology of the posterior cingulate cortex, superior temporal gyrus, insula, fusiform gyrus, and caudate nucleus. We conclude that early recognition of social hierarchies relies on the structural properties of a network involved in the automatic recognition of social identity. Humans can perceive social hierarchies very rapidly, an ability that is key for social interactions. However, some individuals are more sensitive to hierarchical information than others. Currently, it is unknown how brain structure supports such fast-paced processes of social hierarchy perception and their individual differences. Here, we addressed this issue for the first time by combining the high temporal resolution of event-related potentials (ERPs) and the high spatial resolution of structural MRI. This methodological approach allowed us to unveil a novel association between ERP neuromarkers of social hierarchy perception and the morphology of several cortical and subcortical brain regions typically assumed to play a role in automatic processes of social cognition. Our results are a step forward in our understanding of the human social brain. Copyright © 2015 the authors 0270-6474/15/3510843-08$15.00/0.

  13. Achilles' ear? Inferior human short-term and recognition memory in the auditory modality.

    Science.gov (United States)

    Bigelow, James; Poremba, Amy

    2014-01-01

    Studies of the memory capabilities of nonhuman primates have consistently revealed a relative weakness for auditory compared to visual or tactile stimuli: extensive training is required to learn auditory memory tasks, and subjects are only capable of retaining acoustic information for a brief period of time. Whether a parallel deficit exists in human auditory memory remains an outstanding question. In the current study, a short-term memory paradigm was used to test human subjects' retention of simple auditory, visual, and tactile stimuli that were carefully equated in terms of discriminability, stimulus exposure time, and temporal dynamics. Mean accuracy did not differ significantly among sensory modalities at very short retention intervals (1-4 s). However, at longer retention intervals (8-32 s), accuracy for auditory stimuli fell substantially below that observed for visual and tactile stimuli. In the interest of extending the ecological validity of these findings, a second experiment tested recognition memory for complex, naturalistic stimuli that would likely be encountered in everyday life. Subjects were able to identify all stimuli when retention was not required, however, recognition accuracy following a delay period was again inferior for auditory compared to visual and tactile stimuli. Thus, the outcomes of both experiments provide a human parallel to the pattern of results observed in nonhuman primates. The results are interpreted in light of neuropsychological data from nonhuman primates, which suggest a difference in the degree to which auditory, visual, and tactile memory are mediated by the perirhinal and entorhinal cortices.

  14. Achilles' ear? Inferior human short-term and recognition memory in the auditory modality.

    Directory of Open Access Journals (Sweden)

    James Bigelow

    Full Text Available Studies of the memory capabilities of nonhuman primates have consistently revealed a relative weakness for auditory compared to visual or tactile stimuli: extensive training is required to learn auditory memory tasks, and subjects are only capable of retaining acoustic information for a brief period of time. Whether a parallel deficit exists in human auditory memory remains an outstanding question. In the current study, a short-term memory paradigm was used to test human subjects' retention of simple auditory, visual, and tactile stimuli that were carefully equated in terms of discriminability, stimulus exposure time, and temporal dynamics. Mean accuracy did not differ significantly among sensory modalities at very short retention intervals (1-4 s. However, at longer retention intervals (8-32 s, accuracy for auditory stimuli fell substantially below that observed for visual and tactile stimuli. In the interest of extending the ecological validity of these findings, a second experiment tested recognition memory for complex, naturalistic stimuli that would likely be encountered in everyday life. Subjects were able to identify all stimuli when retention was not required, however, recognition accuracy following a delay period was again inferior for auditory compared to visual and tactile stimuli. Thus, the outcomes of both experiments provide a human parallel to the pattern of results observed in nonhuman primates. The results are interpreted in light of neuropsychological data from nonhuman primates, which suggest a difference in the degree to which auditory, visual, and tactile memory are mediated by the perirhinal and entorhinal cortices.

  15. Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos

    Directory of Open Access Journals (Sweden)

    Li Yao

    2016-01-01

    Full Text Available Both static features and motion features have shown promising performance in human activities recognition task. However, the information included in these features is insufficient for complex human activities. In this paper, we propose extracting relational information of static features and motion features for human activities recognition. The videos are represented by a classical Bag-of-Word (BoW model which is useful in many works. To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook. After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set. Then we use a k-way partition to create a new codebook in which similar words are getting together. With this new codebook, videos can be represented by a new BoW vector with strong relational information. Moreover, we propose a method to compute new clusters from the divisive algorithm’s projective function. We test our work on the several datasets and obtain very promising results.

  16. Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition.

    Science.gov (United States)

    Tao, Dapeng; Jin, Lianwen; Yuan, Yuan; Xue, Yang

    2016-06-01

    With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition.

  17. Tracking a Subset of Skeleton Joints: An Effective Approach towards Complex Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Muhammad Latif Anjum

    2017-01-01

    Full Text Available We present a robust algorithm for complex human activity recognition for natural human-robot interaction. The algorithm is based on tracking the position of selected joints in human skeleton. For any given activity, only a few skeleton joints are involved in performing the activity, so a subset of joints contributing the most towards the activity is selected. Our approach of tracking a subset of skeleton joints (instead of tracking the whole skeleton is computationally efficient and provides better recognition accuracy. We have developed both manual and automatic approaches for the selection of these joints. The position of the selected joints is tracked for the duration of the activity and is used to construct feature vectors for each activity. Once the feature vectors have been constructed, we use a Support Vector Machines (SVM multiclass classifier for training and testing the algorithm. The algorithm has been tested on a purposely built dataset of depth videos recorded using Kinect camera. The dataset consists of 250 videos of 10 different activities being performed by different users. Experimental results show classification accuracy of 83% when tracking all skeleton joints, 95% when using manual selection of subset joints, and 89% when using automatic selection of subset joints.

  18. Achilles’ Ear? Inferior Human Short-Term and Recognition Memory in the Auditory Modality

    Science.gov (United States)

    Bigelow, James; Poremba, Amy

    2014-01-01

    Studies of the memory capabilities of nonhuman primates have consistently revealed a relative weakness for auditory compared to visual or tactile stimuli: extensive training is required to learn auditory memory tasks, and subjects are only capable of retaining acoustic information for a brief period of time. Whether a parallel deficit exists in human auditory memory remains an outstanding question. In the current study, a short-term memory paradigm was used to test human subjects’ retention of simple auditory, visual, and tactile stimuli that were carefully equated in terms of discriminability, stimulus exposure time, and temporal dynamics. Mean accuracy did not differ significantly among sensory modalities at very short retention intervals (1–4 s). However, at longer retention intervals (8–32 s), accuracy for auditory stimuli fell substantially below that observed for visual and tactile stimuli. In the interest of extending the ecological validity of these findings, a second experiment tested recognition memory for complex, naturalistic stimuli that would likely be encountered in everyday life. Subjects were able to identify all stimuli when retention was not required, however, recognition accuracy following a delay period was again inferior for auditory compared to visual and tactile stimuli. Thus, the outcomes of both experiments provide a human parallel to the pattern of results observed in nonhuman primates. The results are interpreted in light of neuropsychological data from nonhuman primates, which suggest a difference in the degree to which auditory, visual, and tactile memory are mediated by the perirhinal and entorhinal cortices. PMID:24587119

  19. New generation of human machine interfaces for controlling UAV through depth-based gesture recognition

    Science.gov (United States)

    Mantecón, Tomás.; del Blanco, Carlos Roberto; Jaureguizar, Fernando; García, Narciso

    2014-06-01

    New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

  20. Wearable-Based Human Activity Recognition Using an IoT Approach

    Directory of Open Access Journals (Sweden)

    Diego Castro

    2017-11-01

    Full Text Available This paper presents a novel system based on the Internet of Things (IoT to Human Activity Recognition (HAR by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog. Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

  1. Distinct parietal sites mediate the influences of mood, arousal, and their interaction on human recognition memory.

    Science.gov (United States)

    Greene, Ciara M; Flannery, Oliver; Soto, David

    2014-12-01

    The two dimensions of emotion, mood valence and arousal, have independent effects on recognition memory. At present, however, it is not clear how those effects are reflected in the human brain. Previous research in this area has generally dealt with memory for emotionally valenced or arousing stimuli, but the manner in which interacting mood and arousal states modulate responses in memory substrates remains poorly understood. We investigated memory for emotionally neutral items while independently manipulating mood valence and arousal state by means of music exposure. Four emotional conditions were created: positive mood/high arousal, positive mood/low arousal, negative mood/high arousal, and negative mood/low arousal. We observed distinct effects of mood valence and arousal in parietal substrates of recognition memory. Positive mood increased activity in ventral posterior parietal cortex (PPC) and orbitofrontal cortex, whereas arousal condition modulated activity in dorsal PPC and the posterior cingulate. An interaction between valence and arousal was observed in left ventral PPC, notably in a parietal area distinct from the those identified for the main effects, with a stronger effect of mood on recognition memory responses here under conditions of relative high versus low arousal. We interpreted the PPC activations in terms of the attention-to-memory hypothesis: Increased arousal may lead to increased top-down control of memory, and hence dorsal PPC activation, whereas positive mood valence may result in increased activity in ventral PPC regions associated with bottom-up attention to memory. These findings indicate that distinct parietal sites mediate the influences of mood, arousal, and their interplay during recognition memory.

  2. Face Recognition and Tracking in Videos

    Directory of Open Access Journals (Sweden)

    Swapnil Vitthal Tathe

    2017-07-01

    Full Text Available Advancement in computer vision technology and availability of video capturing devices such as surveillance cameras has evoked new video processing applications. The research in video face recognition is mostly biased towards law enforcement applications. Applications involves human recognition based on face and iris, human computer interaction, behavior analysis, video surveillance etc. This paper presents face tracking framework that is capable of face detection using Haar features, recognition using Gabor feature extraction, matching using correlation score and tracking using Kalman filter. The method has good recognition rate for real-life videos and robust performance to changes due to illumination, environmental factors, scale, pose and orientations.

  3. Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

    Directory of Open Access Journals (Sweden)

    Cai Wingfield

    2017-09-01

    Full Text Available There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG, generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

  4. Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    German Ignacio Parisi

    2015-06-01

    Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

  5. Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants.

    Science.gov (United States)

    Capela, N A; Lemaire, E D; Baddour, N; Rudolf, M; Goljar, N; Burger, H

    2016-01-20

    Mobile health monitoring using wearable sensors is a growing area of interest. As the world's population ages and locomotor capabilities decrease, the ability to report on a person's mobility activities outside a hospital setting becomes a valuable tool for clinical decision-making and evaluating healthcare interventions. Smartphones are omnipresent in society and offer convenient and suitable sensors for mobility monitoring applications. To enhance our understanding of human activity recognition (HAR) system performance for able-bodied and populations with gait deviations, this research evaluated a custom smartphone-based HAR classifier on fifteen able-bodied participants and fifteen participants who suffered a stroke. Participants performed a consecutive series of mobility tasks and daily living activities while wearing a BlackBerry Z10 smartphone on their waist to collect accelerometer and gyroscope data. Five features were derived from the sensor data and used to classify participant activities (decision tree). Sensitivity, specificity and F-scores were calculated to evaluate HAR classifier performance. The classifier performed well for both populations when differentiating mobile from immobile states (F-score > 94 %). As activity recognition complexity increased, HAR system sensitivity and specificity decreased for the stroke population, particularly when using information derived from participant posture to make classification decisions. Human activity recognition using a smartphone based system can be accomplished for both able-bodied and stroke populations; however, an increase in activity classification complexity leads to a decrease in HAR performance with a stroke population. The study results can be used to guide smartphone HAR system development for populations with differing movement characteristics.

  6. Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

    Directory of Open Access Journals (Sweden)

    Fernando Moya Rueda

    2018-05-01

    Full Text Available Human activity recognition (HAR is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs.

  7. Stages of Processing in Associative Recognition : Evidence from Behavior, EEG, and Classification

    NARCIS (Netherlands)

    Borst, Jelmer P.; Schneider, Darryl W.; Walsh, Matthew M.; Anderson, John R.

    2013-01-01

    In this study, we investigated the stages of information processing in associative recognition. We recorded EEG data while participants performed an associative recognition task that involved manipulations of word length, associative fan, and probe type, which were hypothesized to affect the

  8. Pattern Recognition via the Toll-Like Receptor System in the Human Female Genital Tract

    Directory of Open Access Journals (Sweden)

    Kaei Nasu

    2010-01-01

    Full Text Available The mucosal surface of the female genital tract is a complex biosystem, which provides a barrier against the outside world and participates in both innate and acquired immune defense systems. This mucosal compartment has adapted to a dynamic, non-sterile environment challenged by a variety of antigenic/inflammatory stimuli associated with sexual intercourse and endogenous vaginal microbiota. Rapid innate immune defenses against microbial infection usually involve the recognition of invading pathogens by specific pattern-recognition receptors recently attributed to the family of Toll-like receptors (TLRs. TLRs recognize conserved pathogen-associated molecular patterns (PAMPs synthesized by microorganisms including bacteria, fungi, parasites, and viruses as well as endogenous ligands associated with cell damage. Members of the TLR family, which includes 10 human TLRs identified to date, recognize distinct PAMPs produced by various bacterial, fungal, and viral pathogens. The available literature regarding the innate immune system of the female genital tract during human reproductive processes was reviewed in order to identify studies specifically related to the expression and function of TLRs under normal as well as pathological conditions. Increased understanding of these molecules may provide insight into site-specific immunoregulatory mechanisms in the female reproductive tract.

  9. A Kinect-Based Gesture Recognition Approach for a Natural Human Robot Interface

    Directory of Open Access Journals (Sweden)

    Grazia Cicirelli

    2015-03-01

    Full Text Available In this paper, we present a gesture recognition system for the development of a human-robot interaction (HRI interface. Kinect cameras and the OpenNI framework are used to obtain real-time tracking of a human skeleton. Ten different gestures, performed by different persons, are defined. Quaternions of joint angles are first used as robust and significant features. Next, neural network (NN classifiers are trained to recognize the different gestures. This work deals with different challenging tasks, such as the real-time implementation of a gesture recognition system and the temporal resolution of gestures. The HRI interface developed in this work includes three Kinect cameras placed at different locations in an indoor environment and an autonomous mobile robot that can be remotely controlled by one operator standing in front of one of the Kinects. Moreover, the system is supplied with a people re-identification module which guarantees that only one person at a time has control of the robot. The system's performance is first validated offline, and then online experiments are carried out, proving the real-time operation of the system as required by a HRI interface.

  10. A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

    Directory of Open Access Journals (Sweden)

    Daniela Sánchez

    2017-01-01

    Full Text Available A grey wolf optimizer for modular neural network (MNN with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

  11. Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening.

    Science.gov (United States)

    Cho, Heeryon; Yoon, Sang Min

    2018-04-01

    Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

  12. Depth-based human activity recognition: A comparative perspective study on feature extraction

    Directory of Open Access Journals (Sweden)

    Heba Hamdy Ali

    2018-06-01

    Full Text Available Depth Maps-based Human Activity Recognition is the process of categorizing depth sequences with a particular activity. In this problem, some applications represent robust solutions in domains such as surveillance system, computer vision applications, and video retrieval systems. The task is challenging due to variations inside one class and distinguishes between activities of various classes and video recording settings. In this study, we introduce a detailed study of current advances in the depth maps-based image representations and feature extraction process. Moreover, we discuss the state of art datasets and subsequent classification procedure. Also, a comparative study of some of the more popular depth-map approaches has provided in greater detail. The proposed methods are evaluated on three depth-based datasets “MSR Action 3D”, “MSR Hand Gesture”, and “MSR Daily Activity 3D”. Experimental results achieved 100%, 95.83%, and 96.55% respectively. While combining depth and color features on “RGBD-HuDaAct” Dataset, achieved 89.1%. Keywords: Activity recognition, Depth, Feature extraction, Video, Human body detection, Hand gesture

  13. Human Activity Recognition in Real-Times Environments using Skeleton Joints

    Directory of Open Access Journals (Sweden)

    Ajay Kumar

    2016-06-01

    Full Text Available In this research work, we proposed a most effective noble approach for Human activity recognition in real-time environments. We recognize several distinct dynamic human activity actions using kinect. A 3D skeleton data is processed from real-time video gesture to sequence of frames and getter skeleton joints (Energy Joints, orientation, rotations of joint angles from selected setof frames. We are using joint angle and orientations, rotations information from Kinect therefore less computation required. However, after extracting the set of frames we implemented several classification techniques Principal Component Analysis (PCA with several distance based classifiers and Artificial Neural Network (ANN respectively with some variants for classify our all different gesture models. However, we conclude that use very less number of frame (10-15% for train our system efficiently from the entire set of gesture frames. Moreover, after successfully completion of our classification methods we clinch an excellent overall accuracy 94%, 96% and 98% respectively. We finally observe that our proposed system is more useful than comparing to other existing system, therefore our model is best suitable for real-time application such as in video games for player action/gesture recognition.

  14. Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening

    Directory of Open Access Journals (Sweden)

    Heeryon Cho

    2018-04-01

    Full Text Available Human Activity Recognition (HAR aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

  15. A Review on Human Activity Recognition Using Vision-Based Method.

    Science.gov (United States)

    Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen

    2017-01-01

    Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

  16. Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening †

    Science.gov (United States)

    Yoon, Sang Min

    2018-01-01

    Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. PMID:29614767

  17. Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

    Science.gov (United States)

    Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

    2016-09-01

    Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

  18. Epitope imprinted polymer nanoparticles containing fluorescent quantum dots for specific recognition of human serum albumin

    International Nuclear Information System (INIS)

    Wang, Yi-Zhi; Li, Dong-Yan; He, Xi-Wen; Li, Wen-You; Zhang, Yu-Kui

    2015-01-01

    Epitope imprinted polymer nanoparticles (EI-NPs) were prepared by one-pot polymerization of N-isopropylacrylamide in the presence of CdTe quantum dots and an epitope (consisting of amino acids 598 to 609) of human serum albumin (HSA). The resulting EI-NPs exhibit specific recognition ability and enable direct fluorescence quantification of HSA based on a fluorescence turn-on mode. The polymer was characterized by FT-IR, X-ray photoelectron spectroscopy, transmission electron microscopy and dynamic light scattering. The linear calibration graph was obtained in the range of 0.25–5 μmol · mL −1 with the detection limit of 44.3 nmol · mL −1 . The EI-NPs were successfully applied to the direct fluorometric quantification of HSA in samples of human serum. Overall, this approach provides a promising tool to design functional fluorescent materials with protein recognition capability and specific applications in proteomics. (author)

  19. Starch Catabolism by a Prominent Human Gut Symbiont Is Directed by the Recognition of Amylose Helices

    Energy Technology Data Exchange (ETDEWEB)

    Koropatkin, Nicole M.; Martens, Eric C.; Gordon, Jeffrey I.; Smith, Thomas J. (WU); (Danforth)

    2009-01-12

    The human gut microbiota performs functions that are not encoded in our Homo sapiens genome, including the processing of otherwise undigestible dietary polysaccharides. Defining the structures of proteins involved in the import and degradation of specific glycans by saccharolytic bacteria complements genomic analysis of the nutrient-processing capabilities of gut communities. Here, we describe the atomic structure of one such protein, SusD, required for starch binding and utilization by Bacteroides thetaiotaomicron, a prominent adaptive forager of glycans in the distal human gut microbiota. The binding pocket of this unique {alpha}-helical protein contains an arc of aromatic residues that complements the natural helical structure of starch and imposes this conformation on bound maltoheptaose. Furthermore, SusD binds cyclic oligosaccharides with higher affinity than linear forms. The structures of several SusD/oligosaccharide complexes reveal an inherent ligand recognition plasticity dominated by the three-dimensional conformation of the oligosaccharides rather than specific interactions with the composite sugars.

  20. Combining users' activity survey and simulators to evaluate human activity recognition systems.

    Science.gov (United States)

    Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

    2015-04-08

    Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

  1. Combining Users’ Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

    Directory of Open Access Journals (Sweden)

    Gorka Azkune

    2015-04-01

    Full Text Available Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

  2. Combining Users' Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

    Science.gov (United States)

    Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

    2015-01-01

    Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant. PMID:25856329

  3. Stress prompts habit behavior in humans.

    Science.gov (United States)

    Schwabe, Lars; Wolf, Oliver T

    2009-06-03

    Instrumental behavior can be controlled by goal-directed action-outcome and habitual stimulus-response processes that are supported by anatomically distinct brain systems. Based on previous findings showing that stress modulates the interaction of "cognitive" and "habit" memory systems, we asked in the presented study whether stress may coordinate goal-directed and habit processes in instrumental learning. For this purpose, participants were exposed to stress (socially evaluated cold pressor test) or a control condition before they were trained to perform two instrumental actions that were associated with two distinct food outcomes. After training, one of these food outcomes was selectively devalued as subjects were saturated with that food. Next, subjects were presented the two instrumental actions in extinction. Stress before training in the instrumental task rendered participants' behavior insensitive to the change in the value of the food outcomes, that is stress led to habit performance. Moreover, stress reduced subjects' explicit knowledge of the action-outcome contingencies. These results demonstrate for the first time that stress promotes habits at the expense of goal-directed performance in humans.

  4. 1st Workshop on Human Factors and Activity Recognition in Healthcare, Wellness and Assisted Living: Recognise2Interact

    NARCIS (Netherlands)

    Casale, P.; Houben, S.; Amft, O.D.

    2013-01-01

    Context-aware systems have the potential to revolutionize the way humans interact with information technology. The first workshop on Human Factors and Activity Recognition in Healthcare, Wellness and Assisted Living (Recognise2Interact) aims to enable researchers and practitioners from both,

  5. Human behavior research and the design of sustainable transport systems

    Science.gov (United States)

    Schauer, James J.

    2011-09-01

    this nature that examine broader demographic groups in real world conditions are greatly needed in different regions of the US and around the world. As interventions are sought to stabilize atmospheric carbon dioxide levels at levels that are expected to have limited climate impact, there is recognition that the mitigation strategies that will be implemented in the next 5-10 years will have a profound impact on the ability to constrain climate change. The evolution of the transport infrastructure over the next decade, which will provide intermodal opportunities and modal trade-offs, will be an important constraint in the ability of transport systems to reduce greenhouse gas emissions. Likewise, the evolution of the transport infrastructure over the next decade will have an equally profound impact on the ability of transport systems to meet society's expectations for transport in a cost effective and efficient manner. The ability to design and build transport infrastructures that can achieve maximum reductions in greenhouse gas emissions while satisfying the demand for transport by the society relies on the ability to understand the human behavior and human preferences for transport in the context of costs, time, time variability, safety and emission reductions. The study by Gaker et al (2011) is central to answering these questions and will hopefully serve as a conduit to motivate additional studies that examine broader segments of society in developed, rapidly developing, and underdeveloped nations to provide the human input needed to assure future transport systems that can meet greenhouse gas emission reduction targets and the transport needs of society. References Gaker D, Vautin D, Vij A and Walker J L 2011 The power and value of green in promoting sustainable transport behavior Environ. Res. Lett. 6 034010 IEA 2009 Transport, Energy and CO2: Moving Toward Sustainability (Paris: International Energy Agency) (available at www

  6. Multi-Touch Collaborative Gesture Recognition Based User Interfaces as Behavioral Interventions for Children with Autistic Spectrum Disorder: A Review

    Directory of Open Access Journals (Sweden)

    AHMED HASSAN

    2016-10-01

    Full Text Available This paper addresses UI (User Interface designing based on multi-touch collaborative gesture recognition meant for ASD (Autism Spectrum Disorder - affected children. The present user interfaces (in the context of behavioral interventions for Autism Spectrum disorder are investigated in detail. Thorough comparison has been made among various groups of these UIs. Advantages and limitations of these interfaces are discussed and future directions for the design of such interfaces are suggested.

  7. Multi-Touch Collaborative Gesture Recognition Based User Interfaces as Behavioral Interventions for Children with Autistic Spectrum Disorder: A Review

    International Nuclear Information System (INIS)

    Hassan, A.; Shafi, M.; Khattak, M.

    2016-01-01

    This paper addresses UI (User Interface) designing based on multi-touch collaborative gesture recognition meant for ASD (Autism Spectrum Disorder) - affected children. The present user interfaces (in the context of behavioral interventions for Autism Spectrum disorder) are investigated in detail. Thorough comparison has been made among various groups of these UIs. Advantages and limitations of these interfaces are discussed and future directions for the design of such interfaces are suggested. (author)

  8. Emotion expression in human punishment behavior.

    Science.gov (United States)

    Xiao, Erte; Houser, Daniel

    2005-05-17

    Evolutionary theory reveals that punishment is effective in promoting cooperation and maintaining social norms. Although it is accepted that emotions are connected to punishment decisions, there remains substantial debate over why humans use costly punishment. Here we show experimentally that constraints on emotion expression can increase the use of costly punishment. We report data from ultimatum games, where a proposer offers a division of a sum of money and a responder decides whether to accept the split, or reject and leave both players with nothing. Compared with the treatment in which expressing emotions directly to proposers is prohibited, rejection of unfair offers is significantly less frequent when responders can convey their feelings to the proposer concurrently with their decisions. These data support the view that costly punishment might itself be used to express negative emotions and suggest that future studies will benefit by recognizing that human demand for emotion expression can have significant behavioral consequences in social environments, including families, courts, companies, and markets.

  9. Recognition and Prediction of Human Actions for Safe Human-Robot Collaboration

    DEFF Research Database (Denmark)

    Andersen, Rasmus Skovgaard; Bøgh, Simon; Ceballos, Iker

    Collaborative industrial robots are creating new opportunities for collaboration between humans and robots in shared workspaces. In order for such collaboration to be efficient, robots - as well as humans - need to have an understanding of the other's intentions and current ongoing action....... In this work, we propose a method for learning, classifying, and predicting actions taken by a human. Our proposed method is based on the human skeleton model from a Kinect. For demonstration of our approach we chose a typical pick-and-place scenario. Therefore, only arms and upper body are considered......; in total 15 joints. Based on trajectories on these joints, different classes of motion are separated using partitioning around medoids (PAM). Subsequently, SVM is used to train the classes to form a library of human motions. The approach allows run-time detection of when a new motion has been initiated...

  10. A DESIGN FRAMEWORK FOR HUMAN EMOTION RECOGNITION USING ELECTROCARDIOGRAM AND SKIN CONDUCTANCE RESPONSE SIGNALS

    Directory of Open Access Journals (Sweden)

    KHAIRUN NISA’ MINHAD

    2017-11-01

    Full Text Available Identification of human emotional state while driving a vehicle can help in understanding the human behaviour. Based on this identification, a response system can be developed in order to mitigate the impact that may be resulted from the behavioural changes. However, the adaptation of emotions to the environment at most scenarios is subjective to an individual’s perspective. Many factors, mainly cultural and geography, gender, age, life style and history, level of education and professional status, can affect the detection of human emotional affective states. This work investigated sympathetic responses toward human emotions defined by using electrocardiography (ECG and skin conductance response (SCR signals recorded simultaneously. This work aimed to recognize ECG and SCR patterns of the investigated emotions measured using selected sensor. A pilot study was conducted to evaluate the proposed framework. Initial results demonstrated the importance of suitability of the stimuli used to evoke the emotions and high opportunity for the ECG and SCR signals to be used in the automotive real-time emotion recognition systems.

  11. Human SAP is a novel peptidoglycan recognition protein that induces complement- independent phagocytosis of Staphylococcus aureus

    Science.gov (United States)

    An, Jang-Hyun; Kurokawa, Kenji; Jung, Dong-Jun; Kim, Min-Jung; Kim, Chan-Hee; Fujimoto, Yukari; Fukase, Koichi; Coggeshall, K. Mark; Lee, Bok Luel

    2014-01-01

    The human pathogen Staphylococcus aureus is responsible for many community-acquired and hospital-associated infections and is associated with high mortality. Concern over the emergence of multidrug-resistant strains has renewed interest in the elucidation of host mechanisms that defend against S. aureus infection. We recently demonstrated that human serum mannose-binding lectin (MBL) binds to S. aureus wall teichoic acid (WTA), a cell wall glycopolymer, a discovery that prompted further screening to identify additional serum proteins that recognize S. aureus cell wall components. In this report, we incubated human serum with 10 different S. aureus mutants and determined that serum amyloid P component (SAP) bound specifically to a WTA-deficient S. aureus ΔtagO mutant, but not to tagO-complemented, WTA-expressing cells. Biochemical characterization revealed that SAP recognizes bacterial peptidoglycan as a ligand and that WTA inhibits this interaction. Although SAP binding to peptidoglycan was not observed to induce complement activation, SAP-bound ΔtagO cells were phagocytosed by human polymorphonuclear leukocytes in an Fcγ receptor-dependent manner. These results indicate that SAP functions as a host defense factor, similar to other peptidoglycan recognition proteins and nucleotide-binding oligomerization domain (NOD)-like receptors. PMID:23966633

  12. Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Fei Chen

    2015-04-01

    Full Text Available Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker's hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM method is adopted to recognize patterns via data streams and identify workers' gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.

  13. RGBD Video Based Human Hand Trajectory Tracking and Gesture Recognition System

    Directory of Open Access Journals (Sweden)

    Weihua Liu

    2015-01-01

    Full Text Available The task of human hand trajectory tracking and gesture trajectory recognition based on synchronized color and depth video is considered. Toward this end, in the facet of hand tracking, a joint observation model with the hand cues of skin saliency, motion and depth is integrated into particle filter in order to move particles to local peak in the likelihood. The proposed hand tracking method, namely, salient skin, motion, and depth based particle filter (SSMD-PF, is capable of improving the tracking accuracy considerably, in the context of the signer performing the gesture toward the camera device and in front of moving, cluttered backgrounds. In the facet of gesture recognition, a shape-order context descriptor on the basis of shape context is introduced, which can describe the gesture in spatiotemporal domain. The efficient shape-order context descriptor can reveal the shape relationship and embed gesture sequence order information into descriptor. Moreover, the shape-order context leads to a robust score for gesture invariant. Our approach is complemented with experimental results on the settings of the challenging hand-signed digits datasets and American sign language dataset, which corroborate the performance of the novel techniques.

  14. Natural micropolymorphism in human leukocyte antigens provides a basis for genetic control of antigen recognition

    Energy Technology Data Exchange (ETDEWEB)

    Archbold, Julia K.; Macdonald, Whitney A.; Gras, Stephanie; Ely, Lauren K.; Miles, John J.; Bell, Melissa J.; Brennan, Rebekah M.; Beddoe, Travis; Wilce, Matthew C.J.; Clements, Craig S.; Purcell, Anthony W.; McCluskey, James; Burrows, Scott R.; Rossjohn, Jamie; (Monash); (Queensland Inst. of Med. Rsrch.); (Melbourne)

    2009-07-10

    Human leukocyte antigen (HLA) gene polymorphism plays a critical role in protective immunity, disease susceptibility, autoimmunity, and drug hypersensitivity, yet the basis of how HLA polymorphism influences T cell receptor (TCR) recognition is unclear. We examined how a natural micropolymorphism in HLA-B44, an important and large HLA allelic family, affected antigen recognition. T cell-mediated immunity to an Epstein-Barr virus determinant (EENLLDFVRF) is enhanced when HLA-B*4405 was the presenting allotype compared with HLA-B*4402 or HLA-B*4403, each of which differ by just one amino acid. The micropolymorphism in these HLA-B44 allotypes altered the mode of binding and dynamics of the bound viral epitope. The structure of the TCR-HLA-B*4405EENLLDFVRF complex revealed that peptide flexibility was a critical parameter in enabling preferential engagement with HLA-B*4405 in comparison to HLA-B*4402/03. Accordingly, major histocompatibility complex (MHC) polymorphism can alter the dynamics of the peptide-MHC landscape, resulting in fine-tuning of T cell responses between closely related allotypes.

  15. Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors.

    Science.gov (United States)

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

    2016-03-24

    The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2-30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.

  16. A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition.

    Directory of Open Access Journals (Sweden)

    Muhammad Hameed Siddiqi

    Full Text Available Research in video based FER systems has exploded in the past decade. However, most of the previous methods work well when they are trained and tested on the same dataset. Illumination settings, image resolution, camera angle, and physical characteristics of the people differ from one dataset to another. Considering a single dataset keeps the variance, which results from differences, to a minimum. Having a robust FER system, which can work across several datasets, is thus highly desirable. The aim of this work is to design, implement, and validate such a system using different datasets. In this regard, the major contribution is made at the recognition module which uses the maximum entropy Markov model (MEMM for expression recognition. In this model, the states of the human expressions are modeled as the states of an MEMM, by considering the video-sensor observations as the observations of MEMM. A modified Viterbi is utilized to generate the most probable expression state sequence based on such observations. Lastly, an algorithm is designed which predicts the expression state from the generated state sequence. Performance is compared against several existing state-of-the-art FER systems on six publicly available datasets. A weighted average accuracy of 97% is achieved across all datasets.

  17. Individual recognition of human infants on the basis of cries alone.

    Science.gov (United States)

    Green, J A; Gustafson, G E

    1983-11-01

    Human parents were asked to identify their infants on the basis of tape-recorded cries that they had not previously heard. The cries of twenty 30-day-old infants were recorded just prior to a feeding, then rerecorded onto a test tape containing cries from three other infants. Eighty percent of mothers were able to recognize their infants' cries, as were 45% of fathers. An additional 140 adults (non-parents) were tested in order to determine if the process of dubbing cries onto test tapes had left extraneous auditory cues to infants' identities and if the foil infants were equally discriminable. The results indicated that parents' recognition was not based on extraneous cues and that, overall, the foils were appropriate distractors in the parents' task. Thus, the majority of parents can recognize their 30-day-old infants on the sole basis of acoustic cues contained in the infants' cries. The acoustic features that underlie this recognition are now being investigated.

  18. Audio-Visual Tibetan Speech Recognition Based on a Deep Dynamic Bayesian Network for Natural Human Robot Interaction

    Directory of Open Access Journals (Sweden)

    Yue Zhao

    2012-12-01

    Full Text Available Audio-visual speech recognition is a natural and robust approach to improving human-robot interaction in noisy environments. Although multi-stream Dynamic Bayesian Network and coupled HMM are widely used for audio-visual speech recognition, they fail to learn the shared features between modalities and ignore the dependency of features among the frames within each discrete state. In this paper, we propose a Deep Dynamic Bayesian Network (DDBN to perform unsupervised extraction of spatial-temporal multimodal features from Tibetan audio-visual speech data and build an accurate audio-visual speech recognition model under a no frame-independency assumption. The experiment results on Tibetan speech data from some real-world environments showed the proposed DDBN outperforms the state-of-art methods in word recognition accuracy.

  19. Psychopathy and facial emotion recognition ability in patients with bipolar affective disorder with or without delinquent behaviors.

    Science.gov (United States)

    Demirel, Husrev; Yesilbas, Dilek; Ozver, Ismail; Yuksek, Erhan; Sahin, Feyzi; Aliustaoglu, Suheyla; Emul, Murat

    2014-04-01

    It is well known that patients with bipolar disorder are more prone to violence and have more criminal behaviors than general population. A strong relationship between criminal behavior and inability to empathize and imperceptions to other person's feelings and facial expressions increases the risk of delinquent behaviors. In this study, we aimed to investigate the deficits of facial emotion recognition ability in euthymic bipolar patients who committed an offense and compare with non-delinquent euthymic patients with bipolar disorder. Fifty-five euthymic patients with delinquent behaviors and 54 non-delinquent euthymic bipolar patients as a control group were included in the study. Ekman's Facial Emotion Recognition Test, sociodemographic data, Hare Psychopathy Checklist, Hamilton Depression Rating Scale and Young Mania Rating Scale were applied to both groups. There were no significant differences between case and control groups in the meaning of average age, gender, level of education, mean age onset of disease and suicide attempt (p>0.05). The three types of most committed delinquent behaviors in patients with euthymic bipolar disorder were as follows: injury (30.8%), threat or insult (20%) and homicide (12.7%). The best accurate percentage of identified facial emotion was "happy" (>99%, for both) while the worst misidentified facial emotion was "fear" in both groups (delinquent behaviors than non-delinquent ones (pdelinquent behaviors. We have shown that patients with bipolar disorder who had delinquent behaviors may have some social interaction problems i.e., misrecognizing fearful and modestly anger facial emotions and need some more time to response facial emotions even in remission. Copyright © 2014 Elsevier Inc. All rights reserved.

  20. Modeling Individual Cyclic Variation in Human Behavior.

    Science.gov (United States)

    Pierson, Emma; Althoff, Tim; Leskovec, Jure

    2018-04-01

    Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets-of human menstrual cycle symptoms and physical activity tracking data-yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.

  1. Mobilization and Counter-mobilization Against LGBT Rights. Conservative Responses to the Recognition of Human Rights

    Directory of Open Access Journals (Sweden)

    Jairo Antonio López Pacheco

    2017-10-01

    Full Text Available This article analyzes the counter-mobilization against the institutionalization of lgbt rights in Colombia and Mexico. From an analytical framework that integrates the dimension of mobilization and counter-mobilization in the conflicts for rights, we show that the conservative reaction in Colombia and Mexico is a coordinated and active response against the conquests of sexual minorities, led by the churches, which has slowed the progress of effective recognition of human rights. We have identified similar strategies of mobilization of demand frameworks, mobilization structures and collective action repertories in both cases that have raised the political costs of institutional changes through two mechanisms of conflict: street and electoral activism and institutional activism.

  2. Improving human activity recognition and its application in early stroke diagnosis.

    Science.gov (United States)

    Villar, José R; González, Silvia; Sedano, Javier; Chira, Camelia; Trejo-Gabriel-Galan, Jose M

    2015-06-01

    The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

  3. Human antibody recognition of antigenic site IV on Pneumovirus fusion proteins.

    Science.gov (United States)

    Mousa, Jarrod J; Binshtein, Elad; Human, Stacey; Fong, Rachel H; Alvarado, Gabriela; Doranz, Benjamin J; Moore, Martin L; Ohi, Melanie D; Crowe, James E

    2018-02-01

    Respiratory syncytial virus (RSV) is a major human pathogen that infects the majority of children by two years of age. The RSV fusion (F) protein is a primary target of human antibodies, and it has several antigenic regions capable of inducing neutralizing antibodies. Antigenic site IV is preserved in both the pre-fusion and post-fusion conformations of RSV F. Antibodies to antigenic site IV have been described that bind and neutralize both RSV and human metapneumovirus (hMPV). To explore the diversity of binding modes at antigenic site IV, we generated a panel of four new human monoclonal antibodies (mAbs) and competition-binding suggested the mAbs bind at antigenic site IV. Mutagenesis experiments revealed that binding and neutralization of two mAbs (3M3 and 6F18) depended on arginine (R) residue R429. We discovered two R429-independent mAbs (17E10 and 2N6) at this site that neutralized an RSV R429A mutant strain, and one of these mAbs (17E10) neutralized both RSV and hMPV. To determine the mechanism of cross-reactivity, we performed competition-binding, recombinant protein mutagenesis, peptide binding, and electron microscopy experiments. It was determined that the human cross-reactive mAb 17E10 binds to RSV F with a binding pose similar to 101F, which may be indicative of cross-reactivity with hMPV F. The data presented provide new concepts in RSV immune recognition and vaccine design, as we describe the novel idea that binding pose may influence mAb cross-reactivity between RSV and hMPV. Characterization of the site IV epitope bound by human antibodies may inform the design of a pan-Pneumovirus vaccine.

  4. Goal inferences about robot behavior : goal inferences and human response behaviors

    NARCIS (Netherlands)

    Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.

    2014-01-01

    This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.

  5. A bottom-up model of spatial attention predicts human error patterns in rapid scene recognition.

    Science.gov (United States)

    Einhäuser, Wolfgang; Mundhenk, T Nathan; Baldi, Pierre; Koch, Christof; Itti, Laurent

    2007-07-20

    Humans demonstrate a peculiar ability to detect complex targets in rapidly presented natural scenes. Recent studies suggest that (nearly) no focal attention is required for overall performance in such tasks. Little is known, however, of how detection performance varies from trial to trial and which stages in the processing hierarchy limit performance: bottom-up visual processing (attentional selection and/or recognition) or top-down factors (e.g., decision-making, memory, or alertness fluctuations)? To investigate the relative contribution of these factors, eight human observers performed an animal detection task in natural scenes presented at 20 Hz. Trial-by-trial performance was highly consistent across observers, far exceeding the prediction of independent errors. This consistency demonstrates that performance is not primarily limited by idiosyncratic factors but by visual processing. Two statistical stimulus properties, contrast variation in the target image and the information-theoretical measure of "surprise" in adjacent images, predict performance on a trial-by-trial basis. These measures are tightly related to spatial attention, demonstrating that spatial attention and rapid target detection share common mechanisms. To isolate the causal contribution of the surprise measure, eight additional observers performed the animal detection task in sequences that were reordered versions of those all subjects had correctly recognized in the first experiment. Reordering increased surprise before and/or after the target while keeping the target and distractors themselves unchanged. Surprise enhancement impaired target detection in all observers. Consequently, and contrary to several previously published findings, our results demonstrate that attentional limitations, rather than target recognition alone, affect the detection of targets in rapidly presented visual sequences.

  6. Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition.

    Science.gov (United States)

    Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

    2017-02-08

    Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates ( F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.

  7. Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Mario Munoz-Organero

    2017-02-01

    Full Text Available Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data. The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users, the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77 even in the case of using different people executing a different sequence of movements and using different

  8. From Humans to Rats and Back Again: Bridging the Divide between Human and Animal Studies of Recognition Memory with Receiver Operating Characteristics

    Science.gov (United States)

    Koen, Joshua D.; Yonelinas, Andrew P.

    2011-01-01

    Receiver operating characteristics (ROCs) have been used extensively to study the processes underlying human recognition memory, and this method has recently been applied in studies of rats. However, the extent to which the results from human and animal studies converge is neither entirely clear, nor is it known how the different methods used to…

  9. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ahmadi Majid

    2003-01-01

    Full Text Available This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF neural network with a hybrid learning algorithm (HLA has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

  10. Interaction studies reveal specific recognition of an anti-inflammatory polyphosphorhydrazone dendrimer by human monocytes.

    Science.gov (United States)

    Ledall, Jérémy; Fruchon, Séverine; Garzoni, Matteo; Pavan, Giovanni M; Caminade, Anne-Marie; Turrin, Cédric-Olivier; Blanzat, Muriel; Poupot, Rémy

    2015-11-14

    Dendrimers are nano-materials with perfectly defined structure and size, and multivalency properties that confer substantial advantages for biomedical applications. Previous work has shown that phosphorus-based polyphosphorhydrazone (PPH) dendrimers capped with azabisphosphonate (ABP) end groups have immuno-modulatory and anti-inflammatory properties leading to efficient therapeutic control of inflammatory diseases in animal models. These properties are mainly prompted through activation of monocytes. Here, we disclose new insights into the molecular mechanisms underlying the anti-inflammatory activation of human monocytes by ABP-capped PPH dendrimers. Following an interdisciplinary approach, we have characterized the physicochemical and biological behavior of the lead ABP dendrimer with model and cell membranes, and compared this experimental set of data to predictive computational modelling studies. The behavior of the ABP dendrimer was compared to the one of an isosteric analog dendrimer capped with twelve azabiscarboxylate (ABC) end groups instead of twelve ABP end groups. The ABC dendrimer displayed no biological activity on human monocytes, therefore it was considered as a negative control. In detail, we show that the ABP dendrimer can bind both non-specifically and specifically to the membrane of human monocytes. The specific binding leads to the internalization of the ABP dendrimer by human monocytes. On the contrary, the ABC dendrimer only interacts non-specifically with human monocytes and is not internalized. These data indicate that the bioactive ABP dendrimer is recognized by specific receptor(s) at the surface of human monocytes.

  11. Human activity recognition from wireless sensor network data: benchmark and software

    NARCIS (Netherlands)

    van Kasteren, T.L.M.; Englebienne, G.; Kröse, B.J.A.; Chen, L.; Nugent, C.; Biswas, J.; Hoey, J.

    2011-01-01

    Although activity recognition is an active area of research no common benchmark for evaluating the performance of activity recognition methods exists. In this chapter we present the state of the art probabilistic models used in activity recognition and show their performance on several real world

  12. A development of the Human Factors Assessment Guide for the Study of Erroneous Human Behaviors in Nuclear Power Plants

    International Nuclear Information System (INIS)

    Oh, Yeon Ju; Lee, Yong Hee; Jang, Tong Il; Kim, Sa Kil

    2014-01-01

    The aim of this paper is to describe a human factors assessment guide for the study of the erroneous characteristic of operators in nuclear power plants (NPPs). We think there are still remaining the human factors issues such as an uneasy emotion, fatigue and stress, varying mental workload situation by digital environment, and various new type of unsafe response to digital interface for better decisions, although introducing an advanced main control room. These human factors issues may not be resolved through the current human reliability assessment which evaluates the total probability of a human error occurring throughout the completion of a specific task. This paper provides an assessment guide for the human factors issues a set of experimental methodology, and presents an assessment case of measurement and analysis especially from neuro physiology approach. It would be the most objective psycho-physiological research technique on human performance for a qualitative analysis considering the safety aspects. This paper can be trial to experimental assessment of erroneous behaviors and their influencing factors, and it can be used as an index for recognition and a method to apply human factors engineering V and V, which is required as a mandatory element of human factor engineering program plan for a NPP design

  13. A development of the Human Factors Assessment Guide for the Study of Erroneous Human Behaviors in Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Yeon Ju; Lee, Yong Hee; Jang, Tong Il; Kim, Sa Kil [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2014-08-15

    The aim of this paper is to describe a human factors assessment guide for the study of the erroneous characteristic of operators in nuclear power plants (NPPs). We think there are still remaining the human factors issues such as an uneasy emotion, fatigue and stress, varying mental workload situation by digital environment, and various new type of unsafe response to digital interface for better decisions, although introducing an advanced main control room. These human factors issues may not be resolved through the current human reliability assessment which evaluates the total probability of a human error occurring throughout the completion of a specific task. This paper provides an assessment guide for the human factors issues a set of experimental methodology, and presents an assessment case of measurement and analysis especially from neuro physiology approach. It would be the most objective psycho-physiological research technique on human performance for a qualitative analysis considering the safety aspects. This paper can be trial to experimental assessment of erroneous behaviors and their influencing factors, and it can be used as an index for recognition and a method to apply human factors engineering V and V, which is required as a mandatory element of human factor engineering program plan for a NPP design.

  14. Understanding the mechanisms of familiar voice-identity recognition in the human brain.

    Science.gov (United States)

    Maguinness, Corrina; Roswandowitz, Claudia; von Kriegstein, Katharina

    2018-03-31

    Humans have a remarkable skill for voice-identity recognition: most of us can remember many voices that surround us as 'unique'. In this review, we explore the computational and neural mechanisms which may support our ability to represent and recognise a unique voice-identity. We examine the functional architecture of voice-sensitive regions in the superior temporal gyrus/sulcus, and bring together findings on how these regions may interact with each other, and additional face-sensitive regions, to support voice-identity processing. We also contrast findings from studies on neurotypicals and clinical populations which have examined the processing of familiar and unfamiliar voices. Taken together, the findings suggest that representations of familiar and unfamiliar voices might dissociate in the human brain. Such an observation does not fit well with current models for voice-identity processing, which by-and-large assume a common sequential analysis of the incoming voice signal, regardless of voice familiarity. We provide a revised audio-visual integrative model of voice-identity processing which brings together traditional and prototype models of identity processing. This revised model includes a mechanism of how voice-identity representations are established and provides a novel framework for understanding and examining the potential differences in familiar and unfamiliar voice processing in the human brain. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. 24/7 security system: 60-FPS color EMCCD camera with integral human recognition

    Science.gov (United States)

    Vogelsong, T. L.; Boult, T. E.; Gardner, D. W.; Woodworth, R.; Johnson, R. C.; Heflin, B.

    2007-04-01

    An advanced surveillance/security system is being developed for unattended 24/7 image acquisition and automated detection, discrimination, and tracking of humans and vehicles. The low-light video camera incorporates an electron multiplying CCD sensor with a programmable on-chip gain of up to 1000:1, providing effective noise levels of less than 1 electron. The EMCCD camera operates in full color mode under sunlit and moonlit conditions, and monochrome under quarter-moonlight to overcast starlight illumination. Sixty frame per second operation and progressive scanning minimizes motion artifacts. The acquired image sequences are processed with FPGA-compatible real-time algorithms, to detect/localize/track targets and reject non-targets due to clutter under a broad range of illumination conditions and viewing angles. The object detectors that are used are trained from actual image data. Detectors have been developed and demonstrated for faces, upright humans, crawling humans, large animals, cars and trucks. Detection and tracking of targets too small for template-based detection is achieved. For face and vehicle targets the results of the detection are passed to secondary processing to extract recognition templates, which are then compared with a database for identification. When combined with pan-tilt-zoom (PTZ) optics, the resulting system provides a reliable wide-area 24/7 surveillance system that avoids the high life-cycle cost of infrared cameras and image intensifiers.

  16. Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

    Science.gov (United States)

    Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

    2017-02-27

    In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

  17. High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections.

    Science.gov (United States)

    Zhu, Xiangbin; Qiu, Huiling

    2016-01-01

    Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved.

  18. Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2017-02-01

    Full Text Available In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

  19. High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections.

    Directory of Open Access Journals (Sweden)

    Xiangbin Zhu

    Full Text Available Human activity recognition(HAR from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved.

  20. Innate recognition of bacteria in human milk is mediated by a milk-derived highly expressed pattern recognition receptor, soluble CD14.

    OpenAIRE

    Lab?ta, MO; Vidal, K; Nores, JE; Arias, M; Vita, N; Morgan, BP; Guillemot, JC; Loyaux, D; Ferrara, P; Schmid, D; Affolter, M; Borysiewicz, LK; Donnet-Hughes, A; Schiffrin, EJ

    2000-01-01

    Little is known about innate immunity to bacteria after birth in the hitherto sterile fetal intestine. Breast-feeding has long been associated with a lower incidence of gastrointestinal infections and inflammatory and allergic diseases. We found in human breast milk a 48-kD polypeptide, which we confirmed by mass spectrometry and sequencing to be a soluble form of the bacterial pattern recognition receptor CD14 (sCD14). Milk sCD14 (m-sCD14) concentrations were up to 20-fold higher than serum ...

  1. Innate Recognition of Bacteria in Human Milk Is Mediated by a Milk-Derived Highly Expressed Pattern Recognition Receptor, Soluble Cd14

    OpenAIRE

    Labéta, Mario O.; Vidal, Karine; Nores, Julia E. Rey; Arias, Mauricio; Vita, Natalio; Morgan, B. Paul; Guillemot, Jean Claude; Loyaux, Denis; Ferrara, Pascual; Schmid, Daniel; Affolter, Michael; Borysiewicz, Leszek K.; Donnet-Hughes, Anne; Schiffrin, Eduardo J.

    2000-01-01

    Little is known about innate immunity to bacteria after birth in the hitherto sterile fetal intestine. Breast-feeding has long been associated with a lower incidence of gastrointestinal infections and inflammatory and allergic diseases. We found in human breast milk a 48-kD polypeptide, which we confirmed by mass spectrometry and sequencing to be a soluble form of the bacterial pattern recognition receptor CD14 (sCD14). Milk sCD14 (m-sCD14) concentrations were up to 20-fold higher than serum ...

  2. Humanized TLR4/MD-2 mice reveal LPS recognition differentially impacts susceptibility to Yersinia pestis and Salmonella enterica.

    Directory of Open Access Journals (Sweden)

    Adeline M Hajjar

    Full Text Available Although lipopolysaccharide (LPS stimulation through the Toll-like receptor (TLR-4/MD-2 receptor complex activates host defense against Gram-negative bacterial pathogens, how species-specific differences in LPS recognition impact host defense remains undefined. Herein, we establish how temperature dependent shifts in the lipid A of Yersinia pestis LPS that differentially impact recognition by mouse versus human TLR4/MD-2 dictate infection susceptibility. When grown at 37°C, Y. pestis LPS is hypo-acylated and less stimulatory to human compared with murine TLR4/MD-2. By contrast, when grown at reduced temperatures, Y. pestis LPS is more acylated, and stimulates cells equally via human and mouse TLR4/MD-2. To investigate how these temperature dependent shifts in LPS impact infection susceptibility, transgenic mice expressing human rather than mouse TLR4/MD-2 were generated. We found the increased susceptibility to Y. pestis for "humanized" TLR4/MD-2 mice directly paralleled blunted inflammatory cytokine production in response to stimulation with purified LPS. By contrast, for other Gram-negative pathogens with highly acylated lipid A including Salmonella enterica or Escherichia coli, infection susceptibility and the response after stimulation with LPS were indistinguishable between mice expressing human or mouse TLR4/MD-2. Thus, Y. pestis exploits temperature-dependent shifts in LPS acylation to selectively evade recognition by human TLR4/MD-2 uncovered with "humanized" TLR4/MD-2 transgenic mice.

  3. Humanized TLR4/MD-2 mice reveal LPS recognition differentially impacts susceptibility to Yersinia pestis and Salmonella enterica.

    Science.gov (United States)

    Hajjar, Adeline M; Ernst, Robert K; Fortuno, Edgardo S; Brasfield, Alicia S; Yam, Cathy S; Newlon, Lindsay A; Kollmann, Tobias R; Miller, Samuel I; Wilson, Christopher B

    2012-01-01

    Although lipopolysaccharide (LPS) stimulation through the Toll-like receptor (TLR)-4/MD-2 receptor complex activates host defense against Gram-negative bacterial pathogens, how species-specific differences in LPS recognition impact host defense remains undefined. Herein, we establish how temperature dependent shifts in the lipid A of Yersinia pestis LPS that differentially impact recognition by mouse versus human TLR4/MD-2 dictate infection susceptibility. When grown at 37°C, Y. pestis LPS is hypo-acylated and less stimulatory to human compared with murine TLR4/MD-2. By contrast, when grown at reduced temperatures, Y. pestis LPS is more acylated, and stimulates cells equally via human and mouse TLR4/MD-2. To investigate how these temperature dependent shifts in LPS impact infection susceptibility, transgenic mice expressing human rather than mouse TLR4/MD-2 were generated. We found the increased susceptibility to Y. pestis for "humanized" TLR4/MD-2 mice directly paralleled blunted inflammatory cytokine production in response to stimulation with purified LPS. By contrast, for other Gram-negative pathogens with highly acylated lipid A including Salmonella enterica or Escherichia coli, infection susceptibility and the response after stimulation with LPS were indistinguishable between mice expressing human or mouse TLR4/MD-2. Thus, Y. pestis exploits temperature-dependent shifts in LPS acylation to selectively evade recognition by human TLR4/MD-2 uncovered with "humanized" TLR4/MD-2 transgenic mice.

  4. The right of public access to legal information : A proposal for its universal recognition as a human right

    NARCIS (Netherlands)

    Mitee, Leesi Ebenezer

    2017-01-01

    Abstract: This Article examines the desirability of the universal recognition of the right of public access to legal information as a human right and therefore as part of a legal framework for improving national and global access to legal information. It discusses the right of public access to legal

  5. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human--Robot Interaction

    Directory of Open Access Journals (Sweden)

    Tatsuro Yamada

    2016-07-01

    Full Text Available To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language--behavior relationships and the temporal patterns of interaction. Here, ``internal dynamics'' refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language--behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language--behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

  6. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human-Robot Interaction.

    Science.gov (United States)

    Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

    2016-01-01

    To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language-behavior relationships and the temporal patterns of interaction. Here, "internal dynamics" refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language-behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language-behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

  7. A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Carlos E. Galván-Tejada

    2018-02-01

    Full Text Available The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc. were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS that uses contextual information from human activities (identified with data provided from the environmental sound can achieve an estimation that is 95% correct.

  8. Acoustic signature recognition technique for Human-Object Interactions (HOI) in persistent surveillance systems

    Science.gov (United States)

    Alkilani, Amjad; Shirkhodaie, Amir

    2013-05-01

    Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

  9. Fraudulent ID using face morphs: Experiments on human and automatic recognition.

    Science.gov (United States)

    Robertson, David J; Kramer, Robin S S; Burton, A Mike

    2017-01-01

    Matching unfamiliar faces is known to be difficult, and this can give an opportunity to those engaged in identity fraud. Here we examine a relatively new form of fraud, the use of photo-ID containing a graphical morph between two faces. Such a document may look sufficiently like two people to serve as ID for both. We present two experiments with human viewers, and a third with a smartphone face recognition system. In Experiment 1, viewers were asked to match pairs of faces, without being warned that one of the pair could be a morph. They very commonly accepted a morphed face as a match. However, in Experiment 2, following very short training on morph detection, their acceptance rate fell considerably. Nevertheless, there remained large individual differences in people's ability to detect a morph. In Experiment 3 we show that a smartphone makes errors at a similar rate to 'trained' human viewers-i.e. accepting a small number of morphs as genuine ID. We discuss these results in reference to the use of face photos for security.

  10. From Annotated Multimodal Corpora to Simulated Human-Like Behaviors

    DEFF Research Database (Denmark)

    Rehm, Matthias; André, Elisabeth

    2008-01-01

    Multimodal corpora prove useful at different stages of the development process of embodied conversational agents. Insights into human-human communicative behaviors can be drawn from such corpora. Rules for planning and generating such behavior in agents can be derived from this information....... And even the evaluation of human-agent interactions can rely on corpus data from human-human communication. In this paper, we exemplify how corpora can be exploited at the different development steps, starting with the question of how corpora are annotated and on what level of granularity. The corpus data...

  11. Human Behavioral Contributions to Climate Change: Psychological and Contextual Drivers

    Science.gov (United States)

    Swim, Janet K.; Clayton, Susan; Howard, George S.

    2011-01-01

    We are facing rapid changes in the global climate, and these changes are attributable to human behavior. Humans produce this global impact through our use of natural resources, multiplied by the vast increase in population seen in the past 50 to 100 years. Our goal in this article is to examine the underlying psychosocial causes of human impact,…

  12. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

    Directory of Open Access Journals (Sweden)

    Juan Carlos Davila

    2017-06-01

    Full Text Available The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  13. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

    Science.gov (United States)

    Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

    2017-06-07

    The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  14. Improving intercultural competency in global IT projects through recognition of culture-based behaviors

    Directory of Open Access Journals (Sweden)

    Richard Amster

    2016-01-01

    Full Text Available The success of global IT projects is highly influenced by culture-based behaviors. Issues between individuals arise when behaviors are (mis-perceived, (mis-interpreted, and (mis-judged by using the perceiver’s expectations, beliefs, and values. Misperception results when the behavior is not anticipated because it would not occur in ones own culture. As a result, behavior should be the starting point for cross-cultural research. But, studies have primarily focused on belief and value systems which are more abstract and less specific than behaviors. This paper presents a study that analyzed cultural behavioral differences between Indian project managers and their counterparts in other countries. The conducted qualitative, semi-structured interviews revealed insights into cross-cultural challenges and shed light on the complex ways that culture-based behaviors impact IT projects. The study identified 127 behaviors that significantly affected project success and cross-cultural cooperation between Indian managers and managers from all over the world. These behaviors were grouped into 19 behavior clusters. Understanding these behavior clusters, and correlating these behaviors to values and beliefs, will improve project collaboration, and inform cross-cultural training strategies. In addition, existing cultural dimensions were reduced in scope, additional dimensions were defined for clarity, and new business-related dimensions were identified. Finally, based on the study’s results, the paper suggests four important components that should be added to cross-cultural training programs for international project managers.

  15. Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

    OpenAIRE

    Huiyu Chen; Chao Yang; Xiangdong Xu

    2017-01-01

    Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR) data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spati...

  16. Rasmussen's model of human behavior in laparoscopy training.

    Science.gov (United States)

    Wentink, M; Stassen, L P S; Alwayn, I; Hosman, R J A W; Stassen, H G

    2003-08-01

    Compared to aviation, where virtual reality (VR) training has been standardized and simulators have proven their benefits, the objectives, needs, and means of VR training in minimally invasive surgery (MIS) still have to be established. The aim of the study presented is to introduce Rasmussen's model of human behavior as a practical framework for the definition of the training objectives, needs, and means in MIS. Rasmussen distinguishes three levels of human behavior: skill-, rule-, and knowledge-based behaviour. The training needs of a laparoscopic novice can be determined by identifying the specific skill-, rule-, and knowledge-based behavior that is required for performing safe laparoscopy. Future objectives of VR laparoscopy trainers should address all three levels of behavior. Although most commercially available simulators for laparoscopy aim at training skill-based behavior, especially the training of knowledge-based behavior during complications in surgery will improve safety levels. However, the cost and complexity of a training means increases when the training objectives proceed from the training of skill-based behavior to the training of complex knowledge-based behavior. In aviation, human behavior models have been used successfully to integrate the training of skill-, rule-, and knowledge-based behavior in a full flight simulator. Understanding surgeon behavior is one of the first steps towards a future full-scale laparoscopy simulator.

  17. First contacts and the common behavior of human beings

    OpenAIRE

    Van Brakel, Jaap

    2005-01-01

    In this paper my aim is to shed light on the common behavior of human beings by looking at '' first contacts '': the situation where people with unshared histories first meet (who don't speak one an others' language, don't have access to interpreters, etc.). The limits of the human life form are given by what is similar in the common behavior(s) of human beings. But what is similar should not be understood as something that is biologically or psychologically or transcendentally shared by all ...

  18. UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

    Directory of Open Access Journals (Sweden)

    Daniela Micucci

    2017-10-01

    Full Text Available Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects. Regarding the

  19. Fatigue Crack Growth Behavior of and Recognition of AE Signals from Composite Patch-Repaired Aluminum Panel

    International Nuclear Information System (INIS)

    Kim, Sung Jin; Kwon, Oh Yang; Jang, Yong Joon

    2007-01-01

    The fatigue crack growth behavior of a cracked and patch-repaired Ah2024-T3 panel has been monitored by acoustic emission(AE). The overall crack growth rate was reduced The crack propagation into the adjacent hole was also retarded by introducing the patch repair. AE signals due to crack growth after the patch repair and those due to debonding of the plate-patch interface were discriminated by using the principal component analysis. The former showed high center frequency and low amplitude, whereas the latter showed long rise tine, low frequency and high amplitude. This type of AE signal recognition method could be effective for the prediction of fatigue crack growth behavior in the patch-repaired structures with the aid of AE source location

  20. Under what conditions is recognition spared relative to recall after selective hippocampal damage in humans?

    Science.gov (United States)

    Holdstock, J S; Mayes, A R; Roberts, N; Cezayirli, E; Isaac, C L; O'Reilly, R C; Norman, K A

    2002-01-01

    The claim that recognition memory is spared relative to recall after focal hippocampal damage has been disputed in the literature. We examined this claim by investigating object and object-location recall and recognition memory in a patient, YR, who has adult-onset selective hippocampal damage. Our aim was to identify the conditions under which recognition was spared relative to recall in this patient. She showed unimpaired forced-choice object recognition but clearly impaired recall, even when her control subjects found the object recognition task to be numerically harder than the object recall task. However, on two other recognition tests, YR's performance was not relatively spared. First, she was clearly impaired at an equivalently difficult yes/no object recognition task, but only when targets and foils were very similar. Second, YR was clearly impaired at forced-choice recognition of object-location associations. This impairment was also unrelated to difficulty because this task was no more difficult than the forced-choice object recognition task for control subjects. The clear impairment of yes/no, but not of forced-choice, object recognition after focal hippocampal damage, when targets and foils are very similar, is predicted by the neural network-based Complementary Learning Systems model of recognition. This model postulates that recognition is mediated by hippocampally dependent recollection and cortically dependent familiarity; thus hippocampal damage should not impair item familiarity. The model postulates that familiarity is ineffective when very similar targets and foils are shown one at a time and subjects have to identify which items are old (yes/no recognition). In contrast, familiarity is effective in discriminating which of similar targets and foils, seen together, is old (forced-choice recognition). Independent evidence from the remember/know procedure also indicates that YR's familiarity is normal. The Complementary Learning Systems model can

  1. Modeling and simulating human teamwork behaviors using intelligent agents

    Science.gov (United States)

    Fan, Xiaocong; Yen, John

    2004-12-01

    Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively gathering, fusing, and sharing information, and for building coherent teams with both humans and agents working effectively on intelligence-intensive problems. Teamwork modeling is also challenging because the research has spanned diverse disciplines from business management to cognitive science, human discourse, and distributed artificial intelligence. This article presents an extensive, but not exhaustive, list of work in the field, where the taxonomy is organized along two main dimensions: team social structure and social behaviors. Along the dimension of social structure, we consider agent-only teams and mixed human-agent teams. Along the dimension of social behaviors, we consider collaborative behaviors, communicative behaviors, helping behaviors, and the underpinning of effective teamwork-shared mental models. The contribution of this article is that it presents an organizational framework for analyzing a variety of teamwork simulation systems and for further studying simulated teamwork behaviors.

  2. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Frédéric Li

    2018-02-01

    Full Text Available Getting a good feature representation of data is paramount for Human Activity Recognition (HAR using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM to obtain features characterising both short- and long-term time dependencies in the data.

  3. ATRX ADD domain links an atypical histone methylation recognition mechanism to human mental-retardation syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Iwase, Shigeki; Xiang, Bin; Ghosh, Sharmistha; Ren, Ting; Lewis, Peter W.; Cochrane, Jesse C.; Allis, C. David; Picketts, David J.; Patel, Dinshaw J.; Li, Haitao; Shi, Yang (Harvard-Med); (Ottawa Hosp.); (MSKCC); (Rockefeller); (CH-Boston); (Tsinghua); (Mass. Gen. Hosp.)

    2011-07-19

    ATR-X (alpha-thalassemia/mental retardation, X-linked) syndrome is a human congenital disorder that causes severe intellectual disabilities. Mutations in the ATRX gene, which encodes an ATP-dependent chromatin-remodeler, are responsible for the syndrome. Approximately 50% of the missense mutations in affected persons are clustered in a cysteine-rich domain termed ADD (ATRX-DNMT3-DNMT3L, ADD{sub ATRX}), whose function has remained elusive. Here we identify ADD{sub ATRX} as a previously unknown histone H3-binding module, whose binding is promoted by lysine 9 trimethylation (H3K9me3) but inhibited by lysine 4 trimethylation (H3K4me3). The cocrystal structure of ADD{sub ATRX} bound to H3{sub 1-15}K9me3 peptide reveals an atypical composite H3K9me3-binding pocket, which is distinct from the conventional trimethyllysine-binding aromatic cage. Notably, H3K9me3-pocket mutants and ATR-X syndrome mutants are defective in both H3K9me3 binding and localization at pericentromeric heterochromatin; thus, we have discovered a unique histone-recognition mechanism underlying the ATR-X etiology.

  4. ATRX ADD Domain Links an Atypical Histone Methylation Recognition Mechanism to Human Mental-Retardation Syndrome

    Energy Technology Data Exchange (ETDEWEB)

    S Iwase; B Xiang; S Ghosh; T Ren; P Lewis; J Cochrane; C Allis; D Picketts; D Patel; et al.

    2011-12-31

    ATR-X (alpha-thalassemia/mental retardation, X-linked) syndrome is a human congenital disorder that causes severe intellectual disabilities. Mutations in the ATRX gene, which encodes an ATP-dependent chromatin-remodeler, are responsible for the syndrome. Approximately 50% of the missense mutations in affected persons are clustered in a cysteine-rich domain termed ADD (ATRX-DNMT3-DNMT3L, ADD{sub ATRX}), whose function has remained elusive. Here we identify ADD{sub ATRX} as a previously unknown histone H3-binding module, whose binding is promoted by lysine 9 trimethylation (H3K9me3) but inhibited by lysine 4 trimethylation (H3K4me3). The cocrystal structure of ADD{sub ATRX} bound to H3{sub 1-15}K9me3 peptide reveals an atypical composite H3K9me3-binding pocket, which is distinct from the conventional trimethyllysine-binding aromatic cage. Notably, H3K9me3-pocket mutants and ATR-X syndrome mutants are defective in both H3K9me3 binding and localization at pericentromeric heterochromatin; thus, we have discovered a unique histone-recognition mechanism underlying the ATR-X etiology.

  5. Structure of Human GIVD Cytosolic Phospholipase A2 Reveals Insights into Substrate Recognition

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Hui; Klein, Michael G.; Snell, Gyorgy; Lane, Weston; Zou, Hua; Levin, Irena; Li, Ke; Sang, Bi-Ching (Takeda Cali)

    2016-07-01

    Cytosolic phospholipases A2 (cPLA2s) consist of a family of calcium-sensitive enzymes that function to generate lipid second messengers through hydrolysis of membrane-associated glycerophospholipids. The GIVD cPLA2 (cPLA2δ) is a potential drug target for developing a selective therapeutic agent for the treatment of psoriasis. Here, we present two X-ray structures of human cPLA2δ, capturing an apo state, and in complex with a substrate-like inhibitor. Comparison of the apo and inhibitor-bound structures reveals conformational changes in a flexible cap that allows the substrate to access the relatively buried active site, providing new insight into the mechanism for substrate recognition. The cPLA2δ structure reveals an unexpected second C2 domain that was previously unrecognized from sequence alignments, placing cPLA2δ into the class of membrane-associated proteins that contain a tandem pair of C2 domains. Furthermore, our structures elucidate novel inter-domain interactions and define three potential calcium-binding sites that are likely important for regulation and activation of enzymatic activity. These findings provide novel insights into the molecular mechanisms governing cPLA2's function in signal transduction.

  6. A method to deal with installation errors of wearable accelerometers for human activity recognition

    International Nuclear Information System (INIS)

    Jiang, Ming; Wang, Zhelong; Shang, Hong; Li, Hongyi; Wang, Yuechao

    2011-01-01

    Human activity recognition (HAR) by using wearable accelerometers has gained significant interest in recent years in a range of healthcare areas, including inferring metabolic energy expenditure, predicting falls, measuring gait parameters and monitoring daily activities. The implementation of HAR relies heavily on the correctness of sensor fixation. The installation errors of wearable accelerometers may dramatically decrease the accuracy of HAR. In this paper, a method is proposed to improve the robustness of HAR to the installation errors of accelerometers. The method first calculates a transformation matrix by using Gram–Schmidt orthonormalization in order to eliminate the sensor's orientation error and then employs a low-pass filter with a cut-off frequency of 10 Hz to eliminate the main effect of the sensor's misplacement. The experimental results showed that the proposed method obtained a satisfactory performance for HAR. The average accuracy rate from ten subjects was 95.1% when there were no installation errors, and was 91.9% when installation errors were involved in wearable accelerometers

  7. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.

    Science.gov (United States)

    Li, Frédéric; Shirahama, Kimiaki; Nisar, Muhammad Adeel; Köping, Lukas; Grzegorzek, Marcin

    2018-02-24

    Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

  8. Structure of the active form of human origin recognition complex and its ATPase motor module

    Energy Technology Data Exchange (ETDEWEB)

    Tocilj, Ante; On, Kin Fan; Yuan, Zuanning; Sun, Jingchuan; Elkayam, Elad; Li, Huilin; Stillman, Bruce; Joshua-Tor, Leemor

    2017-01-23

    Binding of the Origin Recognition Complex (ORC) to origins of replication marks the first step in the initiation of replication of the genome in all eukaryotic cells. Here, we report the structure of the active form of human ORC determined by X-ray crystallography and cryo-electron microscopy. The complex is composed of an ORC1/4/5 motor module lobe in an organization reminiscent of the DNA polymerase clamp loader complexes. A second lobe contains the ORC2/3 subunits. The complex is organized as a double-layered shallow corkscrew, with the AAA+ and AAA+-like domains forming one layer, and the winged-helix domains (WHDs) forming a top layer. CDC6 fits easily between ORC1 and ORC2, completing the ring and the DNA-binding channel, forming an additional ATP hydrolysis site. Analysis of the ATPase activity of the complex provides a basis for understanding ORC activity as well as molecular defects observed in Meier-Gorlin Syndrome mutations.

  9. Science and Human Behavior, dualism, and conceptual modification.

    Science.gov (United States)

    Zuriff, G E

    2003-11-01

    Skinner's Science and Human Behavior is in part an attempt to solve psychology's problem with mind-body dualism by revising our everyday mentalistic conceptual scheme. In the case of descriptive mentalism (the use of mentalistic terms to describe behavior), Skinner offers behavioral "translations." In contrast, Skinner rejects explanatory mentalism (the use of mental concepts to explain behavior) and suggests how to replace it with a behaviorist explanatory framework. For experiential mentalism, Skinner presents a theory of verbal behavior that integrates the use of mentalistic language in first-person reports of phenomenal experience into a scientific framework.

  10. Human surfactant protein D: SP-D contains a C-type lectin carbohydrate recognition domain.

    Science.gov (United States)

    Rust, K; Grosso, L; Zhang, V; Chang, D; Persson, A; Longmore, W; Cai, G Z; Crouch, E

    1991-10-01

    Lung surfactant protein D (SP-D) shows calcium-dependent binding to specific saccharides, and is similar in domain structure to certain members of the calcium-dependent (C-type) lectin family. Using a degenerate oligomeric probe corresponding to a conserved peptide sequence derived from the amino-terminus of the putative carbohydrate binding domain of rat and bovine SP-D, we screened a human lung cDNA library and isolated a 1.4-kb cDNA for the human protein. The relationship of the cDNA to SP-D was established by several techniques including amino-terminal microsequencing of SP-D-derived peptides, and immunoprecipitation of translation products of transcribed mRNA with monospecific antibodies to SP-D. In addition, antibodies to a synthetic peptide derived from a predicted unique epitope within the carbohydrate recognition domain of SP-D specifically reacted with SP-D. DNA sequencing demonstrated a noncollagenous carboxy-terminal domain that is highly homologous with the carboxy-terminal globular domain of previously described C-type lectins. This domain contains all of the so-called "invariant residues," including four conserved cysteine residues, and shows high homology with the mannose-binding subfamily of C-type lectins. Sequencing also demonstrated an amino-terminal collagenous domain that contains an uninterrupted sequence of 59 Gly-X-Y triplets and that also contains the only identified consensus for asparagine-linked oligosaccharides. The studies demonstrate that SP-D is a member of the C-type lectin family, and confirm predicted structural similarities to conglutinin, SP-D, and the serum mannose binding proteins.

  11. Recognition and stabilization of geranylgeranylated human Rab5 by the GDP Dissociation Inhibitor (GDI).

    Science.gov (United States)

    Edler, Eileen; Stein, Matthias

    2017-10-25

    The small GTPase Rab5 is the key regulator of early endosomal fusion. It is post-translationally modified by covalent attachment of two geranylgeranyl (GG) chains to adjacent cysteine residues of the C-terminal hypervariable region (HVR). The GDP dissociation inhibitor (GDI) recognizes membrane-associated Rab5(GDP) and serves to release it into the cytoplasm where it is kept in a soluble state. A detailed new structural and dynamic model for human Rab5(GDP) recognition and binding with human GDI at the early endosome membrane and in its dissociated state is presented. In the cytoplasm, the GDI protein accommodates the GG chains in a transient hydrophobic binding pocket. In solution, two different binding modes of the isoprenoid chains inserted into the hydrophobic pocket of the Rab5(GDP):GDI complex can be identified. This equilibrium between the two states helps to stabilize the protein-protein complex in solution. Interprotein contacts between the Rab5 switch regions and characteristic patches of GDI residues from the Rab binding platform (RBP) and the C-terminus coordinating region (CCR) reveal insight on the formation of such a stable complex. GDI binding to membrane-anchored Rab5(GDP) is initially mediated by the solvent accessible switch regions of the Rab-specific RBP. Formation of the membrane-associated Rab5(GDP):GDI complex induces a GDI reorientation to establish additional interactions with the Rab5 HVR. These results allow to devise a detailed structural model for the process of extraction of GG-Rab5(GDP) by GDI from the membrane and the dissociation from targeting factors and effector proteins prior to GDI binding.

  12. Human gestation-associated tissues express functional cytosolic nucleic acid sensing pattern recognition receptors.

    Science.gov (United States)

    Bryant, A H; Menzies, G E; Scott, L M; Spencer-Harty, S; Davies, L B; Smith, R A; Jones, R H; Thornton, C A

    2017-07-01

    The role of viral infections in adverse pregnancy outcomes has gained interest in recent years. Innate immune pattern recognition receptors (PRRs) and their signalling pathways, that yield a cytokine output in response to pathogenic stimuli, have been postulated to link infection at the maternal-fetal interface and adverse pregnancy outcomes. The objective of this study was to investigate the expression and functional response of nucleic acid ligand responsive Toll-like receptors (TLR-3, -7, -8 and -9), and retinoic acid-inducible gene 1 (RIG-I)-like receptors [RIG-I, melanoma differentiation-associated protein 5 (MDA5) and Laboratory of Genetics and Physiology 2(LGP2)] in human term gestation-associated tissues (placenta, choriodecidua and amnion) using an explant model. Immunohistochemistry revealed that these PRRs were expressed by the term placenta, choriodecidua and amnion. A statistically significant increase in interleukin (IL)-6 and/or IL-8 production in response to specific agonists for TLR-3 (Poly(I:C); low and high molecular weight), TLR-7 (imiquimod), TLR-8 (ssRNA40) and RIG-I/MDA5 (Poly(I:C)LyoVec) was observed; there was no response to a TLR-9 (ODN21798) agonist. A hierarchical clustering approach was used to compare the response of each tissue type to the ligands studied and revealed that the placenta and choriodecidua generate a more similar IL-8 response, while the choriodecidua and amnion generate a more similar IL-6 response to nucleic acid ligands. These findings demonstrate that responsiveness via TLR-3, TLR-7, TLR-8 and RIG-1/MDA5 is a broad feature of human term gestation-associated tissues with differential responses by tissue that might underpin adverse obstetric outcomes. © 2017 British Society for Immunology.

  13. A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

    Directory of Open Access Journals (Sweden)

    Ahmad Jalal

    2014-07-01

    Full Text Available Recent advancements in depth video sensors technologies have made human activity recognition (HAR realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

  14. A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments.

    Science.gov (United States)

    Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

    2014-07-02

    Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

  15. Discrete time modelization of human pilot behavior

    Science.gov (United States)

    Cavalli, D.; Soulatges, D.

    1975-01-01

    This modelization starts from the following hypotheses: pilot's behavior is a time discrete process, he can perform only one task at a time and his operating mode depends on the considered flight subphase. Pilot's behavior was observed using an electro oculometer and a simulator cockpit. A FORTRAN program has been elaborated using two strategies. The first one is a Markovian process in which the successive instrument readings are governed by a matrix of conditional probabilities. In the second one, strategy is an heuristic process and the concepts of mental load and performance are described. The results of the two aspects have been compared with simulation data.

  16. Context Effects on Facial Affect Recognition in Schizophrenia and Autism: Behavioral and Eye-Tracking Evidence.

    Science.gov (United States)

    Sasson, Noah J; Pinkham, Amy E; Weittenhiller, Lauren P; Faso, Daniel J; Simpson, Claire

    2016-05-01

    Although Schizophrenia (SCZ) and Autism Spectrum Disorder (ASD) share impairments in emotion recognition, the mechanisms underlying these impairments may differ. The current study used the novel "Emotions in Context" task to examine how the interpretation and visual inspection of facial affect is modulated by congruent and incongruent emotional contexts in SCZ and ASD. Both adults with SCZ (n= 44) and those with ASD (n= 21) exhibited reduced affect recognition relative to typically-developing (TD) controls (n= 39) when faces were integrated within broader emotional scenes but not when they were presented in isolation, underscoring the importance of using stimuli that better approximate real-world contexts. Additionally, viewing faces within congruent emotional scenes improved accuracy and visual attention to the face for controls more so than the clinical groups, suggesting that individuals with SCZ and ASD may not benefit from the presence of complementary emotional information as readily as controls. Despite these similarities, important distinctions between SCZ and ASD were found. In every condition, IQ was related to emotion-recognition accuracy for the SCZ group but not for the ASD or TD groups. Further, only the ASD group failed to increase their visual attention to faces in incongruent emotional scenes, suggesting a lower reliance on facial information within ambiguous emotional contexts relative to congruent ones. Collectively, these findings highlight both shared and distinct social cognitive processes in SCZ and ASD that may contribute to their characteristic social disabilities. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  17. Timing of Multimodal Robot Behaviors during Human-Robot Collaboration

    DEFF Research Database (Denmark)

    Jensen, Lars Christian; Fischer, Kerstin; Suvei, Stefan-Daniel

    2017-01-01

    In this paper, we address issues of timing between robot behaviors in multimodal human-robot interaction. In particular, we study what effects sequential order and simultaneity of robot arm and body movement and verbal behavior have on the fluency of interactions. In a study with the Care-O-bot, ...... output plays a special role because participants carry their expectations from human verbal interaction into the interactions with robots....

  18. Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language

    Science.gov (United States)

    Narayanan, Shrikanth; Georgiou, Panayiotis G.

    2013-01-01

    The expression and experience of human behavior are complex and multimodal and characterized by individual and contextual heterogeneity and variability. Speech and spoken language communication cues offer an important means for measuring and modeling human behavior. Observational research and practice across a variety of domains from commerce to healthcare rely on speech- and language-based informatics for crucial assessment and diagnostic information and for planning and tracking response to an intervention. In this paper, we describe some of the opportunities as well as emerging methodologies and applications of human behavioral signal processing (BSP) technology and algorithms for quantitatively understanding and modeling typical, atypical, and distressed human behavior with a specific focus on speech- and language-based communicative, affective, and social behavior. We describe the three important BSP components of acquiring behavioral data in an ecologically valid manner across laboratory to real-world settings, extracting and analyzing behavioral cues from measured data, and developing models offering predictive and decision-making support. We highlight both the foundational speech and language processing building blocks as well as the novel processing and modeling opportunities. Using examples drawn from specific real-world applications ranging from literacy assessment and autism diagnostics to psychotherapy for addiction and marital well being, we illustrate behavioral informatics applications of these signal processing techniques that contribute to quantifying higher level, often subjectively described, human behavior in a domain-sensitive fashion. PMID:24039277

  19. Human Nonverbal Behaviors, Empathy, and Film.

    Science.gov (United States)

    Wiggers, T. Thorne

    Nonverbal behavior is an important aspect of the film and is one of the several tools that a director uses to communicate to an audience the characters' feelings and relationships. By adding to this information their own personal responses, viewers often experience strong feelings. With reference to the social psychological research of nonverbal…

  20. Anomalous human behavior detection: An Adaptive approach

    NARCIS (Netherlands)

    Leeuwen, C. van; Halma, A.; Schutte, K.

    2013-01-01

    Detection of anomalies (outliers or abnormal instances) is an important element in a range of applications such as fault, fraud, suspicious behavior detection and knowledge discovery. In this article we propose a new method for anomaly detection and performed tested its ability to detect anomalous

  1. Facing History and Ourselves: Holocaust and Human Behavior.

    Science.gov (United States)

    Strom, Margot Stern; Parsons, William S.

    This unit for junior and senior high school students presents techniques and materials for studying about the holocaust of World War II. Emphasis in the guide is on human behavior and the role of the individual within society. Among the guide's 18 objectives are for students to examine society's influence on individual behavior, place Hitler's…

  2. Human Computing and Machine Understanding of Human Behavior: A Survey

    NARCIS (Netherlands)

    Pantic, Maja; Pentland, Alex; Nijholt, Antinus; Huang, Thomas; Quek, F.; Yang, Yie

    2006-01-01

    A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should

  3. Speech recognition technology: an outlook for human-to-machine interaction.

    Science.gov (United States)

    Erdel, T; Crooks, S

    2000-01-01

    Speech recognition, as an enabling technology in healthcare-systems computing, is a topic that has been discussed for quite some time, but is just now coming to fruition. Traditionally, speech-recognition software has been constrained by hardware, but improved processors and increased memory capacities are starting to remove some of these limitations. With these barriers removed, companies that create software for the healthcare setting have the opportunity to write more successful applications. Among the criticisms of speech-recognition applications are the high rates of error and steep training curves. However, even in the face of such negative perceptions, there remains significant opportunities for speech recognition to allow healthcare providers and, more specifically, physicians, to work more efficiently and ultimately spend more time with their patients and less time completing necessary documentation. This article will identify opportunities for inclusion of speech-recognition technology in the healthcare setting and examine major categories of speech-recognition software--continuous speech recognition, command and control, and text-to-speech. We will discuss the advantages and disadvantages of each area, the limitations of the software today, and how future trends might affect them.

  4. Human body scents: do they influence our behavior?

    Science.gov (United States)

    Mildner, Sophie; Buchbauer, Gerhard

    2013-11-01

    Pheromonal communication in the animal world has been of great research interest for a long time. While extraordinary discoveries in this field have been made, the importance of the human sense of smell was of far lower interest. Humans are seen as poor smellers and therefore research about human olfaction remains quite sparse compared with other animals. Nevertheless amazing achievements have been made during the past 15 years. This is a collection of available data on this topic and a controversial discussion on the role of putative human pheromones in our modem way of living. While the focus was definitely put on behavioral changes evoked by putative human pheromones this article also includes other important aspects such as the possible existence of a human vomeronasal organ. If pheromones do have an influence on human behavior there has to be a receptor organ. How are human body scents secreted and turned into odorous substances? And how can con-specifics detect those very odors and transmit them to the brain? Apart from that the most likely candidates for human pheromones are taken on account and their impact on human behavior is shown in various detail.

  5. Reconceptualizing Social Work Behaviors from a Human Rights Perspective

    Science.gov (United States)

    Steen, Julie A.

    2018-01-01

    Although the human rights philosophy has relevance for many segments of the social work curriculum, the latest version of accreditation standards only includes a few behaviors specific to human rights. This deficit can be remedied by incorporating innovations found in the social work literature, which provides a wealth of material for…

  6. The development of human behavior analysis techniques

    International Nuclear Information System (INIS)

    Lee, Jung Woon; Lee, Yong Hee; Park, Geun Ok; Cheon, Se Woo; Suh, Sang Moon; Oh, In Suk; Lee, Hyun Chul; Park, Jae Chang.

    1997-07-01

    In this project, which is to study on man-machine interaction in Korean nuclear power plants, we developed SACOM (Simulation Analyzer with a Cognitive Operator Model), a tool for the assessment of task performance in the control rooms using software simulation, and also develop human error analysis and application techniques. SACOM was developed to assess operator's physical workload, workload in information navigation at VDU workstations, and cognitive workload in procedural tasks. We developed trip analysis system including a procedure based on man-machine interaction analysis system including a procedure based on man-machine interaction analysis and a classification system. We analyzed a total of 277 trips occurred from 1978 to 1994 to produce trip summary information, and for 79 cases induced by human errors time-lined man-machine interactions. The INSTEC, a database system of our analysis results, was developed. The MARSTEC, a multimedia authoring and representation system for trip information, was also developed, and techniques for human error detection in human factors experiments were established. (author). 121 refs., 38 tabs., 52 figs

  7. The development of human behavior analysis techniques

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jung Woon; Lee, Yong Hee; Park, Geun Ok; Cheon, Se Woo; Suh, Sang Moon; Oh, In Suk; Lee, Hyun Chul; Park, Jae Chang

    1997-07-01

    In this project, which is to study on man-machine interaction in Korean nuclear power plants, we developed SACOM (Simulation Analyzer with a Cognitive Operator Model), a tool for the assessment of task performance in the control rooms using software simulation, and also develop human error analysis and application techniques. SACOM was developed to assess operator`s physical workload, workload in information navigation at VDU workstations, and cognitive workload in procedural tasks. We developed trip analysis system including a procedure based on man-machine interaction analysis system including a procedure based on man-machine interaction analysis and a classification system. We analyzed a total of 277 trips occurred from 1978 to 1994 to produce trip summary information, and for 79 cases induced by human errors time-lined man-machine interactions. The INSTEC, a database system of our analysis results, was developed. The MARSTEC, a multimedia authoring and representation system for trip information, was also developed, and techniques for human error detection in human factors experiments were established. (author). 121 refs., 38 tabs., 52 figs.

  8. Structural Basis of Substrate Recognition in Human Nicotinamide N-Methyltransferase

    Energy Technology Data Exchange (ETDEWEB)

    Peng, Yi; Sartini, Davide; Pozzi, Valentina; Wilk, Dennis; Emanuelli, Monica; Yee, Vivien C. (Case Western); (Politecnica Valencia)

    2012-05-02

    Nicotinamide N-methyltransferase (NNMT) catalyzes the N-methylation of nicotinamide, pyridines, and other analogues using S-adenosyl-L-methionine as donor. NNMT plays a significant role in the regulation of metabolic pathways and is expressed at markedly high levels in several kinds of cancers, presenting it as a potential molecular target for cancer therapy. We have determined the crystal structure of human NNMT as a ternary complex bound to both the demethylated donor S-adenosyl-L-homocysteine and the acceptor substrate nicotinamide, to 2.7 {angstrom} resolution. These studies reveal the structural basis for nicotinamide binding and highlight several residues in the active site which may play roles in nicotinamide recognition and NNMT catalysis. The functional importance of these residues was probed by mutagenesis. Of three residues near the nicotinamide's amide group, substitution of S201 and S213 had no effect on enzyme activity while replacement of D197 dramatically decreased activity. Substitutions of Y20, whose side chain hydroxyl interacts with both the nicotinamide aromatic ring and AdoHcy carboxylate, also compromised activity. Enzyme kinetics analysis revealed k{sub cat}/K{sub m} decreases of 2-3 orders of magnitude for the D197A and Y20A mutants, confirming the functional importance of these active site residues. The mutants exhibited substantially increased K{sub m} for both NCA and AdoMet and modestly decreased k{sub cat}. MD simulations revealed long-range conformational effects which provide an explanation for the large increase in K{sub m}(AdoMet) for the D197A mutant, which interacts directly only with nicotinamide in the ternary complex crystal structure.

  9. Arousal Rather than Basic Emotions Influence Long-Term Recognition Memory in Humans.

    Science.gov (United States)

    Marchewka, Artur; Wypych, Marek; Moslehi, Abnoos; Riegel, Monika; Michałowski, Jarosław M; Jednoróg, Katarzyna

    2016-01-01

    Emotion can influence various cognitive processes, however its impact on memory has been traditionally studied over relatively short retention periods and in line with dimensional models of affect. The present study aimed to investigate emotional effects on long-term recognition memory according to a combined framework of affective dimensions and basic emotions. Images selected from the Nencki Affective Picture System were rated on the scale of affective dimensions and basic emotions. After 6 months, subjects took part in a surprise recognition test during an fMRI session. The more negative the pictures the better they were remembered, but also the more false recognitions they provoked. Similar effects were found for the arousal dimension. Recognition success was greater for pictures with lower intensity of happiness and with higher intensity of surprise, sadness, fear, and disgust. Consecutive fMRI analyses showed a significant activation for remembered (recognized) vs. forgotten (not recognized) images in anterior cingulate and bilateral anterior insula as well as in bilateral caudate nuclei and right thalamus. Further, arousal was found to be the only subjective rating significantly modulating brain activation. Higher subjective arousal evoked higher activation associated with memory recognition in the right caudate and the left cingulate gyrus. Notably, no significant modulation was observed for other subjective ratings, including basic emotion intensities. These results emphasize the crucial role of arousal for long-term recognition memory and support the hypothesis that the memorized material, over time, becomes stored in a distributed cortical network including the core salience network and basal ganglia.

  10. A Culture-Behavior-Brain Loop Model of Human Development.

    Science.gov (United States)

    Han, Shihui; Ma, Yina

    2015-11-01

    Increasing evidence suggests that cultural influences on brain activity are associated with multiple cognitive and affective processes. These findings prompt an integrative framework to account for dynamic interactions between culture, behavior, and the brain. We put forward a culture-behavior-brain (CBB) loop model of human development that proposes that culture shapes the brain by contextualizing behavior, and the brain fits and modifies culture via behavioral influences. Genes provide a fundamental basis for, and interact with, the CBB loop at both individual and population levels. The CBB loop model advances our understanding of the dynamic relationships between culture, behavior, and the brain, which are crucial for human phylogeny and ontogeny. Future brain changes due to cultural influences are discussed based on the CBB loop model. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Where to look? Automating attending behaviors of virtual human characters

    Science.gov (United States)

    Chopra Khullar, S.; Badler, N. I.

    2001-01-01

    This research proposes a computational framework for generating visual attending behavior in an embodied simulated human agent. Such behaviors directly control eye and head motions, and guide other actions such as locomotion and reach. The implementation of these concepts, referred to as the AVA, draws on empirical and qualitative observations known from psychology, human factors and computer vision. Deliberate behaviors, the analogs of scanpaths in visual psychology, compete with involuntary attention capture and lapses into idling or free viewing. Insights provided by implementing this framework are: a defined set of parameters that impact the observable effects of attention, a defined vocabulary of looking behaviors for certain motor and cognitive activity, a defined hierarchy of three levels of eye behavior (endogenous, exogenous and idling) and a proposed method of how these types interact.

  12. The multifactorial nature of human homosexual behavior: A brief review

    Directory of Open Access Journals (Sweden)

    Barona, Daniel

    2014-12-01

    Full Text Available Homosexual behavior has been analyzed as an evolutionary paradox in the biological context. In this review, we will try to compile the main genetic, epigenetic, hormonal, neurological and immune explanations of homosexuality, as well as the ultimate evolutionary causes of this complex behavior in the human being, incorporating information from studies in other animal species. All these factors determine the homosexual behavior, acting most of the times, simultaneously. Hereditary and non hereditary factors determine homosexual behavior, explaining its persistence despite its apparent disadvantages in relation to reproductive fitness.

  13. Human behavioral contributions to climate change: psychological and contextual drivers.

    Science.gov (United States)

    Swim, Janet K; Clayton, Susan; Howard, George S

    2011-01-01

    We are facing rapid changes in the global climate, and these changes are attributable to human behavior. Humans produce this global impact through our use of natural resources, multiplied by the vast increase in population seen in the past 50 to 100 years. Our goal in this article is to examine the underlying psychosocial causes of human impact, primarily through patterns of reproduction and consumption. We identify and distinguish individual, societal, and behavioral predictors of environmental impact. Relevant research in these areas (as well as areas that would be aided by greater attention by psychologists) are reviewed. We conclude by highlighting ethical issues that emerge when considering how to address human behavioral contributions to climate change.

  14. Human wagering behavior depends on opponents' faces.

    Directory of Open Access Journals (Sweden)

    Erik J Schlicht

    Full Text Available Research in competitive games has exclusively focused on how opponent models are developed through previous outcomes and how peoples' decisions relate to normative predictions. Little is known about how rapid impressions of opponents operate and influence behavior in competitive economic situations, although such subjective impressions have been shown to influence cooperative decision-making. This study investigates whether an opponent's face influences players' wagering decisions in a zero-sum game with hidden information. Participants made risky choices in a simplified poker task while being presented opponents whose faces differentially correlated with subjective impressions of trust. Surprisingly, we find that threatening face information has little influence on wagering behavior, but faces relaying positive emotional characteristics impact peoples' decisions. Thus, people took significantly longer and made more mistakes against emotionally positive opponents. Differences in reaction times and percent correct were greatest around the optimal decision boundary, indicating that face information is predominantly used when making decisions during medium-value gambles. Mistakes against emotionally positive opponents resulted from increased folding rates, suggesting that participants may have believed that these opponents were betting with hands of greater value than other opponents. According to these results, the best "poker face" for bluffing may not be a neutral face, but rather a face that contains emotional correlates of trustworthiness. Moreover, it suggests that rapid impressions of an opponent play an important role in competitive games, especially when people have little or no experience with an opponent.

  15. Acute fasting inhibits central caspase-1 activity reducing anxiety-like behavior and increasing novel object and object location recognition.

    Science.gov (United States)

    Towers, Albert E; Oelschlager, Maci L; Patel, Jay; Gainey, Stephen J; McCusker, Robert H; Freund, Gregory G

    2017-06-01

    Inflammation within the central nervous system (CNS) is frequently comorbid with anxiety. Importantly, the pro-inflammatory cytokine most commonly associated with anxiety is IL-1β. The bioavailability and activity of IL-1β are regulated by caspase-1-dependent proteolysis vis-a-vis the inflammasome. Thus, interventions regulating the activation or activity of caspase-1 should reduce anxiety especially in states that foster IL-1β maturation. Male C57BL/6j, C57BL/6j mice treated with the capase-1 inhibitor biotin-YVAD-cmk, caspase-1 knockout (KO) mice and IL-1R1 KO mice were fasted for 24h or allowed ad libitum access to food. Immediately after fasting, caspase-1 activity was measured in brain region homogenates while activated caspase-1 was localized in the brain by immunohistochemistry. Mouse anxiety-like behavior and cognition were tested using the elevated zero maze and novel object/object location tasks, respectively. A 24h fast in mice reduced the activity of caspase-1 in whole brain and in the prefrontal cortex, amygdala, hippocampus, and hypothalamus by 35%, 25%, 40%, 40%, and 40% respectively. A 24h fast also reduced anxiety-like behavior by 40% and increased novel object and object location recognition by 21% and 31%, respectively. IL-1β protein, however, was not reduced in the brain by fasting. ICV administration of YVAD decreased caspase-1 activity in the prefrontal cortex and amygdala by 55%, respectively leading to a 64% reduction in anxiety like behavior. Importantly, when caspase-1 KO or IL1-R1 KO mice are fasted, no fasting-dependent reduction in anxiety-like behavior was observed. Results indicate that fasting decrease anxiety-like behavior and improves memory by a mechanism tied to reducing caspase-1 activity throughout the brain. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Modulation of neonatal microbial recognition: TLR-mediated innate immune responses are specifically and differentially modulated by human milk.

    Science.gov (United States)

    LeBouder, Emmanuel; Rey-Nores, Julia E; Raby, Anne-Catherine; Affolter, Michael; Vidal, Karine; Thornton, Catherine A; Labéta, Mario O

    2006-03-15

    The mechanisms controlling innate microbial recognition in the neonatal gut are still to be fully understood. We have sought specific regulatory mechanisms operating in human breast milk relating to TLR-mediated microbial recognition. In this study, we report a specific and differential modulatory effect of early samples (days 1-5) of breast milk on ligand-induced cell stimulation via TLRs. Although a negative modulation was exerted on TLR2 and TLR3-mediated responses, those via TLR4 and TLR5 were enhanced. This effect was observed in human adult and fetal intestinal epithelial cell lines, monocytes, dendritic cells, and PBMC as well as neonatal blood. In the latter case, milk compensated for the low capacity of neonatal plasma to support responses to LPS. Cell stimulation via the IL-1R or TNFR was not modulated by milk. This, together with the differential effect on TLR activation, suggested that the primary effect of milk is exerted upstream of signaling proximal to TLR ligand recognition. The analysis of TLR4-mediated gene expression, used as a model system, showed that milk modulated TLR-related genes differently, including those coding for signal intermediates and regulators. A proteinaceous milk component of > or =80 kDa was found to be responsible for the effect on TLR4. Notably, infant milk formulations did not reproduce the modulatory activity of breast milk. Together, these findings reveal an unrecognized function of human milk, namely, its capacity to influence neonatal microbial recognition by modulating TLR-mediated responses specifically and differentially. This in turn suggests the existence of novel mechanisms regulating TLR activation.

  17. Vision-Based Navigation and Recognition

    National Research Council Canada - National Science Library

    Rosenfeld, Azriel

    1998-01-01

    .... (4) Invariants: both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

  18. Vision-Based Navigation and Recognition

    National Research Council Canada - National Science Library

    Rosenfeld, Azriel

    1996-01-01

    .... (4) Invariants -- both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

  19. Human computing and machine understanding of human behavior: A survey

    NARCIS (Netherlands)

    Pentland, Alex; Huang, Thomas S.; Huang, Th.S.; Nijholt, Antinus; Pantic, Maja; Pentland, A.

    2007-01-01

    A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing should be about anticipatory user interfaces

  20. Modeling Human Behavior to Anticipate Insider Attacks

    Directory of Open Access Journals (Sweden)

    Ryan E Hohimer

    2011-01-01

    Full Text Available The insider threat ranks among the most pressing cyber-security challenges that threaten government and industry information infrastructures. To date, no systematic methods have been developed that provide a complete and effective approach to prevent data leakage, espionage, and sabotage. Current practice is forensic in nature, relegating to the analyst the bulk of the responsibility to monitor, analyze, and correlate an overwhelming amount of data. We describe a predictive modeling framework that integrates a diverse set of data sources from the cyber domain, as well as inferred psychological/motivational factors that may underlie malicious insider exploits. This comprehensive threat assessment approach provides automated support for the detection of high-risk behavioral "triggers" to help focus the analyst's attention and inform the analysis. Designed to be domain-independent, the system may be applied to many different threat and warning analysis/sense-making problems.

  1. Food marketing towards children: brand logo recognition, food-related behavior and BMI among 3-13-year-olds in a south Indian town.

    Science.gov (United States)

    Ueda, Peter; Tong, Leilei; Viedma, Cristobal; Chandy, Sujith J; Marrone, Gaetano; Simon, Anna; Stålsby Lundborg, Cecilia

    2012-01-01

    To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3-13, from different socioeconomic backgrounds in a south Indian town. Child-parent pairs (n=306) were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt) featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (pfood preferences or purchase requests. Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India.

  2. Food Marketing towards Children: Brand Logo Recognition, Food-Related Behavior and BMI among 3–13-Year-Olds in a South Indian Town

    Science.gov (United States)

    Ueda, Peter; Tong, Leilei; Viedma, Cristobal; Chandy, Sujith J.; Marrone, Gaetano; Simon, Anna; Stålsby Lundborg, Cecilia

    2012-01-01

    Objectives To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3–13, from different socioeconomic backgrounds in a south Indian town. Methods Child-parent pairs (n = 306) were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt) featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. Results Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (pfood preferences or purchase requests. Conclusions Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India. PMID:23082137

  3. Recognition of HIV-1 peptides by host CTL is related to HIV-1 similarity to human proteins.

    Directory of Open Access Journals (Sweden)

    Morgane Rolland

    Full Text Available BACKGROUND: While human immunodeficiency virus type 1 (HIV-1-specific cytotoxic T lymphocytes preferentially target specific regions of the viral proteome, HIV-1 features that contribute to immune recognition are not well understood. One hypothesis is that similarities between HIV and human proteins influence the host immune response, i.e., resemblance between viral and host peptides could preclude reactivity against certain HIV epitopes. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed the extent of similarity between HIV-1 and the human proteome. Proteins from the HIV-1 B consensus sequence from 2001 were dissected into overlapping k-mers, which were then probed against a non-redundant database of the human proteome in order to identify segments of high similarity. We tested the relationship between HIV-1 similarity to host encoded peptides and immune recognition in HIV-infected individuals, and found that HIV immunogenicity could be partially modulated by the sequence similarity to the host proteome. ELISpot responses to peptides spanning the entire viral proteome evaluated in 314 individuals showed a trend indicating an inverse relationship between the similarity to the host proteome and the frequency of recognition. In addition, analysis of responses by a group of 30 HIV-infected individuals against 944 overlapping peptides representing a broad range of individual HIV-1B Nef variants, affirmed that the degree of similarity to the host was significantly lower for peptides with reactive epitopes than for those that were not recognized. CONCLUSIONS/SIGNIFICANCE: Our results suggest that antigenic motifs that are scarcely represented in human proteins might represent more immunogenic CTL targets not selected against in the host. This observation could provide guidance in the design of more effective HIV immunogens, as sequences devoid of host-like features might afford superior immune reactivity.

  4. Prenatal Flavor Exposure Affects Flavor Recognition and Stress-Related Behavior of Piglets

    NARCIS (Netherlands)

    Oostindjer, M.; Bolhuis, J.E.; Brand, van den H.; Kemp, B.

    2009-01-01

    Exposure to flavors in the amniotic fluid and mother's milk derived from the maternal diet has been shown to modulate food preferences and neophobia of young animals of several species. Aim of the experiment was to study the effects of pre- and postnatal flavor exposure on behavior of piglets during

  5. Talking with a Virtual Human : Controlling the Human Experience and Behavior in a Virtual Conversation

    NARCIS (Netherlands)

    Qu, C.

    2014-01-01

    Virtual humans are often designed to replace real humans in virtual reality applications for e.g., psychotherapy, education and entertainment. In general, applications with virtual humans are created for modifying a person's knowledge, beliefs, attitudes, emotions or behaviors. Reaching these

  6. Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

    Directory of Open Access Journals (Sweden)

    Huiyu Chen

    2017-01-01

    Full Text Available Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI and Silhouette Coefficient (SC are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically.

  7. Differences in human skin between the epidermal growth factor receptor distribution detected by EGF binding and monoclonal antibody recognition

    DEFF Research Database (Denmark)

    Green, M R; Couchman, J R

    1985-01-01

    , the eccrine sweat glands, capillary system, and the hair follicle outer root sheath, generally similar in pattern to that previously reported for full-thickness rat skin and human epidermis. The same areas also bound EGF-R1 but in addition the monoclonal antibody recognized a cone of melanin containing......Two methods have been used to examine epidermal growth factor (EGF) receptor distribution in human scalp and foreskin. The first employed [125I]EGF viable explants and autoradiography to determine the EGF binding pattern while the second used a monoclonal antibody to the human EGF receptor to map...... whether EGF-R1 could recognize molecules unrelated to the EGF receptor, the EGF binding and EGF-R1 recognition profiles were compared on cultures of SVK14 cells, a SV40 transformed human keratinocyte cell line. EGF binding and EGF-R1 monoclonal antibody distribution on these cells was found to be similar...

  8. Arousal rather than basic emotions influence long-term recognition memory in humans.

    Directory of Open Access Journals (Sweden)

    Artur Marchewka

    2016-10-01

    Full Text Available Emotion can influence various cognitive processes, however its impact on memory has been traditionally studied over relatively short retention periods and in line with dimensional models of affect. The present study aimed to investigate emotional effects on long-term recognition memory according to a combined framework of affective dimensions and basic emotions. Images selected from the Nencki Affective Picture System were rated on the scale of affective dimensions and basic emotions. After six months, subjects took part in a surprise recognition test during an fMRI session. The more negative the pictures the better they were remembered, but also the more false recognitions they provoked. Similar effects were found for the arousal dimension. Recognition success was greater for pictures with lower intensity of happiness and with higher intensity of surprise, sadness, fear, and disgust. Consecutive fMRI analyses showed a significant activation for remembered (recognized vs. forgotten (not recognized images in anterior cingulate and bilateral anterior insula as well as in bilateral caudate nuclei and right thalamus. Further, arousal was found to be the only subjective rating significantly modulating brain activation. Higher subjective arousal evoked higher activation associated with memory recognition in the right caudate and the left cingulate gyrus. Notably, no significant modulation was observed for other subjective ratings, including basic emotion intensities. These results emphasize the crucial role of arousal for long-term recognition memory and support the hypothesis that the memorized material, over time, becomes stored in a distributed cortical network including the core salience network and basal ganglia.

  9. International and Regional Institutional Dialogues for Human Rights for LGBT persons: The quest for recognition, anti-discrimination, and marriage in Southeast Asia

    OpenAIRE

    Holzhacker, Ronald

    2016-01-01

    There is a rich interplay between civil society organizations and institutions involved in human rights norm diffusion and the ‘ricochet’ of ideas at the regional level across Southeast Asia. There is a broad discussion occurring about human rights for LGBT persons and SOGI rights (Sexual Orientation and Gender Identity) including recognition, non-discrimination in employment, education, and housing, and partnership recognition and same-sex marriage. We focus on four countries, Thailand, Viet...

  10. Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment.

    Science.gov (United States)

    Lemaire, Edward D; Tundo, Marco D; Baddour, Natalie

    2015-12-11

    An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated. The mobile versus immobile classification set had a sensitivity of 86.30% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86.17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity

  11. Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

    KAUST Repository

    Magana-Mora, Arturo

    2017-08-15

    BackgroundPolyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3′-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge.ResultsIn this study, we analyzed human genomic DNA sequences for the 12 most common PAS variants. Our analysis has identified a set of features that helps in the recognition of true PAS, which may be involved in the regulation of the polyadenylation process. The proposed features, in combination with a recognition model, resulted in a novel method and tool, Omni-PolyA. Omni-PolyA combines several machine learning techniques such as different classifiers in a tree-like decision structure and genetic algorithms for deriving a robust classification model. We performed a comparison between results obtained by state-of-the-art methods, deep neural networks, and Omni-PolyA. Results show that Omni-PolyA significantly reduced the average classification error rate by 35.37% in the prediction of the 12 considered PAS variants relative to the state-of-the-art results.ConclusionsThe results of our study demonstrate that Omni-PolyA is currently the most accurate model for the prediction of PAS in human and can serve as a useful complement to other PAS recognition methods. Omni-PolyA is publicly available as an online tool accessible at www.cbrc.kaust.edu.sa/omnipolya/.

  12. Marmosets: A Neuroscientific Model of Human Social Behavior

    Science.gov (United States)

    Freiwald, Winrich A; Leopold, David A; Mitchell, Jude F; Silva, Afonso C; Wang, Xiaoqin

    2016-01-01

    The common marmoset (Callithrix jacchus) has garnered interest recently as a powerful model for the future of neuroscience research. Much of this excitement has centered on the species’ reproductive biology and compatibility with gene editing techniques, which together have provided a path for transgenic marmosets to contribute to the study of disease as well as basic brain mechanisms. In step with technical advances is the need to establish experimental paradigms that optimally tap into the marmosets’ behavioral and cognitive capacities. While conditioned task performance of a marmoset can compare unfavorably with rhesus monkey performance on conventional testing paradigms, marmosets’ social cognition and communication are more similar to that of humans. For example, marmosets are amongst only a handful of primates that, like humans, routinely pair bond and care cooperatively for their young. They are also notably pro-social and exhibit social cognitive abilities, such as imitation, that are rare outside of the Apes. In this review, we describe key facets of marmoset natural social behavior and demonstrate that emerging behavioral paradigms are well suited to isolate components of marmoset cognition that are highly relevant to humans. These approaches generally embrace natural behavior and communication, which has been rare in conventional primate testing, and thus allow for a new consideration of neural mechanisms underlying primate social cognition and communication. We anticipate that through parallel technical and paradigmatic advances, marmosets will become an essential model of human social behavior, including its dysfunction in nearly all neuropsychiatric disorders. PMID:27100195

  13. Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2016-07-01

    Full Text Available With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG, which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

  14. Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

    Science.gov (United States)

    Nguyen, Dat Tien; Park, Kang Ryoung

    2016-07-21

    With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

  15. Revisiting vocal perception in non-human animals: a review of vowel discrimination, speaker voice recognition, and speaker normalization

    Directory of Open Access Journals (Sweden)

    Buddhamas eKriengwatana

    2015-01-01

    Full Text Available The extent to which human speech perception evolved by taking advantage of predispositions and pre-existing features of vertebrate auditory and cognitive systems remains a central question in the evolution of speech. This paper reviews asymmetries in vowel perception, speaker voice recognition, and speaker normalization in non-human animals – topics that have not been thoroughly discussed in relation to the abilities of non-human animals, but are nonetheless important aspects of vocal perception. Throughout this paper we demonstrate that addressing these issues in non-human animals is relevant and worthwhile because many non-human animals must deal with similar issues in their natural environment. That is, they must also discriminate between similar-sounding vocalizations, determine signaler identity from vocalizations, and resolve signaler-dependent variation in vocalizations from conspecifics. Overall, we find that, although plausible, the current evidence is insufficiently strong to conclude that directional asymmetries in vowel perception are specific to humans, or that non-human animals can use voice characteristics to recognize human individuals. However, we do find some indication that non-human animals can normalize speaker differences. Accordingly, we identify avenues for future research that would greatly improve and advance our understanding of these topics.

  16. Sensor-based Human Activity Recognition in a Multi-user Scenario

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Tao, Xianping

    2009-01-01

    Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activiti...

  17. Real-time Human Activity Recognition using a Body Sensor Network

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Chen, Hanhua

    2010-01-01

    Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this mo...

  18. Recognition memory of neutral words can be impaired by task-irrelevant emotional encoding contexts: behavioral and electrophysiological evidence.

    Science.gov (United States)

    Zhang, Qin; Liu, Xuan; An, Wei; Yang, Yang; Wang, Yinan

    2015-01-01

    Previous studies on the effects of emotional context on memory for centrally presented neutral items have obtained inconsistent results. And in most of those studies subjects were asked to either make a connection between the item and the context at study or retrieve both the item and the context. When no response for the contexts is required, how emotional contexts influence memory for neutral items is still unclear. Thus, the present study attempted to investigate the influences of four types of emotional picture contexts on recognition memory of neutral words using both behavioral and event-related potential (ERP) measurements. During study, words were superimposed centrally onto emotional contexts, and subjects were asked to just remember the words. During test, both studied and new words were presented without the emotional contexts and subjects had to make "old/new" judgments for those words. The results revealed that, compared with the neutral context, the negative contexts and positive high-arousing context impaired recognition of words. ERP results at encoding demonstrated that, compared with items presented in the neutral context, items in the positive and negative high-arousing contexts elicited more positive ERPs, which probably reflects an automatic process of attention capturing of high-arousing context as well as a conscious and effortful process of overcoming the interference of high-arousing context. During retrieval, significant FN400 old/new effects occurred in conditions of the negative low-arousing, positive, and neutral contexts but not in the negative high-arousing condition. Significant LPC old/new effects occurred in all conditions of context. However, the LPC old/new effect in the negative high-arousing condition was smaller than that in the positive high-arousing and low-arousing conditions. These results suggest that emotional context might influence both the familiarity and recollection processes.

  19. Fatigue crack growth behavior and AE signal recognition from a composite patch repaired Ai thein plate

    International Nuclear Information System (INIS)

    Kim, Sung Jin; Kwon, Oh Yang

    2004-01-01

    The fatigue crack growth behavior of a fatigue-cracked and patch-repaired AA2024-T3 plate has been monitored. It was found that the overall crack growth rate was reduced and the crack propagation into the adjacent hole was also retarded. Signals due to crack growth after patch-repair and those due to debonding of the plate-patch interface were discriminated each other by using principal component analysis. The former showed higher center frequency and lower amplitude, whereas the latter showed longer rise time, lower frequency and higher amplitude.

  20. An investigation and comparison of speech recognition software for determining if bird song recordings contain legible human voices

    Directory of Open Access Journals (Sweden)

    Tim D. Hunt

    Full Text Available The purpose of this work was to test the effectiveness of using readily available speech recognition API services to determine if recordings of bird song had inadvertently recorded human voices. A mobile phone was used to record a human speaking at increasing distances from the phone in an outside setting with bird song occurring in the background. One of the services was trained with sample recordings and each service was compared for their ability to return recognized words. The services from Google and IBM performed similarly and the Microsoft service, that allowed training, performed slightly better. However, all three services failed to perform at a level that would enable recordings with recognizable human speech to be deleted in order to maintain full privacy protection.

  1. The role of the molecular chaperone heat shock protein A2 (HSPA2 in regulating human sperm-egg recognition

    Directory of Open Access Journals (Sweden)

    Brett Nixon

    2015-01-01

    Full Text Available One of the most common lesions present in the spermatozoa of human infertility patients is an idiopathic failure of sperm-egg recognition. Although this unique cellular interaction can now be readily by-passed by assisted reproductive strategies such as intracytoplasmic sperm injection (ICSI, recent large-scale epidemiological studies have encouraged the cautious use of this technology and highlighted the need for further research into the mechanisms responsible for defective sperm-egg recognition. Previous work in this field has established that the sperm domains responsible for oocyte interaction are formed during spermatogenesis prior to being dynamically modified during epididymal maturation and capacitation in female reproductive tract. While the factors responsible for the regulation of these sequential maturational events are undoubtedly complex, emerging research has identified the molecular chaperone, heat shock protein A2 (HSPA2, as a key regulator of these events in human spermatozoa. HSPA2 is a testis-enriched member of the 70 kDa heat shock protein family that promotes the folding, transport, and assembly of protein complexes and has been positively correlated with in vitro fertilization (IVF success. Furthermore, reduced expression of HSPA2 from the human sperm proteome leads to an impaired capacity for cumulus matrix dispersal, sperm-egg recognition and fertilization following both IVF and ICSI. In this review, we consider the evidence supporting the role of HSPA2 in sperm function and explore the potential mechanisms by which it is depleted in the spermatozoa of infertile patients. Such information offers novel insights into the molecular mechanisms governing sperm function.

  2. Food marketing towards children: brand logo recognition, food-related behavior and BMI among 3-13-year-olds in a south Indian town.

    Directory of Open Access Journals (Sweden)

    Peter Ueda

    Full Text Available OBJECTIVES: To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3-13, from different socioeconomic backgrounds in a south Indian town. METHODS: Child-parent pairs (n=306 were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. RESULTS: Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (p<0.001 and socioeconomic level (p<0.001 comparing children in the highest and lowest of three socioeconomic groups. Adjusted for gender, age and socioeconomic group, logo recognition was associated with higher BMI (p=0.022 and nutritional knowledge (p<0.001 but not to unhealthy food preferences or purchase requests. CONCLUSIONS: Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India.

  3. Novelty, Stress, and Biological Roots in Human Market Behavior

    Directory of Open Access Journals (Sweden)

    Alexey Sarapultsev

    2014-02-01

    Full Text Available Although studies examining the biological roots of human behavior have been conducted since the seminal work Kahneman and Tversky, crises and panics have not disappeared. The frequent occurrence of various types of crises has led some economists to the conviction that financial markets occasionally praise irrational judgments and that market crashes cannot be avoided a priori (Sornette 2009; Smith 2004. From a biological point of view, human behaviors are essentially the same during crises accompanied by stock market crashes and during bubble growth when share prices exceed historic highs. During those periods, most market participants see something new for themselves, and this inevitably induces a stress response in them with accompanying changes in their endocrine profiles and motivations. The result is quantitative and qualitative changes in behavior (Zhukov 2007. An underestimation of the role of novelty as a stressor is the primary shortcoming of current approaches for market research. When developing a mathematical market model, it is necessary to account for the biologically determined diphasisms of human behavior in everyday low-stress conditions and in response to stressors. This is the only type of approach that will enable forecasts of market dynamics and investor behaviors under normal conditions as well as during bubbles and panics.

  4. Recognition of Human Erythrocyte Receptors by the Tryptophan-Rich Antigens of Monkey Malaria Parasite Plasmodium knowlesi.

    Directory of Open Access Journals (Sweden)

    Kriti Tyagi

    Full Text Available The monkey malaria parasite Plasmodium knowlesi also infect humans. There is a lack of information on the molecular mechanisms that take place between this simian parasite and its heterologous human host erythrocytes leading to this zoonotic disease. Therefore, we investigated here the binding ability of P. knowlesi tryptophan-rich antigens (PkTRAgs to the human erythrocytes and sharing of the erythrocyte receptors between them as well as with other commonly occurring human malaria parasites.Six PkTRAgs were cloned and expressed in E.coli as well as in mammalian CHO-K1 cell to determine their human erythrocyte binding activity by cell-ELISA, and in-vitro rosetting assay, respectively.Three of six PkTRAgs (PkTRAg38.3, PkTRAg40.1, and PkTRAg67.1 showed binding to human erythrocytes. Two of them (PkTRAg40.1 and PkTRAg38.3 showed cross-competition with each other as well as with the previously described P.vivax tryptophan-rich antigens (PvTRAgs for human erythrocyte receptors. However, the third protein (PkTRAg67.1 utilized the additional but different human erythrocyte receptor(s as it did not cross-compete for erythrocyte binding with either of these two PkTRAgs as well as with any of the PvTRAgs. These three PkTRAgs also inhibited the P.falciparum parasite growth in in-vitro culture, further indicating the sharing of human erythrocyte receptors by these parasite species and the biological significance of this receptor-ligand interaction between heterologous host and simian parasite.Recognition and sharing of human erythrocyte receptor(s by PkTRAgs with human parasite ligands could be part of the strategy adopted by the monkey malaria parasite to establish inside the heterologous human host.

  5. Recognition of Human Erythrocyte Receptors by the Tryptophan-Rich Antigens of Monkey Malaria Parasite Plasmodium knowlesi.

    Science.gov (United States)

    Tyagi, Kriti; Gupta, Deepali; Saini, Ekta; Choudhary, Shilpa; Jamwal, Abhishek; Alam, Mohd Shoeb; Zeeshan, Mohammad; Tyagi, Rupesh K; Sharma, Yagya D

    2015-01-01

    The monkey malaria parasite Plasmodium knowlesi also infect humans. There is a lack of information on the molecular mechanisms that take place between this simian parasite and its heterologous human host erythrocytes leading to this zoonotic disease. Therefore, we investigated here the binding ability of P. knowlesi tryptophan-rich antigens (PkTRAgs) to the human erythrocytes and sharing of the erythrocyte receptors between them as well as with other commonly occurring human malaria parasites. Six PkTRAgs were cloned and expressed in E.coli as well as in mammalian CHO-K1 cell to determine their human erythrocyte binding activity by cell-ELISA, and in-vitro rosetting assay, respectively. Three of six PkTRAgs (PkTRAg38.3, PkTRAg40.1, and PkTRAg67.1) showed binding to human erythrocytes. Two of them (PkTRAg40.1 and PkTRAg38.3) showed cross-competition with each other as well as with the previously described P.vivax tryptophan-rich antigens (PvTRAgs) for human erythrocyte receptors. However, the third protein (PkTRAg67.1) utilized the additional but different human erythrocyte receptor(s) as it did not cross-compete for erythrocyte binding with either of these two PkTRAgs as well as with any of the PvTRAgs. These three PkTRAgs also inhibited the P.falciparum parasite growth in in-vitro culture, further indicating the sharing of human erythrocyte receptors by these parasite species and the biological significance of this receptor-ligand interaction between heterologous host and simian parasite. Recognition and sharing of human erythrocyte receptor(s) by PkTRAgs with human parasite ligands could be part of the strategy adopted by the monkey malaria parasite to establish inside the heterologous human host.

  6. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

    Directory of Open Access Journals (Sweden)

    Carlos E. Galván-Tejada

    2016-01-01

    Full Text Available This work presents a human activity recognition (HAR model based on audio features. The use of sound as an information source for HAR models represents a challenge because sound wave analyses generate very large amounts of data. However, feature selection techniques may reduce the amount of data required to represent an audio signal sample. Some of the audio features that were analyzed include Mel-frequency cepstral coefficients (MFCC. Although MFCC are commonly used in voice and instrument recognition, their utility within HAR models is yet to be confirmed, and this work validates their usefulness. Additionally, statistical features were extracted from the audio samples to generate the proposed HAR model. The size of the information is necessary to conform a HAR model impact directly on the accuracy of the model. This problem also was tackled in the present work; our results indicate that we are capable of recognizing a human activity with an accuracy of 85% using the HAR model proposed. This means that minimum computational costs are needed, thus allowing portable devices to identify human activities using audio as an information source.

  7. Counseling Children and Adolescents: Rational Emotive Behavior Therapy and Humanism.

    Science.gov (United States)

    Vernon, Ann

    1996-01-01

    Describes specific parallels between rational emotive behavior therapy and humanism. Places specific emphasis on the application of these principles with children and adolescents. Concepts are illustrated with case studies and a description of the similarities between rational emotive and humanistic, or affective, education. Highlights emotional…

  8. Cognitive Factors Affecting Freeze-like Behavior in Humans.

    Science.gov (United States)

    Alban, Michael W; Pocknell, Victoria

    2017-01-01

    Contemporary research on survival-related defensive behaviors has identified physiological markers of freeze/flight/fight. Our research focused on cognitive factors associated with freeze-like behavior in humans. Study 1 tested if an explicit decision to freeze is associated with the psychophysiological state of freezing. Heart rate deceleration occurred when participants chose to freeze. Study 2 varied the efficacy of freezing relative to other defense options and found "freeze" was responsive to variations in the perceived effectiveness of alternative actions. Study 3 tested if individual differences in motivational orientation affect preference for a "freeze" option when the efficacy of options is held constant. A trend in the predicted direction suggested that naturally occurring cognitions led loss-avoiders to select "freeze" more often than reward-seekers. In combination, our attention to the cognitive factors affecting freeze-like behavior in humans represents a preliminary step in addressing an important but neglected research area.

  9. Data Mining and Visualization of Large Human Behavior Data Sets

    DEFF Research Database (Denmark)

    Cuttone, Andrea

    and credit card transactions – have provided us new sources for studying our behavior. In particular smartphones have emerged as new tools for collecting data about human activity, thanks to their sensing capabilities and their ubiquity. This thesis investigates the question of what we can learn about human...... behavior from this rich and pervasive mobile sensing data. In the first part, we describe a large-scale data collection deployment collecting high-resolution data for over 800 students at the Technical University of Denmark using smartphones, including location, social proximity, calls and SMS. We provide...... an overview of the technical infrastructure, the experimental design, and the privacy measures. The second part investigates the usage of this mobile sensing data for understanding personal behavior. We describe two large-scale user studies on the deployment of self-tracking apps, in order to understand...

  10. Assessing Human Judgment of Computationally Generated Swarming Behavior

    Directory of Open Access Journals (Sweden)

    John Harvey

    2018-02-01

    Full Text Available Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects’ assessment of the behavior of a simplified version of Reynolds’ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.

  11. Facial Emotion Recognition Performance Differentiates Between Behavioral Variant Frontotemporal Dementia and Major Depressive Disorder.

    Science.gov (United States)

    Chiu, Isabelle; Piguet, Olivier; Diehl-Schmid, Janine; Riedl, Lina; Beck, Johannes; Leyhe, Thomas; Holsboer-Trachsler, Edith; Kressig, Reto W; Berres, Manfred; Monsch, Andreas U; Sollberger, Marc

    Misdiagnosis of early behavioral variant frontotemporal dementia (bvFTD) with major depressive disorder (MDD) is not uncommon due to overlapping symptoms. The aim of this study was to improve the discrimination between these disorders using a novel facial emotion perception task. In this prospective cohort study (July 2013-March 2016), we compared 25 patients meeting Rascovsky diagnostic criteria for bvFTD, 20 patients meeting DSM-IV criteria for MDD, 21 patients meeting McKhann diagnostic criteria for Alzheimer's disease dementia, and 31 healthy participants on a novel emotion intensity rating task comprising morphed low-intensity facial stimuli. Participants were asked to rate the intensity of morphed faces on the congruent basic emotion (eg, rating on sadness when sad face is shown) and on the 5 incongruent basic emotions (eg, rating on each of the other basic emotions when sad face is shown). While bvFTD patients underrated congruent emotions (P dementia patients perceived emotions similarly to healthy participants, indicating no impact of cognitive impairment on rating scores. Our congruent and incongruent facial emotion intensity rating task allows a detailed assessment of facial emotion perception in patient populations. By using this simple task, we achieved an almost complete discrimination between bvFTD and MDD, potentially helping improve the diagnostic certainty in early bvFTD. © Copyright 2018 Physicians Postgraduate Press, Inc.

  12. DPP6 Loss Impacts Hippocampal Synaptic Development and Induces Behavioral Impairments in Recognition, Learning and Memory

    Directory of Open Access Journals (Sweden)

    Lin Lin

    2018-03-01

    Full Text Available DPP6 is well known as an auxiliary subunit of Kv4-containing, A-type K+ channels which regulate dendritic excitability in hippocampal CA1 pyramidal neurons. We have recently reported, however, a novel role for DPP6 in regulating dendritic filopodia formation and stability, affecting synaptic development and function. These results are notable considering recent clinical findings associating DPP6 with neurodevelopmental and intellectual disorders. Here we assessed the behavioral consequences of DPP6 loss. We found that DPP6 knockout (DPP6-KO mice are impaired in hippocampus-dependent learning and memory. Results from the Morris water maze and T-maze tasks showed that DPP6-KO mice exhibit slower learning and reduced memory performance. DPP6 mouse brain weight is reduced throughout development compared with WT, and in vitro imaging results indicated that DPP6 loss affects synaptic structure and motility. Taken together, these results show impaired synaptic development along with spatial learning and memory deficiencies in DPP6-KO mice.

  13. Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior.

    Science.gov (United States)

    Carlson, Bruce A

    2009-07-29

    Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge. In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals, and bandpass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally relevant stimulus information encoded into temporal patterns of activity by sensory neurons.

  14. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

    Science.gov (United States)

    Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

    2016-12-02

    Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  15. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

    Directory of Open Access Journals (Sweden)

    Huile Xu

    2016-12-01

    Full Text Available Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT or wavelet transform (WT. However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA and instantaneous frequency (IF by means of empirical mode decomposition (EMD, as well as instantaneous energy density (IE and marginal spectrum (MS derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  16. Discrimination of complex human behavior by pigeons (Columba livia and humans.

    Directory of Open Access Journals (Sweden)

    Muhammad A J Qadri

    Full Text Available The cognitive and neural mechanisms for recognizing and categorizing behavior are not well understood in non-human animals. In the current experiments, pigeons and humans learned to categorize two non-repeating, complex human behaviors ("martial arts" vs. "Indian dance". Using multiple video exemplars of a digital human model, pigeons discriminated these behaviors in a go/no-go task and humans in a choice task. Experiment 1 found that pigeons already experienced with discriminating the locomotive actions of digital animals acquired the discrimination more rapidly when action information was available than when only pose information was available. Experiments 2 and 3 found this same dynamic superiority effect with naïve pigeons and human participants. Both species used the same combination of immediately available static pose information and more slowly perceived dynamic action cues to discriminate the behavioral categories. Theories based on generalized visual mechanisms, as opposed to embodied, species-specific action networks, offer a parsimonious account of how these different animals recognize behavior across and within species.

  17. Edwin Grant Dexter: an early researcher in human behavioral biometeorology

    Science.gov (United States)

    Stewart, Alan E.

    2015-06-01

    Edwin Grant Dexter (1868-1938) was one of the first researchers to study empirically the effects of specific weather conditions on human behavior. Dexter (1904) published his findings in a book, Weather influences. The author's purposes in this article were to (1) describe briefly Dexter's professional life and examine the historical contexts and motivations that led Dexter to conduct some of the first empirical behavioral biometeorological studies of the time, (2) describe the methods Dexter used to examine weather-behavior relationships and briefly characterize the results that he reported in Weather influences, and (3) provide a historical analysis of Dexter's work and assess its significance for human behavioral biometeorology. Dexter's Weather influences, while demonstrating an exemplary approach to weather, health, and behavior relationships, came at the end of a long era of such studies, as health, social, and meteorological sciences were turning to different paradigms to advance their fields. For these reasons, Dexter's approach and contributions may not have been fully recognized at the time and are, consequently, worthy of consideration by contemporary biometeorologists.

  18. Understanding the heavy-tailed dynamics in human behavior

    Science.gov (United States)

    Ross, Gordon J.; Jones, Tim

    2015-06-01

    The recent availability of electronic data sets containing large volumes of communication data has made it possible to study human behavior on a larger scale than ever before. From this, it has been discovered that across a diverse range of data sets, the interevent times between consecutive communication events obey heavy-tailed power law dynamics. Explaining this has proved controversial, and two distinct hypotheses have emerged. The first holds that these power laws are fundamental, and arise from the mechanisms such as priority queuing that humans use to schedule tasks. The second holds that they are statistical artifacts which only occur in aggregated data when features such as circadian rhythms and burstiness are ignored. We use a large social media data set to test these hypotheses, and find that although models that incorporate circadian rhythms and burstiness do explain part of the observed heavy tails, there is residual unexplained heavy-tail behavior which suggests a more fundamental cause. Based on this, we develop a quantitative model of human behavior which improves on existing approaches and gives insight into the mechanisms underlying human interactions.

  19. Behavioral responses associated with a human-mediated predator shelter.

    Directory of Open Access Journals (Sweden)

    Graeme Shannon

    Full Text Available Human activities in protected areas can affect wildlife populations in a similar manner to predation risk, causing increases in movement and vigilance, shifts in habitat use and changes in group size. Nevertheless, recent evidence indicates that in certain situations ungulate species may actually utilize areas associated with higher levels of human presence as a potential refuge from disturbance-sensitive predators. We now use four-years of behavioral activity budget data collected from pronghorn (Antilocapra americana and elk (Cervus elephus in Grand Teton National Park, USA to test whether predictable patterns of human presence can provide a shelter from predatory risk. Daily behavioral scans were conducted along two parallel sections of road that differed in traffic volume--with the main Teton Park Road experiencing vehicle use that was approximately thirty-fold greater than the River Road. At the busier Teton Park Road, both species of ungulate engaged in higher levels of feeding (27% increase in the proportion of pronghorn feeding and 21% increase for elk, lower levels of alert behavior (18% decrease for pronghorn and 9% decrease for elk and formed smaller groups. These responses are commonly associated with reduced predatory threat. Pronghorn also exhibited a 30% increase in the proportion of individuals moving at the River Road as would be expected under greater exposure to predation risk. Our findings concur with the 'predator shelter hypothesis', suggesting that ungulates in GTNP use human presence as a potential refuge from predation risk, adjusting their behavior accordingly. Human activity has the potential to alter predator-prey interactions and drive trophic-mediated effects that could ultimately impact ecosystem function and biodiversity.

  20. Industrial Buying Behavior Related to Human Resource Consulting Services

    DEFF Research Database (Denmark)

    Grünbaum, Niels Nolsøe; Hollensen, Svend; Kahle, Lynn

    2013-01-01

    The purpose of this article is to extend the understanding of the industrial buying process in connection with purchasing professional business (B2B) services, specifically human resource (HR) consulting services. Early B2B buying-behavior literature strongly emphasizes the rational aspects...... of buying behavior in B2B services. Based on a comprehensive exploratory study of Danish companies’ purchases of HR consulting services, the authors provide insights into the factors that determine how Danish companies choose a consulting services supplier. Five hypotheses are developed based...

  1. Question of Drug Workers under the Prism Theory of Axel Honneth recognition . Human Right to Work and his pretension of Universal Validity

    OpenAIRE

    Maria Cecília Máximo Teodoro; Sabrina Colares Nogueira

    2016-01-01

    Relate the theme regarding the Theory of Recognition Axel Honnet, the human right to work as a human right to claim to universal validity, from the perspective of the Declaration of Fundamental Principles and Rights at Work ILO, for purposes of setting parameters and recognition of social (re) integration of chemical class dependent workers is the main goal of this article. We will analyze the theoretical framework in which the pursuit of human dignity at work is the goal of the state to whic...

  2. Face shape and face identity processing in behavioral variant fronto-temporal dementia: A specific deficit for familiarity and name recognition of famous faces.

    Science.gov (United States)

    De Winter, François-Laurent; Timmers, Dorien; de Gelder, Beatrice; Van Orshoven, Marc; Vieren, Marleen; Bouckaert, Miriam; Cypers, Gert; Caekebeke, Jo; Van de Vliet, Laura; Goffin, Karolien; Van Laere, Koen; Sunaert, Stefan; Vandenberghe, Rik; Vandenbulcke, Mathieu; Van den Stock, Jan

    2016-01-01

    Deficits in face processing have been described in the behavioral variant of fronto-temporal dementia (bvFTD), primarily regarding the recognition of facial expressions. Less is known about face shape and face identity processing. Here we used a hierarchical strategy targeting face shape and face identity recognition in bvFTD and matched healthy controls. Participants performed 3 psychophysical experiments targeting face shape detection (Experiment 1), unfamiliar face identity matching (Experiment 2), familiarity categorization and famous face-name matching (Experiment 3). The results revealed group differences only in Experiment 3, with a deficit in the bvFTD group for both familiarity categorization and famous face-name matching. Voxel-based morphometry regression analyses in the bvFTD group revealed an association between grey matter volume of the left ventral anterior temporal lobe and familiarity recognition, while face-name matching correlated with grey matter volume of the bilateral ventral anterior temporal lobes. Subsequently, we quantified familiarity-specific and name-specific recognition deficits as the sum of the celebrities of which respectively only the name or only the familiarity was accurately recognized. Both indices were associated with grey matter volume of the bilateral anterior temporal cortices. These findings extent previous results by documenting the involvement of the left anterior temporal lobe (ATL) in familiarity detection and the right ATL in name recognition deficits in fronto-temporal lobar degeneration.

  3. Sensor-Based Human Activity Recognition in a Multi-user Scenario

    Science.gov (United States)

    Wang, Liang; Gu, Tao; Tao, Xianping; Lu, Jian

    Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

  4. The Humanism of Rational Emotive Behavior Therapy and Other Cognitive Behavior Therapies.

    Science.gov (United States)

    Ellis, Albert

    1996-01-01

    Describes aspects of rational emotive behavior therapy (REBT). REBT shows how people can both create and uncreate many of their emotional disturbances. It is a theory of personality which avoids devotion to any kind of magic and supernaturalism and emphasizes unconditional self-acceptance, antiabsolutism, uncertainty, and human fallibility. (RJM)

  5. Semantic Radicals Contribute More Than Phonetic Radicals to the Recognition of Chinese Phonograms: Behavioral and ERP Evidence in a Factorial Study

    Directory of Open Access Journals (Sweden)

    Xieshun Wang

    2017-12-01

    Full Text Available The Chinese phonograms consist of a semantic radical and a phonetic radical. The two types of radicals have different functional contributions to their host phonogram. The semantic radical typically signifies the meaning of the phonogram, while the phonetic radical usually contains a phonological clue to the phonogram’s pronunciation. However, it is still unclear how they interplay with each other when we attempt to recognize a phonogram because previous studies rarely manipulated the functionality of the two types of radicals in a single design. Using a full factorial design, the present study aimed to probe this issue by directly manipulating the functional validity of the two types of radicals in a lexical decision task with both behavioral and event-related potential (ERP measurements. The results showed that recognition of phonograms which were related to their semantic radicals in meaning took a shorter reaction time, showed a lower error rate, and elicited a smaller P200 and a larger N400 than did recognition of those which had no semantic relation with their semantic radicals. However, the validity of phonetic radicals did not show any main effect or interaction with that of semantic radicals on either behavioral or ERP measurements. These results indicated that semantic radicals played a dominant role in the recognition of phonograms. Transparent semantic radicals, which provide valid semantic cues to phonograms, can facilitate the recognition of phonograms.

  6. Modeling human behaviors and reactions under dangerous environment.

    Science.gov (United States)

    Kang, J; Wright, D K; Qin, S F; Zhao, Y

    2005-01-01

    This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools Dev. Programming within Virtools Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed.

  7. THE PREREQUISITES OF PROSOCIAL BEHAVIOR IN HUMAN ONTOGENY

    Directory of Open Access Journals (Sweden)

    Irina M. Sozinova

    2017-06-01

    Full Text Available Understanding the development of moral attitudes toward unrelated individuals from different social groups may provide insights into the role of biological and cultural factors in prosocial behavior. Children (3–11 years old, N=80 were presented with moral dilemmas describing a conflict of interests between a con-specific (human and another species (animals or aliens. Participants were asked to evaluate the behavior of a human in terms of ‘good’ and ‘bad’, and to choose whom they would help: a human aggressor who benefits at the expense of a victim in vital need, or the victim. Results showed that the older children preferred to help non-human victims significantly more often than the younger children. The evaluation of human actions was related to the proportion of such preferences. These findings are discussed from the perspectives of kin selection theory, group selection theory and the system-evolutionary approach. The implications of the study for moral education are suggested.

  8. Accommodating complexity and human behaviors in decision analysis.

    Energy Technology Data Exchange (ETDEWEB)

    Backus, George A.; Siirola, John Daniel; Schoenwald, David Alan; Strip, David R.; Hirsch, Gary B.; Bastian, Mark S.; Braithwaite, Karl R.; Homer, Jack [Homer Consulting

    2007-11-01

    This is the final report for a LDRD effort to address human behavior in decision support systems. One sister LDRD effort reports the extension of this work to include actual human choices and additional simulation analyses. Another provides the background for this effort and the programmatic directions for future work. This specific effort considered the feasibility of five aspects of model development required for analysis viability. To avoid the use of classified information, healthcare decisions and the system embedding them became the illustrative example for assessment.

  9. DTIC Review: Human, Social, Cultural and Behavior Modeling. Volume 9, Number 1 (CD-ROM)

    National Research Council Canada - National Science Library

    2008-01-01

    ...: Human, Social, Cultural and Behavior (HSCB) models are designed to help understand the structure, interconnections, dependencies, behavior, and trends associated with any collection of individuals...

  10. Human NKG2D-ligands: cell biology strategies to ensure immune recognition

    Directory of Open Access Journals (Sweden)

    Lola eFernández-Messina

    2012-09-01

    Full Text Available Immune recognition mediated by the activating receptor NKG2D plays an important role for the elimination of stressed cells, including tumours and virus-infected cells. On the other hand, the ligands for NKG2D can also be shed into the sera of cancer patients where they weaken the immune response by downmodulating the receptor on effector cells, mainly NK and T cells. Although both families of NKG2D-ligands, MICA/B and ULBPs, are related to MHC molecules and their expression is increased after stress, many differences are observed in terms of their biochemical properties and cell trafficking. In this paper, we summarise the variety of NKG2D-ligands and propose that selection pressure has driven evolution of diversity in their trafficking and shedding, but not receptor binding affinity. However, it is also possible to identify functional properties common to individual ULBP molecules and MICA/B alleles, but not generally conserved within the MIC or ULBP families. These characteristics likely represent examples of convergent evolution for efficient immune recognition, but are also attractive targets for pathogen immune evasion strategies. Categorization of NKG2D-ligands according to their biological features, rather than their genetic family, may help to achieve a better understanding of NKG2D-ligand association with disease.

  11. Training together: how another human's presence affects behavior during virtual human-based team training

    Directory of Open Access Journals (Sweden)

    Andrew Robb

    2016-08-01

    Full Text Available Despite research showing that team training can lead to strong improvements in team performance, logistical difficulties can prevent team training programs from being adopted on a large scale. A proposed solution to these difficulties is the use of virtual humans to replace missing teammates. Existing research evaluating the use of virtual humans for team training has been conducted in settings involving a single human trainee. However, in the real world multiple human trainees would most likely train together. In this paper, we explore how the presence of a second human trainee can alter behavior during a medical team training program. Ninety-two nurses and surgical technicians participated in a medical training exercise, where they worked with a virtual surgeon and virtual anesthesiologist to prepare a simulated patient for surgery. The agency of the nurse and the surgical technician were varied between three conditions: human nurses and surgical technicians working together; human nurses working with a virtual surgical technician; and human surgical technicians working with a virtual nurse. Variations in agency did not produce statistically significant differences in the training outcomes, but several notable differences were observed in other aspects of the team's behavior. Specifically, when working with a virtual nurse, human surgical technicians were more likely to assist with speaking up about patient safety issues that were outside of their normal responsibilities; human trainees spent less time searching for a missing item when working with a virtual partner, likely because the virtual partner was physically unable to move throughout the room and assist with the searching process; and more breaks in presence were observed when two human teammates were present. These results show that some behaviors may be influenced by the presence of multiple human trainees, though these behaviors may not impinge on core training goals. When

  12. The human factor: behavioral and neural correlates of humanized perception in moral decision making.

    Science.gov (United States)

    Majdandžić, Jasminka; Bauer, Herbert; Windischberger, Christian; Moser, Ewald; Engl, Elisabeth; Lamm, Claus

    2012-01-01

    The extent to which people regard others as full-blown individuals with mental states ("humanization") seems crucial for their prosocial motivation towards them. Previous research has shown that decisions about moral dilemmas in which one person can be sacrificed to save multiple others do not consistently follow utilitarian principles. We hypothesized that this behavior can be explained by the potential victim's perceived humanness and an ensuing increase in vicarious emotions and emotional conflict during decision making. Using fMRI, we assessed neural activity underlying moral decisions that affected fictitious persons that had or had not been experimentally humanized. In implicit priming trials, participants either engaged in mentalizing about these persons (Humanized condition) or not (Neutral condition). In subsequent moral dilemmas, participants had to decide about sacrificing these persons' lives in order to save the lives of numerous others. Humanized persons were sacrificed less often, and the activation pattern during decisions about them indicated increased negative affect, emotional conflict, vicarious emotions, and behavioral control (pgACC/mOFC, anterior insula/IFG, aMCC and precuneus/PCC). Besides, we found enhanced effective connectivity between aMCC and anterior insula, which suggests increased emotion regulation during decisions affecting humanized victims. These findings highlight the importance of others' perceived humanness for prosocial behavior - with aversive affect and other-related concern when imagining harming more "human-like" persons acting against purely utilitarian decisions.

  13. The human factor: behavioral and neural correlates of humanized perception in moral decision making.

    Directory of Open Access Journals (Sweden)

    Jasminka Majdandžić

    Full Text Available The extent to which people regard others as full-blown individuals with mental states ("humanization" seems crucial for their prosocial motivation towards them. Previous research has shown that decisions about moral dilemmas in which one person can be sacrificed to save multiple others do not consistently follow utilitarian principles. We hypothesized that this behavior can be explained by the potential victim's perceived humanness and an ensuing increase in vicarious emotions and emotional conflict during decision making. Using fMRI, we assessed neural activity underlying moral decisions that affected fictitious persons that had or had not been experimentally humanized. In implicit priming trials, participants either engaged in mentalizing about these persons (Humanized condition or not (Neutral condition. In subsequent moral dilemmas, participants had to decide about sacrificing these persons' lives in order to save the lives of numerous others. Humanized persons were sacrificed less often, and the activation pattern during decisions about them indicated increased negative affect, emotional conflict, vicarious emotions, and behavioral control (pgACC/mOFC, anterior insula/IFG, aMCC and precuneus/PCC. Besides, we found enhanced effective connectivity between aMCC and anterior insula, which suggests increased emotion regulation during decisions affecting humanized victims. These findings highlight the importance of others' perceived humanness for prosocial behavior - with aversive affect and other-related concern when imagining harming more "human-like" persons acting against purely utilitarian decisions.

  14. ON THE R-CURVE BEHAVIOR OF HUMAN TOOTH ENAMEL

    OpenAIRE

    Bajaj, Devendra; Arola, Dwayne

    2009-01-01

    In this study the crack growth resistance behavior and fracture toughness of human tooth enamel were quantified using incremental crack growth measures and conventional fracture mechanics. Results showed that enamel undergoes an increase in crack growth resistance (i.e. rising R-curve) with crack extension from the outer to the inner enamel, and that the rise in toughness is function of distance from the Dentin Enamel Junction (DEJ). The outer enamel exhibited the lowest apparent toughness (0...

  15. Deformation Behavior of Human Dentin under Uniaxial Compression

    Directory of Open Access Journals (Sweden)

    Dmitry Zaytsev

    2012-01-01

    Full Text Available Deformation behavior of a human dentin under compression including size and rate effects is studied. No difference between mechanical properties of crown and root dentin is found. It is mechanically isotropic high elastic and strong hard tissue, which demonstrates considerable plasticity and ability to suppress a crack growth. Mechanical properties of dentin depend on a shape of samples and a deformation rate.

  16. Enantioselective recognition of an isomeric ligand by a biomolecule: mechanistic insights into static and dynamic enantiomeric behavior and structural flexibility.

    Science.gov (United States)

    Peng, Wei; Ding, Fei

    2017-10-24

    Chirality is a ubiquitous basic attribute of nature, which inseparably relates to the life activity of living organisms. However, enantiomeric differences have still failed to arouse enough attention during the biological evaluation and practical application of chiral substances, and this poses a large threat to human health. In the current study, we explore the enantioselective biorecognition of a chiral compound by an asymmetric biomolecule, and then decipher the molecular basis of such a biological phenomenon on the static and, in particular, the dynamic scale. In light of the wet experiments, in silico docking results revealed that the orientation of the latter part of the optical isomer structures in the recognition domain can be greatly affected by the chiral carbon center in a model ligand molecule, and this event may induce large disparities between the static chiral bioreaction modes and noncovalent interactions (especially hydrogen bonding). Dynamic stereoselective biorecognition assays indicated that the conformational stability of the protein-(S)-diclofop system is clearly greater than the protein-(R)-diclofop adduct; and moreover, the conformational alterations of the diclofop enantiomers in the dynamic process will directly influence the conformational flexibility of the key residues found in the biorecognition region. These points enable the changing trends of biopolymer structural flexibility and free energy to exhibit significant distinctions when proteins sterically recognize the (R)-/(S)-stereoisomers. The outcomes of the energy decomposition further showed that the van der Waals' energy has roughly the same contribution to the chiral recognition biosystems, whereas the contribution of electrostatic energy to the protein-(R)-diclofop complex is notably smaller than to the protein-(S)-diclofop bioconjugate. This proves that differences in the noncovalent bonds would have a serious impact on the stereoselective biorecognition between a

  17. The Seven Deadly Tensions of Health-Related Human Information Behavior

    Directory of Open Access Journals (Sweden)

    J. David Johnson

    2015-08-01

    Full Text Available Tensions are a ubiquitous feature of social life and are manifested in a number of particular forms: contradictory logics, competing demands, clashes of ideas, contradictions, dialectics, irony, paradoxes, and/or dilemmas. This essay aims to explore in detail tensions surrounding seven common findings of the information seeking literature relating to: interpersonal communication, accessibility, level of skill, individual preferences, psychological limits, inertia, and costs. Our incomplete understanding of these tensions can lead us to suggest resolutions that do not recognize their underlying dualities. Human information behavior stands at the intersection of many important theoretical and policy issues (e.g., personalized medicine. Policy makers need to be more attuned to these basic tensions of information seeking recognizing the real human limits they represent to informing the public. So, even if you build a great information system, people will not necessarily use it because of the force of these underlying tensions. While rationality rules systems, irrationality rules people. The proliferation of navigator roles over the last several years is actually a hopeful sign: recognition that people need a human interface to inform them about our ever more complex health care systems.

  18. The Human Factor: Behavioral and Neural Correlates of Humanized Perception in Moral Decision Making

    Science.gov (United States)

    Majdandžić, Jasminka; Bauer, Herbert; Windischberger, Christian; Moser, Ewald; Engl, Elisabeth; Lamm, Claus

    2012-01-01

    The extent to which people regard others as full-blown individuals with mental states (“humanization”) seems crucial for their prosocial motivation towards them. Previous research has shown that decisions about moral dilemmas in which one person can be sacrificed to save multiple others do not consistently follow utilitarian principles. We hypothesized that this behavior can be explained by the potential victim’s perceived humanness and an ensuing increase in vicarious emotions and emotional conflict during decision making. Using fMRI, we assessed neural activity underlying moral decisions that affected fictitious persons that had or had not been experimentally humanized. In implicit priming trials, participants either engaged in mentalizing about these persons (Humanized condition) or not (Neutral condition). In subsequent moral dilemmas, participants had to decide about sacrificing these persons’ lives in order to save the lives of numerous others. Humanized persons were sacrificed less often, and the activation pattern during decisions about them indicated increased negative affect, emotional conflict, vicarious emotions, and behavioral control (pgACC/mOFC, anterior insula/IFG, aMCC and precuneus/PCC). Besides, we found enhanced effective connectivity between aMCC and anterior insula, which suggests increased emotion regulation during decisions affecting humanized victims. These findings highlight the importance of others’ perceived humanness for prosocial behavior - with aversive affect and other-related concern when imagining harming more “human-like” persons acting against purely utilitarian decisions. PMID:23082194

  19. Discriminating Drivers through Human Factor and Behavioral Difference

    Directory of Open Access Journals (Sweden)

    Ju Seok Oh

    2011-05-01

    Full Text Available Since Greenwood and Woods' (1919 study in tendency of accident, many researchers have insisted that various human factors (sensation seeking, anger, anxiety are highly correlated with reckless driving and traffic accidents. Oh and Lee (2011 designed the Driving Behavior Determinants Questionnaire, a psychological tool to predict danger level of drivers and discriminate them into three groups (normal, unintentionally reckless, and intentionally reckless by their characteristics, attitude, and expected reckless behavior level. This tool's overall accuracy of discrimination was 70%. This study aimed to prove that the discrimination reflects the behavioral difference of drivers. Twenty-four young drivers were requested to react to the visual stimuli (tests for subjective speed sense, simple visual reaction time, and left turning at own risk. The results showed no differences in subjective speed sense among the driver groups, which means drivers' excessive speeding behaviors occur due to intention based on personality and attitude, not because of sensory disorders. In addition, there were no differences in simple reaction time among driver groups. However, the results of the ‘Left turning at drivers’ own risk task” revealed significant group differences. All reckless drivers showed a greater degree of dangerous left turning behaviors than the normal group did.

  20. Lysophospholipid presentation by CD1d and recognition by a human Natural Killer T-cell receptor

    Energy Technology Data Exchange (ETDEWEB)

    López-Sagaseta, Jacinto; Sibener, Leah V.; Kung, Jennifer E.; Gumperz, Jenny; Adams, Erin J. (UC); (UW-MED)

    2014-10-02

    Invariant Natural Killer T (iNKT) cells use highly restricted {alpha}{beta} T cell receptors (TCRs) to probe the repertoire of lipids presented by CD1d molecules. Here, we describe our studies of lysophosphatidylcholine (LPC) presentation by human CD1d and its recognition by a native, LPC-specific iNKT TCR. Human CD1d presenting LPC adopts an altered conformation from that of CD1d presenting glycolipid antigens, with a shifted {alpha}1 helix resulting in an open A pocket. Binding of the iNKT TCR requires a 7-{angstrom} displacement of the LPC headgroup but stabilizes the CD1d-LPC complex in a closed conformation. The iNKT TCR CDR loop footprint on CD1d-LPC is anchored by the conserved positioning of the CDR3{alpha} loop, whereas the remaining CDR loops are shifted, due in part to amino-acid differences in the CDR3{beta} and J{beta} segment used by this iNKT TCR. These findings provide insight into how lysophospholipids are presented by human CD1d molecules and how this complex is recognized by some, but not all, human iNKT cells.

  1. Structure of the central RNA recognition motif of human TIA-1 at 1.95 A resolution

    International Nuclear Information System (INIS)

    Kumar, Amit O.; Swenson, Matthew C.; Benning, Matthew M.; Kielkopf, Clara L.

    2008-01-01

    T-cell-restricted intracellular antigen-1 (TIA-1) regulates alternative pre-mRNA splicing in the nucleus, and mRNA translation in the cytoplasm, by recognizing uridine-rich sequences of RNAs. As a step towards understanding RNA recognition by this regulatory factor, the X-ray structure of the central RNA recognition motif (RRM2) of human TIA-1 is presented at 1.95 A resolution. Comparison with structurally homologous RRM-RNA complexes identifies residues at the RNA interfaces that are conserved in TIA-1-RRM2. The versatile capability of RNP motifs to interact with either proteins or RNA is reinforced by symmetry-related protein-protein interactions mediated by the RNP motifs of TIA-1-RRM2. Importantly, the TIA-1-RRM2 structure reveals the locations of mutations responsible for inhibiting nuclear import. In contrast with previous assumptions, the mutated residues are buried within the hydrophobic interior of the domain, where they would be likely to destabilize the RRM fold rather than directly inhibit RNA binding

  2. Scavenger receptor-mediated recognition of maleyl bovine plasma albumin and the demaleylated protein in human monocyte macrophages

    International Nuclear Information System (INIS)

    Haberland, M.E.; Fogelman, A.M.

    1985-01-01

    Maleyl bovine plasma albumin competed on an equimolar basis with malondialdehyde low density lipoprotein (LDL) in suppressing the lysosomal hydrolysis of 125 I-labeled malondialdehyde LDL mediated by the scavenger receptor of human monocyte macrophages. Maleyl bovine plasma albumin, in which 94% of the amino groups were modified, exhibited an anodic mobility in agarose electrophoresis 1.7 times that of the native protein. Incubation of maleyl bovine plasma albumin at pH 3.5 regenerated the free amino groups and restored the protein to the same electrophoretic mobility as native albumin. Although ligands recognized by the scavenger receptor typically are anionic, the authors propose that addition of new negative charge achieved by maleylation, rather than directly forming the receptor binding site(s), induces conformational changes in albumin as a prerequisite to expression of the recognition domain(s). They conclude that the primary sequence of albumin, rather than addition of new negative charge, provides the recognition determinant(s) essential for interaction of maleyl bovine plasma albumin with the scavenger receptor

  3. On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Ignacio Rojas

    2012-06-01

    Full Text Available The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

  4. Human motion behavior while interacting with an industrial robot.

    Science.gov (United States)

    Bortot, Dino; Ding, Hao; Antonopolous, Alexandros; Bengler, Klaus

    2012-01-01

    Human workers and industrial robots both have specific strengths within industrial production. Advantageously they complement each other perfectly, which leads to the development of human-robot interaction (HRI) applications. Bringing humans and robots together in the same workspace may lead to potential collisions. The avoidance of such is a central safety requirement. It can be realized with sundry sensor systems, all of them decelerating the robot when the distance to the human decreases alarmingly and applying the emergency stop, when the distance becomes too small. As a consequence, the efficiency of the overall systems suffers, because the robot has high idle times. Optimized path planning algorithms have to be developed to avoid that. The following study investigates human motion behavior in the proximity of an industrial robot. Three different kinds of encounters between the two entities under three robot speed levels are prompted. A motion tracking system is used to capture the motions. Results show, that humans keep an average distance of about 0,5m to the robot, when the encounter occurs. Approximation of the workbenches is influenced by the robot in ten of 15 cases. Furthermore, an increase of participants' walking velocity with higher robot velocities is observed.

  5. Large Scale Immune Profiling of Infected Humans and Goats Reveals Differential Recognition of Brucella melitensis Antigens

    Science.gov (United States)

    Liang, Li; Leng, Diana; Burk, Chad; Nakajima-Sasaki, Rie; Kayala, Matthew A.; Atluri, Vidya L.; Pablo, Jozelyn; Unal, Berkay; Ficht, Thomas A.; Gotuzzo, Eduardo; Saito, Mayuko; Morrow, W. John W.; Liang, Xiaowu; Baldi, Pierre; Gilman, Robert H.; Vinetz, Joseph M.; Tsolis, Renée M.; Felgner, Philip L.

    2010-01-01

    Brucellosis is a widespread zoonotic disease that is also a potential agent of bioterrorism. Current serological assays to diagnose human brucellosis in clinical settings are based on detection of agglutinating anti-LPS antibodies. To better understand the universe of antibody responses that develop after B. melitensis infection, a protein microarray was fabricated containing 1,406 predicted B. melitensis proteins. The array was probed with sera from experimentally infected goats and naturally infected humans from an endemic region in Peru. The assay identified 18 antigens differentially recognized by infected and non-infected goats, and 13 serodiagnostic antigens that differentiate human patients proven to have acute brucellosis from syndromically similar patients. There were 31 cross-reactive antigens in healthy goats and 20 cross-reactive antigens in healthy humans. Only two of the serodiagnostic antigens and eight of the cross-reactive antigens overlap between humans and goats. Based on these results, a nitrocellulose line blot containing the human serodiagnostic antigens was fabricated and applied in a simple assay that validated the accuracy of the protein microarray results in the diagnosis of humans. These data demonstrate that an experimentally infected natural reservoir host produces a fundamentally different immune response than a naturally infected accidental human host. PMID:20454614

  6. A Pattern Mining Approach to Sensor-based Human Activity Recognition

    DEFF Research Database (Denmark)

    Gu, Tao; Wang, Liang; Wu, Zhanqing

    2011-01-01

    Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only...

  7. 5,7-DHT lesion of the dorsal raphe nuclei impairs object recognition but not affective behavior and corticosterone response to stressor in the rat.

    Science.gov (United States)

    Lieben, Cindy K J; Steinbusch, Harry W M; Blokland, Arjan

    2006-04-03

    Previous studies with acute tryptophan depletion, leading to transient central 5-HT reductions, showed no effects on affective behavior but impaired object memory. In the present study, the behavioral effects of a 5,7-dihydroxytryptamine (5,7-DHT) lesion in the dorsal raphe were evaluated in animal models of anxiety (open field test), depression (forced swimming test), behavioral inhibition (discrete fixed interval test) and cognition (object recognition task). The corticosterone response to a stress condition was examined at several intervals after 5,7-DHT treatment. The substantial reduction in neuronal 5-HT markers in the dorsal raphe did not affect anxiety-related, depressive-like or impulsive behavior. Compared to the SHAM group, the lesioned rats showed a lower response latency to obtain a reward, indicating a quick and accurate reaction to a stimulus. No differences were found in the progressive ratio test for food motivation. A marked impairment in object recognition was found. The 5,7-DHT treatment did not affect the corticosterone response to a stressful situation. Overall, these results corroborate studies with acute tryptophan depletion suggesting a role of 5-HT in object memory, but not affective behavior.

  8. The Human Factor: Behavioral and Neural Correlates of Humanized Perception in Moral Decision Making

    OpenAIRE

    Majdandžić, Jasminka; Bauer, Herbert; Windischberger, Christian; Moser, Ewald; Engl, Elisabeth; Lamm, Claus

    2012-01-01

    The extent to which people regard others as full-blown individuals with mental states ("humanization") seems crucial for their prosocial motivation towards them. Previous research has shown that decisions about moral dilemmas in which one person can be sacrificed to save multiple others do not consistently follow utilitarian principles. We hypothesized that this behavior can be explained by the potential victim's perceived humanness and an ensuing increase in vicarious emotions and emotional ...

  9. A New Profile Shape Matching Stereovision Algorithm for Real-time Human Pose and Hand Gesture Recognition

    Directory of Open Access Journals (Sweden)

    Dong Zhang

    2014-02-01

    Full Text Available This paper presents a new profile shape matching stereovision algorithm that is designed to extract 3D information in real time. This algorithm obtains 3D information by matching profile intensity shapes of each corresponding row of the stereo image pair. It detects the corresponding matching patterns of the intensity profile rather than the intensity values of individual pixels or pixels in a small neighbourhood. This approach reduces the effect of the intensity and colour variations caused by lighting differences. As with all real-time vision algorithms, there is always a trade-off between accuracy and processing speed. This algorithm achieves a balance between the two to produce accurate results for real-time applications. To demonstrate its performance, the proposed algorithm is tested for human pose and hand gesture recognition to control a smart phone and an entertainment system.

  10. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Directory of Open Access Journals (Sweden)

    Nicole A Capela

    Full Text Available Human activity recognition (HAR, using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter. The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree. Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  11. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Science.gov (United States)

    Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

    2015-01-01

    Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  12. Tracking and Recognition of Multiple Human Targets Moving in a Wireless Pyroelectric Infrared Sensor Network

    Directory of Open Access Journals (Sweden)

    Ji Xiong

    2014-04-01

    Full Text Available With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.

  13. An investigation of a human in the loop approach to object recognition

    OpenAIRE

    Razeghi, Orod

    2015-01-01

    For several decades researchers around the globe have been avidly investigating practical solutions to the enduring problem of understanding visual content within an image. One might think of the quest as an effort to emulate human visual system. Despite all the endeavours, the simplest of visual tasks to us humans, such as optical segmentation of objects, remain a significant challenge for machines. In a few occasions where a computer's processing power is adequate to accomplish the task, th...

  14. Body odor quality predicts behavioral attractiveness in humans.

    Science.gov (United States)

    Roberts, S Craig; Kralevich, Alexandra; Ferdenzi, Camille; Saxton, Tamsin K; Jones, Benedict C; DeBruine, Lisa M; Little, Anthony C; Havlicek, Jan

    2011-12-01

    Growing effort is being made to understand how different attractive physical traits co-vary within individuals, partly because this might indicate an underlying index of genetic quality. In humans, attention has focused on potential markers of quality such as facial attractiveness, axillary odor quality, the second-to-fourth digit (2D:4D) ratio and body mass index (BMI). Here we extend this approach to include visually-assessed kinesic cues (nonverbal behavior linked to movement) which are statistically independent of structural physical traits. The utility of such kinesic cues in mate assessment is controversial, particularly during everyday conversational contexts, as they could be unreliable and susceptible to deception. However, we show here that the attractiveness of nonverbal behavior, in 20 male participants, is predicted by perceived quality of their axillary body odor. This finding indicates covariation between two desirable traits in different sensory modalities. Depending on two different rating contexts (either a simple attractiveness rating or a rating for long-term partners by 10 female raters not using hormonal contraception), we also found significant relationships between perceived attractiveness of nonverbal behavior and BMI, and between axillary odor ratings and 2D:4D ratio. Axillary odor pleasantness was the single attribute that consistently predicted attractiveness of nonverbal behavior. Our results demonstrate that nonverbal kinesic cues could reliably reveal mate quality, at least in males, and could corroborate and contribute to mate assessment based on other physical traits.

  15. Efficient Interaction Recognition through Positive Action Representation

    Directory of Open Access Journals (Sweden)

    Tao Hu

    2013-01-01

    Full Text Available This paper proposes a novel approach to decompose two-person interaction into a Positive Action and a Negative Action for more efficient behavior recognition. A Positive Action plays the decisive role in a two-person exchange. Thus, interaction recognition can be simplified to Positive Action-based recognition, focusing on an action representation of just one person. Recently, a new depth sensor has become widely available, the Microsoft Kinect camera, which provides RGB-D data with 3D spatial information for quantitative analysis. However, there are few publicly accessible test datasets using this camera, to assess two-person interaction recognition approaches. Therefore, we created a new dataset with six types of complex human interactions (i.e., named K3HI, including kicking, pointing, punching, pushing, exchanging an object, and shaking hands. Three types of features were extracted for each Positive Action: joint, plane, and velocity features. We used continuous Hidden Markov Models (HMMs to evaluate the Positive Action-based interaction recognition method and the traditional two-person interaction recognition approach with our test dataset. Experimental results showed that the proposed recognition technique is more accurate than the traditional method, shortens the sample training time, and therefore achieves comprehensive superiority.

  16. How Does Adult Attachment Affect Human Recognition of Love-related and Sex-related Stimuli: An ERP Study

    Science.gov (United States)

    Hou, Juan; Chen, Xin; Liu, Jinqun; Yao, Fangshu; Huang, Jiani; Ndasauka, Yamikani; Ma, Ru; Zhang, Yuting; Lan, Jing; Liu, Lu; Fang, Xiaoyi

    2016-01-01

    In the present study, we investigated the relationship among three emotion-motivation systems (adult attachment, romantic love, and sex). We recorded event-related potentials in 37 healthy volunteers who had experienced romantic love while they viewed SEX, LOVE, FRIEND, SPORT, and NEUTRAL images. We also measured adult attachment styles, level of passionate love and sexual attitudes. As expected, results showed that, firstly, response to love-related image-stimuli and sex-related image-stimuli on the electrophysiological data significantly different on N1, N2, and positive slow wave (PSW) components. Secondly, the different adult attachment styles affected individuals’ recognition processing in response to love-related and sex-related images, especially, to sex-related images. Further analysis showed that voltages elicited by fearful attachment style individuals were significantly lower than voltages elicited by secure and dismissing attachment style individuals on sex-related images at frontal sites, on N1 and N2 components. Thirdly, from behavior data, we found that adult attachment styles were not significantly related to any dimension of sexual attitudes but were significantly related to passionate love scale (PLS) total points. Thus, the behavior results were not in line with the electrophysiological results. The present study proved that adult attachment styles might mediate individuals’ lust and attraction systems. PMID:27199830

  17. Endocrinology of human female sexuality, mating, and reproductive behavior.

    Science.gov (United States)

    Motta-Mena, Natalie V; Puts, David A

    2017-05-01

    Hormones orchestrate and coordinate human female sexual development, sexuality, and reproduction in relation to three types of phenotypic changes: life history transitions such as puberty and childbirth, responses to contextual factors such as caloric intake and stress, and cyclical patterns such as the ovulatory cycle. Here, we review the endocrinology underlying women's reproductive phenotypes, including sexual orientation and gender identity, mate preferences, competition for mates, sex drive, and maternal behavior. We highlight distinctive aspects of women's sexuality such as the possession of sexual ornaments, relatively cryptic fertile windows, extended sexual behavior across the ovulatory cycle, and a period of midlife reproductive senescence-and we focus on how hormonal mechanisms were shaped by selection to produce adaptive outcomes. We conclude with suggestions for future research to elucidate how hormonal mechanisms subserve women's reproductive phenotypes. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. VISUAL PERCEPTION BASED AUTOMATIC RECOGNITION OF CELL MOSAICS IN HUMAN CORNEAL ENDOTHELIUMMICROSCOPY IMAGES

    Directory of Open Access Journals (Sweden)

    Yann Gavet

    2011-05-01

    Full Text Available The human corneal endothelium can be observed with two types of microscopes: classical optical microscope for ex-vivo imaging, and specular optical microscope for in-vivo imaging. The quality of the cornea is correlated to the endothelial cell density and morphometry. Automatic methods to analyze the human corneal endothelium images are still not totally efficient. Image analysis methods that focus only on cell contours do not give good results in presence of noise and of bad conditions of acquisition. More elaborated methods introduce regional informations in order to performthe cell contours completion, thus implementing the duality contour-region. Their good performance can be explained by their connections with several basic principles of human visual perception (Gestalt Theory and Marr's computational theory.

  19. The nature of visual self-recognition.

    Science.gov (United States)

    Suddendorf, Thomas; Butler, David L

    2013-03-01

    Visual self-recognition is often controversially cited as an indicator of self-awareness and assessed with the mirror-mark test. Great apes and humans, unlike small apes and monkeys, have repeatedly passed mirror tests, suggesting that the underlying brain processes are homologous and evolved 14-18 million years ago. However, neuroscientific, developmental, and clinical dissociations show that the medium used for self-recognition (mirror vs photograph vs video) significantly alters behavioral and brain responses, likely due to perceptual differences among the different media and prior experience. On the basis of this evidence and evolutionary considerations, we argue that the visual self-recognition skills evident in humans and great apes are a byproduct of a general capacity to collate representations, and need not index other aspects of self-awareness. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. Structural Basis for Recognition of Human Enterovirus 71 by a Bivalent Broadly Neutralizing Monoclonal Antibody.

    Directory of Open Access Journals (Sweden)

    Xiaohua Ye

    2016-03-01

    Full Text Available Enterovirus 71 (EV71 is the main pathogen responsible for hand, foot and mouth disease with severe neurological complications and even death in young children. We have recently identified a highly potent anti-EV71 neutralizing monoclonal antibody, termed D5. Here we investigated the structural basis for recognition of EV71 by the antibody D5. Four three-dimensional structures of EV71 particles in complex with IgG or Fab of D5 were reconstructed by cryo-electron microscopy (cryo-EM single particle analysis all at subnanometer resolutions. The most critical EV71 mature virion-Fab structure was resolved to a resolution of 4.8 Å, which is rare in cryo-EM studies of virus-antibody complex so far. The structures reveal a bivalent binding pattern of D5 antibody across the icosahedral 2-fold axis on mature virion, suggesting that D5 binding may rigidify virions to prevent their conformational changes required for subsequent RNA release. Moreover, we also identified that the complementary determining region 3 (CDR3 of D5 heavy chain directly interacts with the extremely conserved VP1 GH-loop of EV71, which was validated by biochemical and virological assays. We further showed that D5 is indeed able to neutralize a variety of EV71 genotypes and strains. Moreover, D5 could potently confer protection in a mouse model of EV71 infection. Since the conserved VP1 GH-loop is involved in EV71 binding with its uncoating receptor, the scavenger receptor class B, member 2 (SCARB2, the broadly neutralizing ability of D5 might attribute to its inhibition of EV71 from binding SCARB2. Altogether, our results elucidate the structural basis for the binding and neutralization of EV71 by the broadly neutralizing antibody D5, thereby enhancing our understanding of antibody-based protection against EV71 infection.

  1. Effects of oxytocin on behavioral and ERP measures of recognition memory for own-race and other-race faces in women and men.

    Science.gov (United States)

    Herzmann, Grit; Bird, Christopher W; Freeman, Megan; Curran, Tim

    2013-10-01

    Oxytocin has been shown to affect human social information processing including recognition memory for faces. Here we investigated the neural processes underlying the effect of oxytocin on memorizing own-race and other-race faces in men and women. In a placebo-controlled, double-blind, between-subject study, participants received either oxytocin or placebo before studying own-race and other-race faces. We recorded event-related potentials (ERPs) during both the study and recognition phase to investigate neural correlates of oxytocin's effect on memory encoding, memory retrieval, and perception. Oxytocin increased the accuracy of familiarity judgments in the recognition test. Neural correlates for this effect were found in ERPs related to memory encoding and retrieval but not perception. In contrast to its facilitating effects on familiarity, oxytocin impaired recollection judgments, but in men only. Oxytocin did not differentially affect own-race and other-race faces. This study shows that oxytocin influences memory, but not perceptual processes, in a face recognition task and is the first to reveal sex differences in the effect of oxytocin on face memory. Contrary to recent findings in oxytocin and moral decision making, oxytocin did not preferentially improve memory for own-race faces. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches

    KAUST Repository

    Jiang, Hanlun; Zhu, Lizhe; Hé liou, Amé lie; Gao, Xin; Bernauer, Julie; Huang, Xuhui

    2016-01-01

    that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms

  3. Epitopes of microbial and human heat shock protein 60 and their recognition in myalgic encephalomyelitis.

    Directory of Open Access Journals (Sweden)

    Amal Elfaitouri

    Full Text Available Myalgic encephalomyelitis (ME, also called Chronic Fatigue Syndrome, a common disease with chronic fatigability, cognitive dysfunction and myalgia of unknown etiology, often starts with an infection. The chaperonin human heat shock protein 60 (HSP60 occurs in mitochondria and in bacteria, is highly conserved, antigenic and a major autoantigen. The anti-HSP60 humoral (IgG and IgM immune response was studied in 69 ME patients and 76 blood donors (BD (the Training set with recombinant human and E coli HSP60, and 136 30-mer overlapping and targeted peptides from HSP60 of humans, Chlamydia, Mycoplasma and 26 other species in a multiplex suspension array. Peptides from HSP60 helix I had a chaperonin-like activity, but these and other HSP60 peptides also bound IgG and IgM with an ME preference, theoretically indicating a competition between HSP60 function and antibody binding. A HSP60-based panel of 25 antigens was selected. When evaluated with 61 other ME and 399 non-ME samples (331 BD, 20 Multiple Sclerosis and 48 Systemic Lupus Erythematosus patients, a peptide from Chlamydia pneumoniae HSP60 detected IgM in 15 of 61 (24% of ME, and in 1 of 399 non-ME at a high cutoff (p<0.0001. IgM to specific cross-reactive epitopes of human and microbial HSP60 occurs in a subset of ME, compatible with infection-induced autoimmunity.

  4. Flexible Human Behavior Analysis Framework for Video Surveillance Applications

    Directory of Open Access Journals (Sweden)

    Weilun Lao

    2010-01-01

    Full Text Available We study a flexible framework for semantic analysis of human motion from surveillance video. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. Although human motion is widely investigated, we have extended such research in three aspects. By adding a second camera, not only more reliable behavior analysis is possible, but it also enables to map the ongoing scene events onto a 3D setting to facilitate further semantic analysis. The second contribution is the introduction of a 3D reconstruction scheme for scene understanding. Thirdly, we perform a fast scheme to detect different body parts and generate a fitting skeleton model, without using the explicit assumption of upright body posture. The extension of multiple-view fusion improves the event-based semantic analysis by 15%–30%. Our proposed framework proves its effectiveness as it achieves a near real-time performance (13–15 frames/second and 6–8 frames/second for monocular and two-view video sequences.

  5. The hypoglossal canal and the origin of human vocal behavior

    Science.gov (United States)

    Kay, Richard F.; Cartmill, Matt; Balow, Michelle

    1998-01-01

    The mammalian hypoglossal canal transmits the nerve that supplies the muscles of the tongue. This canal is absolutely and relatively larger in modern humans than it is in the African apes (Pan and Gorilla). We hypothesize that the human tongue is supplied more richly with motor nerves than are those of living apes and propose that canal size in fossil hominids may provide an indication about the motor coordination of the tongue and reflect the evolution of speech and language. Canals of gracile Australopithecus, and possibly Homo habilis, fall within the range of extant Pan and are significantly smaller than those of modern Homo. The canals of Neanderthals and an early “modern” Homo sapiens (Skhul 5), as well as of African and European middle Pleistocene Homo (Kabwe and Swanscombe), fall within the range of extant Homo and are significantly larger than those of Pan troglodytes. These anatomical findings suggest that the vocal capabilities of Neanderthals were the same as those of humans today. Furthermore, the vocal abilities of Australopithecus were not advanced significantly over those of chimpanzees whereas those of Homo may have been essentially modern by at least 400,000 years ago. Thus, human vocal abilities may have appeared much earlier in time than the first archaeological evidence for symbolic behavior. PMID:9560291

  6. Irrational choice behavior in human and nonhuman primates.

    Science.gov (United States)

    Perdue, Bonnie M; Brown, Ella R

    2018-03-01

    Choice behavior in humans has motivated a large body of research with a focus on whether decisions can be considered to be rational. In general, humans prefer having choice, as do a number of other species that have been tested, even though having increased choice does not necessarily yield a positive outcome. Humans have been found to choose an option more often only because the opportunity to select it was diminishing, an example of a deviation from economic rationality. Here we extend this paradigm to nonhuman primates in an effort to understand the mechanisms underlying this finding. In this study, we presented two groups of laboratory monkeys, capuchins (Cebus apella) and rhesus macaques (Macaca mulatta), as well as human subjects, with a computerized task in which subjects were presented with two differently colored icons. When the subject selected an icon, differing numbers of food pellets were dispensed (or points were assigned), making each icon correspond to a certain level of risk (one icon yielded 1 or 4 pellets/points and the other yielded 2 or 3). Initially, both options remained constantly available and we established choice preference scores for each subject. Then, we assessed preference patterns once the options were not continuously available. Specifically, choosing one icon would cause the other to shrink in size on the screen and eventually disappear if never selected. Selecting it would restore it to its full size. As predicted, humans shifted their risk preferences in the diminishing options phase, choosing to click on both icons more equally in order to keep both options available. At the group level, capuchin monkeys showed this pattern as well, but there was a great deal of individual variability in both capuchins and macaques. The present work suggests that there is some degree of continuity between human and nonhuman primates in the desire to have choice simply for the sake of having choice.

  7. Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2015-01-01

    Full Text Available Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.

  8. Recognition and processing of a new repertoire of DNA substrates by human 3-methyladenine DNA glycosylase (AAG).

    Science.gov (United States)

    Lee, Chun-Yue I; Delaney, James C; Kartalou, Maria; Lingaraju, Gondichatnahalli M; Maor-Shoshani, Ayelet; Essigmann, John M; Samson, Leona D

    2009-03-10

    The human 3-methyladenine DNA glycosylase (AAG) recognizes and excises a broad range of purines damaged by alkylation and oxidative damage, including 3-methyladenine, 7-methylguanine, hypoxanthine (Hx), and 1,N(6)-ethenoadenine (epsilonA). The crystal structures of AAG bound to epsilonA have provided insights into the structural basis for substrate recognition, base excision, and exclusion of normal purines and pyrimidines from its substrate recognition pocket. In this study, we explore the substrate specificity of full-length and truncated Delta80AAG on a library of oligonucleotides containing structurally diverse base modifications. Substrate binding and base excision kinetics of AAG with 13 damaged oligonucleotides were examined. We found that AAG bound to a wide variety of purine and pyrimidine lesions but excised only a few of them. Single-turnover excision kinetics showed that in addition to the well-known epsilonA and Hx substrates, 1-methylguanine (m1G) was also excised efficiently by AAG. Thus, along with epsilonA and ethanoadenine (EA), m1G is another substrate that is shared between AAG and the direct repair protein AlkB. In addition, we found that both the full-length and truncated AAG excised 1,N(2)-ethenoguanine (1,N(2)-epsilonG), albeit weakly, from duplex DNA. Uracil was excised from both single- and double-stranded DNA, but only by full-length AAG, indicating that the N-terminus of AAG may influence glycosylase activity for some substrates. Although AAG has been primarily shown to act on double-stranded DNA, AAG excised both epsilonA and Hx from single-stranded DNA, suggesting the possible significance of repair of these frequent lesions in single-stranded DNA transiently generated during replication and transcription.

  9. Goat anti-rabbit IgG conjugated fluorescent dye-doped silica nanoparticles for human breast carcinoma cell recognition.

    Science.gov (United States)

    Chen, Min-Yan; Chen, Ze-Zhong; Wu, Ling-Ling; Tang, Hong-Wu; Pang, Dai-Wen

    2013-11-12

    We report an indirect method for cancer cell recognition using photostable fluorescent silica nanoprobes as biological labels. The dye-doped fluorescent silica nanoparticles were synthesized using the water-in-oil (W/O) reverse microemulsion method. The silica matrix was produced by the controlled hydrolysis of tetraethylorthosilicate (TEOS) in water nanodroplets with the initiation of ammonia (NH3·H2O). Fluorescein isothiocyanate (FITC) or rhodamine B isothiocyanate conjugated with dextran (RBITC-Dextran) was doped in silica nanoparticles (NPs) with a size of 60 ± 5 nm as a fluorescent signal element by covalent bonding and steric hindrance, respectively. The secondary antibody, goat anti-rabbit IgG, was conjugated on the surface of the PEG-terminated modified FITC-doped or RBITC-Dextran-doped silica nanoparticles (PFSiNPs or PBSiNPs) by covalent binding to the PEG linkers using the cyanogen bromide method. The concentrations of goat anti-rabbit IgG covering the nanoprobes were quantified via the Bradford method. In the proof-of-concept experiment, an epithelial cell adhesion molecule (EpCAM) on the human breast cancer SK-Br-3 cell surface was used as the tumor marker, and the nanoparticle functionalized with rabbit anti-EpCAM antibody was employed as the nanoprobe for cancer cell recognition. Compared with fluorescent dye labeled IgG (FITC-IgG and RBITC-IgG), the designed nanoprobes display dramatically increased stability of fluorescence as well as photostability under continuous irradiation.

  10. NMR and photo-CIDNP studies of human proinsulin and prohormone processing intermediates with application to endopeptidase recognition

    International Nuclear Information System (INIS)

    Weiss, M.A.; Frank, B.H.; Heiney, R.; Pekar, A.; Khait, I.; Neuringer, L.J.; Shoelson, S.E.

    1990-01-01

    The proinsulin-insulin system provides a general model for the proteolytic processing of polypeptide hormones. Two proinsulin-specific endopeptidases have been defined, a type I activity that cleaves the B-chain/C-peptide junction (Arg 31 -Arg 32 ) and a type II activity that cleaves the C-peptide/A-chain junction (Lys 64 -Arg 65 ). These endopeptidases are specific for their respective dibasic target sites; not all such dibasic sites are cleaved, however, and studies of mutant proinsulins have demonstrated that additional sequence or structural features are involved in determining substrate specificity. To define structural elements required for endopeptidase recognition, the authors have undertaken comparative 1 H NMR and photochemical dynamic nuclear polarization (photo-CIDNP) studies of human proinsulin, insulin, and split proinsulin analogues as models or prohormone processing intermediates. The overall conformation of proinsulin is observed to be similar to that of insulin, and the connecting peptide is largely unstructured. In the 1 H NMR spectrum of proinsulin significant variation is observed in the line widths of insulin-specific amide resonances, reflecting exchange among conformational substrates; similar exchange is observed in insulin and is not damped by the connecting peptide. The aromatic 1 H NMR resonances of proinsulin are assigned by analogy to the spectrum of insulin, and assignments are verified by chemical modification. These results suggest that a stable local structure is formed at the CA junction, which influences insulin-specific packing interactions. They propose that this structure (designated the CA knuckle) provides a recognition element for type II proinsulin endopeptidase

  11. Social Media Research, Human Behavior, and Sustainable Society

    Directory of Open Access Journals (Sweden)

    Quan Li

    2017-03-01

    Full Text Available A bibliometric analysis was conducted to review social media research from different perspectives during the period of 2008–2014 based on the Science Citation Index and Social Science Citation Index database. Using a collection of 10,042 articles related to social media, the bibliometric analysis revealed some interesting patterns and trend of the scientific outputs, major journals, subject categories, spatial distribution, international collaboration, and temporal evolution in keywords usage in social media studies. The research on social media has been characterized by rapid growth and dynamic collaboration, with a rising number of publications and citation. Communication, Sociology, Public, Environment & Occupational Health, Business, and Multidisciplinary Psychology were the five most common categories. Computers in Human Behavior was the journal with the most social media publications, and Computers & Education ranked first according to the average citations. The two most productive countries were the U.S. and UK, delivering about half of the publications. The proportion of China’s internationally collaborative publications was the highest. The University of Wisconsin, the University of Michigan, and Harvard University were three most productive institutions. Several keywords, such as “Facebook”, “Twitter”, “communication”, “Social Networking Sites”, “China”, “climate change”, “big data” and “social support” increasingly gained the popularity during the study period, indicating the research trends on human behavior and sustainability.

  12. Human Albumin Improves Long-Term Behavioral Sequelae After Subarachnoid Hemorrhage Through Neurovascular Remodeling.

    Science.gov (United States)

    Xie, Yi; Liu, Wenhua; Zhang, Xiaohao; Wang, Liumin; Xu, Lili; Xiong, Yunyun; Yang, Lian; Sang, Hongfei; Ye, Ruidong; Liu, Xinfeng

    2015-10-01

    Subarachnoid hemorrhage results in significant long-lasting neurologic sequelae. Here, we investigated whether human albumin improves long-term outcomes in experimental subarachnoid hemorrhage and whether neurovascular remodeling is involved in the protection of albumin. Laboratory investigation. Hospital research laboratory. Male Sprague-Dawley rats. Rats underwent subarachnoid hemorrhage by endovascular perforation. Albumin of either 0.63 or 1.25 g/kg was injected IV immediately after the surgery. Modified Garcia test, beam-walking test, novel object recognition, and Morris water maze were employed to determine the behavioral deficits. The effects of albumin on early neurovascular dysfunction and chronic synaptic plasticity were also studied. Both doses of albumin significantly improved the sensorimotor scores (F = 31.277; p = 0.001) and cognitive performance (F = 7.982; p = 0.001 in novel object recognition test; and F = 3.431; p = 0.026 in the latency analysis of Morris water maze test) for at least 40 days after subarachnoid hemorrhage. There were remarkable microvasculature hypoperfusion, intracranial pressure rise, early vasoconstriction, neural apoptosis, and degeneration in subarachnoid hemorrhage rats, with albumin significantly attenuating such neurovascular dysfunction. Furthermore, albumin markedly prevented blood-brain barrier disruption, as indicated by less blood-brain barrier leakage, preserved blood-brain barrier-related proteins, and dampened gelatinase activities. The expressions of key synaptic elements were up-regulated with albumin supplementation in both acute and chronic phases. Accordingly, a higher dendritic spine density was observed in the prefrontal and hippocampal areas of albumin-treated subarachnoid hemorrhage animals. Albumin at low-to-moderate doses markedly improves long-term neurobehavioral sequelae after subarachnoid hemorrhage, which may involve an integrated process of neurovascular remodeling.

  13. Bidirectional Modulation of Recognition Memory.

    Science.gov (United States)

    Ho, Jonathan W; Poeta, Devon L; Jacobson, Tara K; Zolnik, Timothy A; Neske, Garrett T; Connors, Barry W; Burwell, Rebecca D

    2015-09-30

    Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects. For example, animals and humans with perirhinal damage are unable to distinguish familiar from novel objects in recognition memory tasks. In the normal brain, perirhinal neurons respond to novelty and familiarity by increasing or decreasing firing rates. Recent work also implicates oscillatory activity in the low-beta and low-gamma frequency bands in sensory detection, perception, and recognition. Using optogenetic methods in a spontaneous object exploration (SOR) task, we altered recognition memory performance in rats. In the SOR task, normal rats preferentially explore novel images over familiar ones. We modulated exploratory behavior in this task by optically stimulating channelrhodopsin-expressing perirhinal neurons at various frequencies while rats looked at novel or familiar 2D images. Stimulation at 30-40 Hz during looking caused rats to treat a familiar image as if it were novel by increasing time looking at the image. Stimulation at 30-40 Hz was not effective in increasing exploration of novel images. Stimulation at 10-15 Hz caused animals to treat a novel image as familiar by decreasing time looking at the image, but did not affect looking times for images that were already familiar. We conclude that optical stimulation of PER at different frequencies can alter visual recognition memory bidirectionally. Significance statement: Recognition of novelty and familiarity are important for learning, memory, and decision making. Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects, but how novelty and familiarity are encoded and transmitted in the brain is not known. Perirhinal neurons respond to novelty and familiarity by changing firing rates, but recent work suggests that brain oscillations may also be important for recognition. In this study, we showed that stimulation of

  14. Human T cell recognition of the blood stage antigen Plasmodium hypoxanthine guanine xanthine phosphoribosyl transferase (HGXPRT in acute malaria

    Directory of Open Access Journals (Sweden)

    Woodberry Tonia

    2009-06-01

    Full Text Available Abstract Background The Plasmodium purine salvage enzyme, hypoxanthine guanine xanthine phosphoribosyl transferase (HGXPRT can protect mice against Plasmodium yoelii pRBC challenge in a T cell-dependent manner and has, therefore, been proposed as a novel vaccine candidate. It is not known whether natural exposure to Plasmodium falciparum stimulates HGXPRT T cell reactivity in humans. Methods PBMC and plasma collected from malaria-exposed Indonesians during infection and 7–28 days after anti-malarial therapy, were assessed for HGXPRT recognition using CFSE proliferation, IFNγ ELISPOT assay and ELISA. Results HGXPRT-specific T cell proliferation was found in 44% of patients during acute infection; in 80% of responders both CD4+ and CD8+ T cell subsets proliferated. Antigen-specific T cell proliferation was largely lost within 28 days of parasite clearance. HGXPRT-specific IFN-γ production was more frequent 28 days after treatment than during acute infection. HGXPRT-specific plasma IgG was undetectable even in individuals exposed to malaria for at least two years. Conclusion The prevalence of acute proliferative and convalescent IFNγ responses to HGXPRT demonstrates cellular immunogenicity in humans. Further studies to determine minimal HGXPRT epitopes, the specificity of responses for Plasmodia and associations with protection are required. Frequent and robust T cell proliferation, high sequence conservation among Plasmodium species and absent IgG responses distinguish HGXPRT from other malaria antigens.

  15. Preparation of a molecularly imprinted sensor based on quartz crystal microbalance for specific recognition of sialic acid in human urine.

    Science.gov (United States)

    Qiu, Xiuzhen; Xu, Xian-Yan; Chen, Xuncai; Wu, Yiyong; Guo, Huishi

    2018-05-08

    A novel molecularly imprinted quartz crystal microbalance (QCM) sensor was successfully prepared for selective determination of sialic acid (SA) in human urine samples. To obtain the QCM sensor, we first modified the gold surface of the QCM chip by self-assembling of allylmercaptane to introduce polymerizable double bonds on the chip surface. Then, SA molecularly imprinted polymer (MIP) nanofilm was attached to the modified QCM chip surface. For comparison, we have also characterized the nonmodified and improved surfaces of the QCM sensor by using atomic force microscopy (AFM) and Fourier transform infrared (FTIR) spectroscopy. We then tested the selectivity and detection limit of the imprinted QCM sensor via a series of adsorption experiments. The results show a linear response in the range of 0.025-0.50 μmol L -1 for sialic acid. Moreover, the limit of detection (LOD) of the prepared imprinted QCM sensor was found to be 1.0 nmol L -1 for sialic acid, and high recovery values range from 87.6 to 108.5% with RSD sensor was developed and used to detect sialic acid in human urine samples. Graphical abstract Specific recognition of sialic acid by the MIP-QCM sensor system.

  16. Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions

    Directory of Open Access Journals (Sweden)

    Grigorios Kalliatakis

    2017-07-01

    Full Text Available Affective computing in general and human activity and intention analysis in particular comprise a rapidly-growing field of research. Head pose and emotion changes present serious challenges when applied to player’s training and ludology experience in serious games, or analysis of customer satisfaction regarding broadcast and web services, or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D data for subsequent analysis. Discriminative random regression forests were selected in order to rapidly and accurately estimate head pose changes in an unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise, emotion recognition via facial expressions (ERFE was adopted. After that, a lightweight data exchange format (JavaScript Object Notation (JSON is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need to generate comprehensible visual representations from different sets of data, in this paper, we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor.

  17. Immune Recognition of Latency-insitigating Pathogens by Human Dendritic Cells

    DEFF Research Database (Denmark)

    Søndergaard, Jonas Nørskov

    for society. Consequently there is a pressing need to search for new treatment strategies. Nowadays it is known that HIV-1 and Mtb have acquired the ability to escape the removal from the body by exploiting the immune system for their own benefits. Dendritic cells (DCs) determine the way the immune response......Latent infections with the human pathogenic microorganisms Mycobacterium tuberculosis (Mtb) and the human immunodeficiency virus (HIV) are creating some of the most devastating pandemics to date, with great impact on the infected people’s lives, their expected lifetime, as well as general costs...... unfolds by signaling other immune cells how to respond. An early deregulation of the DCs may therefore propagate into detrimental effects in later stages of the immune response, and may permit HIV-1 and Mtb to become latent. Hence, understanding the way HIV-1 and Mtb interacts with DCs could lead to novel...

  18. Alterations in grooming activity and syntax in heterozygous SERT and BDNF knockout mice: the utility of behavior-recognition tools to characterize mutant mouse phenotypes.

    Science.gov (United States)

    Kyzar, Evan J; Pham, Mimi; Roth, Andrew; Cachat, Jonathan; Green, Jeremy; Gaikwad, Siddharth; Kalueff, Allan V

    2012-12-01

    Serotonin transporter (SERT) and brain-derived neurotrophic factor (BDNF) are key modulators of molecular signaling, cognition and behavior. Although SERT and BDNF mutant mouse phenotypes have been extensively characterized, little is known about their self-grooming behavior. Grooming represents an important behavioral domain sensitive to environmental stimuli and is increasingly used as a model for repetitive behavioral syndromes, such as autism and attention deficit/hyperactivity disorder. The present study used heterozygous ((+/-)) SERT and BDNF male mutant mice on a C57BL/6J background and assessed their spontaneous self-grooming behavior applying both manual and automated techniques. Overall, SERT(+/-) mice displayed a general increase in grooming behavior, as indicated by more grooming bouts and more transitions between specific grooming stages. SERT(+/-) mice also aborted more grooming bouts, but showed generally unaltered activity levels in the observation chamber. In contrast, BDNF(+/-) mice displayed a global reduction in grooming activity, with fewer bouts and transitions between specific grooming stages, altered grooming syntax, as well as hypolocomotion and increased turning behavior. Finally, grooming data collected by manual and automated methods (HomeCageScan) significantly correlated in our experiments, confirming the utility of automated high-throughput quantification of grooming behaviors in various genetic mouse models with increased or decreased grooming phenotypes. Taken together, these findings indicate that mouse self-grooming behavior is a reliable behavioral biomarker of genetic deficits in SERT and BDNF pathways, and can be reliably measured using automated behavior-recognition technology. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. Effects of cigarette smoking on human aggressive behavior.

    Science.gov (United States)

    Cherek, D R

    1984-01-01

    Nicotine administered by smoking experimental cigarettes produced decreases in two types of aggressive responses elicited by low and high frequency subtractions of money which were attributed to another "person". The suppressing effects of smoking different doses of nicotine on aggressive responses was dose-dependent, in that smoking the high dose of nicotine produced more suppression than smoking the low dose. The ostensible subtraction of money from another "person", the more aggressive response option available to research subjects, was generally more sensitive to the suppressing effects of nicotine than aggressive noise delivery responses. Although this effect could be attributed to another constituent of tobacco, the dose-dependent effect observed with these cigarettes which contained the same amount of tar suggest the effects are due to nicotine. The relatively selective suppression of aggressive behavior observed in humans in the present study is highly consistent with the effects of nicotine observed in a number of infrahuman species. Nicotine has been found to suppress aggressive behavior in ants (Kostowski 1968), rats (Silverman 1971), and cats (Berntson et. al. 1976). In addition, nicotine has been observed to suppress shock elicited fighting in rats (Driscoll, Baettig 1981; Rodgers 1979; Waldbillig 1980) as well as shock elicited biting in monkeys (Hutchinson, Emley 1973). The importance of determining specificity of drug action on aggressive behavior has been repeatedly emphasized in the field of behavioral pharmacology (Sidman 1959; Cook, Kelleher 1963; Thompson, Boren 1977; Miczek, Krsiak 1979). One method employed to evaluate drug specificity and identify a general non-specific excitatory or depressant drug effect is to determine the drug effect on more than one response option which is available to the subject (Sidman 1959). In this study, the same doses of nicotine which suppressed aggressive responding increased nonaggressive monetary

  20. Computer graphics of SEM images facilitate recognition of chromosome position in isolated human metaphase plates.

    Science.gov (United States)

    Hodge, L D; Barrett, J M; Welter, D A

    1995-04-01

    There is general agreement that at the time of mitosis chromosomes occupy precise positions and that these positions likely affect subsequent nuclear function in interphase. However, before such ideas can be investigated in human cells, it is necessary to determine first the precise position of each chromosome with regard to its neighbors. It has occurred to us that stereo images, produced by scanning electron microscopy, of isolated metaphase plates could form the basis whereby these positions could be ascertained. In this paper we describe a computer graphic technique that permits us to keep track of individual chromosomes in a metaphase plate and to compare chromosome positions in different metaphase plates. Moreover, the computer graphics provide permanent, easily manipulated, rapid recall of stored chromosome profiles. These advantages are demonstrated by a comparison of the relative position of group A-specific and groups D- and G-specific chromosomes to the full complement of chromosomes in metaphase plates isolated from a nearly triploid human-derived cell (HeLa S3) to a hypo-diploid human fetal lung cell.