Analysis of Android Device-Based Solutions for Fall Detection

Directory of Open Access Journals (Sweden)

Eduardo Casilari

2015-07-01

Full Text Available Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources to fall detection solutions.

Analysis of Android Device-Based Solutions for Fall Detection.

Science.gov (United States)

Casilari, Eduardo; Luque, Rafael; Morón, María-José

2015-07-23

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.

Analysis of Android Device-Based Solutions for Fall Detection

Science.gov (United States)

Casilari, Eduardo; Luque, Rafael; Morón, María-José

2015-01-01

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions. PMID:26213928

Fall detection in walking robots by multi-way principal component analysis

NARCIS (Netherlands)

Karssen, J.G.; Wisse, M.

2008-01-01

Large disturbances can cause a biped to fall. If an upcoming fall can be detected, damage can be minimized or the fall can be prevented. We introduce the multi-way principal component analysis (MPCA) method for the detection of upcoming falls. We study the detection capability of the MPCA method in

Home Camera-Based Fall Detection System for the Elderly.

Science.gov (United States)

de Miguel, Koldo; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

2017-12-09

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Automated lung nodule classification following automated nodule detection on CT: A serial approach

International Nuclear Information System (INIS)

Armato, Samuel G. III; Altman, Michael B.; Wilkie, Joel; Sone, Shusuke; Li, Feng; Doi, Kunio; Roy, Arunabha S.

2003-01-01

We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the 'detection task' and the 'classification task'. Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation

Home Camera-Based Fall Detection System for the Elderly

Directory of Open Access Journals (Sweden)

Koldo de Miguel

2017-12-01

Full Text Available Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Falls event detection using triaxial accelerometry and barometric pressure measurement.

Science.gov (United States)

Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Celler, Branko G; Lovell, Nigel H

2009-01-01

A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

Automated Detection of HONcode Website Conformity Compared to Manual Detection: An Evaluation.

Science.gov (United States)

Boyer, Célia; Dolamic, Ljiljana

2015-06-02

To earn HONcode certification, a website must conform to the 8 principles of the HONcode of Conduct In the current manual process of certification, a HONcode expert assesses the candidate website using precise guidelines for each principle. In the scope of the European project KHRESMOI, the Health on the Net (HON) Foundation has developed an automated system to assist in detecting a website's HONcode conformity. Automated assistance in conducting HONcode reviews can expedite the current time-consuming tasks of HONcode certification and ongoing surveillance. Additionally, an automated tool used as a plugin to a general search engine might help to detect health websites that respect HONcode principles but have not yet been certified. The goal of this study was to determine whether the automated system is capable of performing as good as human experts for the task of identifying HONcode principles on health websites. Using manual evaluation by HONcode senior experts as a baseline, this study compared the capability of the automated HONcode detection system to that of the HONcode senior experts. A set of 27 health-related websites were manually assessed for compliance to each of the 8 HONcode principles by senior HONcode experts. The same set of websites were processed by the automated system for HONcode compliance detection based on supervised machine learning. The results obtained by these two methods were then compared. For the privacy criterion, the automated system obtained the same results as the human expert for 17 of 27 sites (14 true positives and 3 true negatives) without noise (0 false positives). The remaining 10 false negative instances for the privacy criterion represented tolerable behavior because it is important that all automatically detected principle conformities are accurate (ie, specificity [100%] is preferred over sensitivity [58%] for the privacy criterion). In addition, the automated system had precision of at least 75%, with a recall of more

A simple fall detection algorithm for Powered Two Wheelers

OpenAIRE

BOUBEZOUL, Abderrahmane; ESPIE, Stéphane; LARNAUDIE, Bruno; BOUAZIZ, Samir

2013-01-01

The aim of this study is to evaluate a low-complexity fall detection algorithm, that use both acceleration and angular velocity signals to trigger an alert-system or to inflate an airbag jacket. The proposed fall detection algorithm is a threshold-based algorithm, using data from 3-accelerometers and 3-gyroscopes sensors mounted on the motorcycle. During the first step, the commonly fall accident configurations were selected and analyzed in order to identify the main causation factors. On the...

Statistical control chart and neural network classification for improving human fall detection

KAUST Repository

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

2017-01-01

This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow's fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.

Statistical control chart and neural network classification for improving human fall detection

KAUST Repository

Harrou, Fouzi

2017-01-05

This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow\\'s fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.

Analysis of Public Datasets for Wearable Fall Detection Systems.

Science.gov (United States)

Casilari, Eduardo; Santoyo-Ramón, José-Antonio; Cano-García, José-Manuel

2017-06-27

Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

Directory of Open Access Journals (Sweden)

Mohammad Ashfak Habib

2014-04-01

Full Text Available This paper presents a state-of-the-art survey of smartphone (SP-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers’ interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems.

Mobile Phone Based Falling Detection Sensor and Computer-Aided Algorithm for Elderly People

Directory of Open Access Journals (Sweden)

Lee Jong-Ha

2016-01-01

Full Text Available Falls are dangerous for the elderly population; therefore many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy.

Fusion of Color and Depth Camera Data for Robust Fall Detection

NARCIS (Netherlands)

Josemans, W.; Englebienne, G.; Kröse, B.; Battiato, S.; Braz, J.

2013-01-01

The availability of cheap imaging sensors makes it possible to increase the robustness of vision-based alarm systems. This paper explores the benefit of data fusion in the application of fall detection. Falls are a common source of injury for elderly people and automatic fall detection is,

Monitoring of bedridden patients: development of a fall detection tool.

Science.gov (United States)

Vilas-Boas, M; Silva, P; Cunha, S R; Correia, M V

2013-01-01

Falls of patients are an important issue in hospitals nowadays; it causes severe injuries, increases hospitalization time and treatment costs. The detection of a fall, in time, provides faster rescue to the patient, preventing more serious injuries, as well as saving nursing time. The MovinSense® is an electronic device designed for monitoring patients to prevent pressure sores, and the main goal of this work was to develop a new tool for this device, with the purpose of detecting if the patient has fallen from the hospital bed, without changing any of the device's original features. Experiments for gathering data samples of inertial signals of falling from the bed were obtained using the device. For fall detection a sensitivity of 72% and specificity of 100% were reached. Another algorithm was developed to detect if the patient got out of his/her bed.

A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

KAUST Repository

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

2016-01-01

Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures

Analysis of Public Datasets for Wearable Fall Detection Systems

Directory of Open Access Journals (Sweden)

Eduardo Casilari

2017-06-01

Full Text Available Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs. In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.. Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

Detecting inpatient falls by using natural language processing of electronic medical records

Directory of Open Access Journals (Sweden)

Toyabe Shin-ichi

2012-12-01

Full Text Available Abstract Background Incident reporting is the most common method for detecting adverse events in a hospital. However, under-reporting or non-reporting and delay in submission of reports are problems that prevent early detection of serious adverse events. The aim of this study was to determine whether it is possible to promptly detect serious injuries after inpatient falls by using a natural language processing method and to determine which data source is the most suitable for this purpose. Methods We tried to detect adverse events from narrative text data of electronic medical records by using a natural language processing method. We made syntactic category decision rules to detect inpatient falls from text data in electronic medical records. We compared how often the true fall events were recorded in various sources of data including progress notes, discharge summaries, image order entries and incident reports. We applied the rules to these data sources and compared F-measures to detect falls between these data sources with reference to the results of a manual chart review. The lag time between event occurrence and data submission and the degree of injury were compared. Results We made 170 syntactic rules to detect inpatient falls by using a natural language processing method. Information on true fall events was most frequently recorded in progress notes (100%, incident reports (65.0% and image order entries (12.5%. However, F-measure to detect falls using the rules was poor when using progress notes (0.12 and discharge summaries (0.24 compared with that when using incident reports (1.00 and image order entries (0.91. Since the results suggested that incident reports and image order entries were possible data sources for prompt detection of serious falls, we focused on a comparison of falls found by incident reports and image order entries. Injury caused by falls found by image order entries was significantly more severe than falls detected by

Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

Directory of Open Access Journals (Sweden)

Quoc T. Huynh

2015-01-01

Full Text Available Falling is a common and significant cause of injury in elderly adults (>65 yrs old, often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling. Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

Wireless Falling Detection System Based on Community.

Science.gov (United States)

Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

2015-06-01

The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

Automated detection of fundus photographic red lesions in diabetic retinopathy.

Science.gov (United States)

Larsen, Michael; Godt, Jannik; Larsen, Nicolai; Lund-Andersen, Henrik; Sjølie, Anne Katrin; Agardh, Elisabet; Kalm, Helle; Grunkin, Michael; Owens, David R

2003-02-01

To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

Science.gov (United States)

Putra, I Putu Edy Suardiyana; Brusey, James; Gaura, Elena; Vesilo, Rein

2017-12-22

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k -nearest neighbor ( k -NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

Automated baseline change detection phase I. Final report

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-12-01

The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER&WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements.

Automated baseline change detection phase I. Final report

International Nuclear Information System (INIS)

1995-12-01

The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER ampersand WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements

Automated early detection of diabetic retinopathy

NARCIS (Netherlands)

Abràmoff, M.D.; Reinhardt, J.M.; Russell, S.R.; Folk, J.C.; Mahajan, V.B.; Niemeijer, M.; Quellec, G.

2010-01-01

Purpose To compare the performance of automated diabetic retinopathy (DR) detection, using the algorithm that won the 2009 Retinopathy Online Challenge Competition in 2009, the Challenge2009, against that of the one currently used in EyeCheck, a large computer-aided early DR detection project.

Fall detection using supervised machine learning algorithms: A comparative study

KAUST Repository

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

2017-01-01

Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

Fall detection using supervised machine learning algorithms: A comparative study

KAUST Repository

Zerrouki, Nabil

2017-01-05

Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK

Directory of Open Access Journals (Sweden)

Hamideh Kerdegari

2013-02-01

Full Text Available This paper presents an accurate wearable fall detection system which can identify the occurrence of falls among elderly population. A waist worn tri-axial accelerometer was used to capture the movement signals of human body. A set of laboratory-based falls and activities of daily living (ADL were performed by volunteers with different physical characteristics. The collected acceleration patterns were classified precisely to fall and ADL using multilayer perceptron (MLP neural network. This work was resulted to a high accuracy wearable fall-detection system with the accuracy of 91.6%.

A vision-based fall detection algorithm of human in indoor environment

Science.gov (United States)

Liu, Hao; Guo, Yongcai

2017-02-01

Elderly care becomes more and more prominent in China as the population is aging fast and the number of aging population is large. Falls, as one of the biggest challenges in elderly guardianship system, have a serious impact on both physical health and mental health of the aged. Based on feature descriptors, such as aspect ratio of human silhouette, velocity of mass center, moving distance of head and angle of the ultimate posture, a novel vision-based fall detection method was proposed in this paper. A fast median method of background modeling with three frames was also suggested. Compared with the conventional bounding box and ellipse method, the novel fall detection technique is not only applicable for recognizing the fall behaviors end of lying down but also suitable for detecting the fall behaviors end of kneeling down and sitting down. In addition, numerous experiment results showed that the method had a good performance in recognition accuracy on the premise of not adding the cost of time.

Automated system for crack detection using infrared thermograph

International Nuclear Information System (INIS)

Starman, Stanislav

2009-01-01

The objective of this study was the development of the automated system for crack detection on square steel bars used in the automotive industry for axle and shaft construction. The automated system for thermographic crack detection uses brief pulsed eddy currents to heat steel components under inspection. Cracks, if present, will disturb the current flow and so generate changes in the temperature profile in the crack area. These changes of temperature are visualized using an infrared camera. The image acquired by the infrared camera is evaluated through an image processing system. The advantages afforded by the system are its inspection time, its excellent flaw detection sensitivity and its ability to detect hidden, subsurface cracks. The automated system consists of four IR cameras (each side of steel bar is evaluated at a time), coil, high frequency generator and control place with computers. The system is a part of the inspection line where the subsurface and surface cracks are searched. If the crack is present, the cracked place is automatically marked. The components without cracks are then deposited apart from defective blocks. The system is fully automated and its ability is to evaluate four meter blocks within 20 seconds. This is the real reason for using this system in real industrial applications. (author)

Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection.

Science.gov (United States)

Hättenschwiler, Nicole; Sterchi, Yanik; Mendes, Marcia; Schwaninger, Adrian

2018-10-01

Bomb attacks on civil aviation make detecting improvised explosive devices and explosive material in passenger baggage a major concern. In the last few years, explosive detection systems for cabin baggage screening (EDSCB) have become available. Although used by a number of airports, most countries have not yet implemented these systems on a wide scale. We investigated the benefits of EDSCB with two different levels of automation currently being discussed by regulators and airport operators: automation as a diagnostic aid with an on-screen alarm resolution by the airport security officer (screener) or EDSCB with an automated decision by the machine. The two experiments reported here tested and compared both scenarios and a condition without automation as baseline. Participants were screeners at two international airports who differed in both years of work experience and familiarity with automation aids. Results showed that experienced screeners were good at detecting improvised explosive devices even without EDSCB. EDSCB increased only their detection of bare explosives. In contrast, screeners with less experience (tenure automated decision provided better human-machine detection performance than on-screen alarm resolution and no automation. This came at the cost of slightly higher false alarm rates on the human-machine system level, which would still be acceptable from an operational point of view. Results indicate that a wide-scale implementation of EDSCB would increase the detection of explosives in passenger bags and automated decision instead of automation as diagnostic aid with on screen alarm resolution should be considered. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

Science.gov (United States)

He, Jian; Bai, Shuang; Wang, Xiaoyi

2017-06-16

Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

Wearable technology and ECG processing for fall risk assessment, prevention and detection.

Science.gov (United States)

Melillo, Paolo; Castaldo, Rossana; Sannino, Giovanna; Orrico, Ada; de Pietro, Giuseppe; Pecchia, Leandro

2015-01-01

Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

Accurate Fall Detection in a Top View Privacy Preserving Configuration.

Science.gov (United States)

Ricciuti, Manola; Spinsante, Susanna; Gambi, Ennio

2018-05-29

Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.

A simple strategy for fall events detection

KAUST Repository

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

2017-01-01

the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart

New Advances and Challenges of Fall Detection Systems: A Survey

Directory of Open Access Journals (Sweden)

Tao Xu

2018-03-01

Full Text Available Falling, as one of the main harm threats to the elderly, has drawn researchers’ attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in understanding current developments and barriers in the systems of fall detection. Although researchers achieve fruitful work and progress, this research domain still confronts challenges on theories and practice. In the near future, the new solutions based on advanced IoT will sustainably urge the development to prevent falling injuries.

Detection of falls using accelerometers and mobile phone technology.

Science.gov (United States)

Lee, Raymond Y W; Carlisle, Alison J

2011-11-01

to study the sensitivity and specificity of fall detection using mobile phone technology. an experimental investigation using motion signals detected by the mobile phone. the research was conducted in a laboratory setting, and 18 healthy adults (12 males and 6 females; age = 29 ± 8.7 years) were recruited. each participant was requested to perform three trials of four different types of simulated falls (forwards, backwards, lateral left and lateral right) and eight other everyday activities (sit-to-stand, stand-to-sit, level walking, walking up- and downstairs, answering the phone, picking up an object and getting up from supine). Acceleration was measured using two devices, a mobile phone and an independent accelerometer attached to the waist of the participants. Bland-Altman analysis shows a higher degree of agreement between the data recorded by the two devices. Using individual upper and lower detection thresholds, the specificity and sensitivity for mobile phone were 0.81 and 0.77, respectively, and for external accelerometer they were 0.82 and 0.96, respectively. fall detection using a mobile phone is a feasible and highly attractive technology for older adults, especially those living alone. It may be best achieved with an accelerometer attached to the waist, which transmits signals wirelessly to a phone.

Automated detection of exudates for diabetic retinopathy screening

International Nuclear Information System (INIS)

Fleming, Alan D; Philip, Sam; Goatman, Keith A; Williams, Graeme J; Olson, John A; Sharp, Peter F

2007-01-01

Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy

Automated detection of exudates for diabetic retinopathy screening

Energy Technology Data Exchange (ETDEWEB)

Fleming, Alan D [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Philip, Sam [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Goatman, Keith A [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Williams, Graeme J [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Olson, John A [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Sharp, Peter F [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom)

2007-12-21

Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.

Automated detection of exudates for diabetic retinopathy screening

Science.gov (United States)

Fleming, Alan D.; Philip, Sam; Goatman, Keith A.; Williams, Graeme J.; Olson, John A.; Sharp, Peter F.

2007-12-01

Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.

Automated Detection of Sepsis Using Electronic Medical Record Data: A Systematic Review.

Science.gov (United States)

Despins, Laurel A

Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

Automated radiometric detection of bacteria

International Nuclear Information System (INIS)

Waters, J.R.

1974-01-01

A new radiometric method called BACTEC, used for the detection of bacteria in cultures or in supposedly sterile samples, was discussed from the standpoint of methodology, both automated and semi-automated. Some of the results obtained so far were reported and some future applications and development possibilities were described. In this new method, the test sample is incubated in a sealed vial with a liquid culture medium containing a 14 C-labeled substrate. If bacteria are present, they break down the substrate, producing 14 CO 2 which is periodically extracted from the vial as a gas and is tested for radioactivity. If this gaseous radioactivity exceeds a threshold level, it is evidence of bacterial presence and growth in the test vial. The first application was for the detection of bacteria in the blood cultures of hospital patients. Data were presented showing typical results. Also discussed were future applications, such as rapid screening for bacteria in urine industrial sterility testing and the disposal of used 14 C substrates. (Mukohata, S.)

The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy.

Science.gov (United States)

Fleming, Alan D; Goatman, Keith A; Philip, Sam; Williams, Graeme J; Prescott, Gordon J; Scotland, Graham S; McNamee, Paul; Leese, Graham P; Wykes, William N; Sharp, Peter F; Olson, John A

2010-06-01

Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy. Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection. Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload. Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.

Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer

International Nuclear Information System (INIS)

Nyan, M N; Tay, Francis E H; Manimaran, M; Seah, K H W

2006-01-01

This paper studied the detection of falls and activities of daily living (ADL) with two objectives: (1) minimum number of sensors for a broad range of activities and (2) maximize the comfort of the wearer for long term use. We used a garment to provide long term comfort for the wearer, with a 3-axis MEMS accelerometer on the shoulder position, as a wearable platform. ADL were detected in time-frequency domain and summation of absolute peak values of 3-D acceleration signals was used as feature in fall detection. 6 male and female subjects performed approximately five-hour long experiment. Sensitivity of 94.98% and specificity of 98.83% for altogether 1495 activities were achieved. Our garment-based detection system fulfilled the objective of providing the comfort of the wearer in long term monitoring of falls and ADL with high sensitivity. In fall detection, our device can summon medical assistances via SMS (Short Message Service). This detection system can raise fall alarm (fall SMS) automatically to individuals to get a shortened interval of the arrival of assistance

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

Science.gov (United States)

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system.

Science.gov (United States)

Shieh, Wann-Yun; Huang, Ju-Chin

2012-09-01

For most elderly, unpredictable falling incidents may occur at the corner of stairs or a long corridor due to body frailty. If we delay to rescue a falling elder who is likely fainting, more serious consequent injury may occur. Traditional secure or video surveillance systems need caregivers to monitor a centralized screen continuously, or need an elder to wear sensors to detect falling incidents, which explicitly waste much human power or cause inconvenience for elders. In this paper, we propose an automatic falling-detection algorithm and implement this algorithm in a multi-camera video surveillance system. The algorithm uses each camera to fetch the images from the regions required to be monitored. It then uses a falling-pattern recognition algorithm to determine if a falling incident has occurred. If yes, system will send short messages to someone needs to be noticed. The algorithm has been implemented in a DSP-based hardware acceleration board for functionality proof. Simulation results show that the accuracy of falling detection can achieve at least 90% and the throughput of a four-camera surveillance system can be improved by about 2.1 times. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

Retrieval-travel-time model for free-fall-flow-rack automated storage and retrieval system

Science.gov (United States)

Metahri, Dhiyaeddine; Hachemi, Khalid

2018-03-01

Automated storage and retrieval systems (AS/RSs) are material handling systems that are frequently used in manufacturing and distribution centers. The modelling of the retrieval-travel time of an AS/RS (expected product delivery time) is practically important, because it allows us to evaluate and improve the system throughput. The free-fall-flow-rack AS/RS has emerged as a new technology for drug distribution. This system is a new variation of flow-rack AS/RS that uses an operator or a single machine for storage operations, and uses a combination between the free-fall movement and a transport conveyor for retrieval operations. The main contribution of this paper is to develop an analytical model of the expected retrieval-travel time for the free-fall flow-rack under a dedicated storage assignment policy. The proposed model, which is based on a continuous approach, is compared for accuracy, via simulation, with discrete model. The obtained results show that the maximum deviation between the continuous model and the simulation is less than 5%, which shows the accuracy of our model to estimate the retrieval time. The analytical model is useful to optimise the dimensions of the rack, assess the system throughput, and evaluate different storage policies.

FPGA-Based Real-Time Motion Detection for Automated Video Surveillance Systems

Directory of Open Access Journals (Sweden)

Sanjay Singh

2016-03-01

Full Text Available Design of automated video surveillance systems is one of the exigent missions in computer vision community because of their ability to automatically select frames of interest in incoming video streams based on motion detection. This research paper focuses on the real-time hardware implementation of a motion detection algorithm for such vision based automated surveillance systems. A dedicated VLSI architecture has been proposed and designed for clustering-based motion detection scheme. The working prototype of a complete standalone automated video surveillance system, including input camera interface, designed motion detection VLSI architecture, and output display interface, with real-time relevant motion detection capabilities, has been implemented on Xilinx ML510 (Virtex-5 FX130T FPGA platform. The prototyped system robustly detects the relevant motion in real-time in live PAL (720 × 576 resolution video streams directly coming from the camera.

Fall Down Detection Under Smart Home System.

Science.gov (United States)

Juang, Li-Hong; Wu, Ming-Ni

2015-10-01

Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used.

A review on automated pavement distress detection methods

NARCIS (Netherlands)

Coenen, Tom B.J.; Golroo, Amir

2017-01-01

In recent years, extensive research has been conducted on pavement distress detection. A large part of these studies applied automated methods to capture different distresses. In this paper, a literature review on the distresses and related detection methods are presented. This review also includes

The state of knowledge on technologies and their use for fall detection: A scoping review.

Science.gov (United States)

Lapierre, N; Neubauer, N; Miguel-Cruz, A; Rios Rincon, A; Liu, L; Rousseau, J

2018-03-01

Globally, populations are aging with increasing life spans. The normal aging process and the resulting disabilities increase fall risks. Falls are an important cause of injury, loss of independence and institutionalization. Technologies have been developed to detect falls and reduce their consequences but their use and impact on quality of life remain debatable. Reviews on fall detection technologies exist but are not extensive. A comprehensive literature review on the state of knowledge of fall detection technologies can inform research, practice, and user adoption. To examine the extent and the diversity of current technologies for fall detection in older adults. A scoping review design was used to search peer-reviewed literature on technologies to detect falls, published in English, French or Spanish since 2006. Data from the studies were analyzed descriptively. The literature search identified 3202 studies of which 118 were included for analysis. Ten types of technologies were identified ranging from wearable (e.g., inertial sensors) to ambient sensors (e.g., vision sensors). Their Technology Readiness Level was low (mean 4.54 SD 1.25; 95% CI [4.31, 4.77] out of a maximum of 9). Outcomes were typically evaluated on technological basis and in controlled environments. Few were evaluated in home settings or care units with older adults. Acceptability, implementation cost and barriers were seldom addressed. Further research should focus on increasing Technology Readiness Levels of fall detection technologies by testing them in real-life settings with older adults. Copyright © 2017 Elsevier B.V. All rights reserved.

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time.

Science.gov (United States)

Greenlee, Eric T; DeLucia, Patricia R; Newton, David C

2018-03-01

The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Participants "drove" a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer.

Science.gov (United States)

Sucerquia, Angela; López, José David; Vargas-Bonilla, Jesús Francisco

2018-04-05

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

Automated spoof-detection for fingerprints using optical coherence tomography

CSIR Research Space (South Africa)

Darlow, LN

2016-05-01

Full Text Available that they are highly separable, resulting in 100% accuracy regarding spoof-detection, with no false rejections of real fingers. This is the first attempt at fully automated spoof-detection using OCT....

Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.

Science.gov (United States)

Goatman, Keith; Charnley, Amanda; Webster, Laura; Nussey, Stephen

2011-01-01

To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography. Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were processed by the Medalytix iGrading™ automated grading system. For each screening episode macular-centred and disc-centred images of both eyes were acquired and independently graded according to the English national grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and external arbitration was used to determine the final study grades. Two versions of the software were used: one that detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms. Results for each version were calculated once using both fields and once using the macula-centred field alone. Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes (7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4% to 99.1% depending on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3% to 99.3%. All the episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of configuration (192/192, 95% confidence interval 98.0% to 100%). If implemented as the first step in grading, the automated system would have reduced the manual grading effort by between 2,183 and 3,147 patient episodes (26.4% to 38.1%). Automated grading can safely reduce the workload of manual grading using two field, mydriatic photography in a routine screening service.

Automated Detection of HONcode Website Conformity Compared to Manual Detection: An Evaluation

OpenAIRE

Boyer, Célia; Dolamic, Ljiljana

2015-01-01

Background To earn HONcode certification, a website must conform to the 8 principles of the HONcode of Conduct In the current manual process of certification, a HONcode expert assesses the candidate website using precise guidelines for each principle. In the scope of the European project KHRESMOI, the Health on the Net (HON) Foundation has developed an automated system to assist in detecting a website?s HONcode conformity. Automated assistance in conducting HONcode reviews can expedite the cu...

Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.

Science.gov (United States)

Ali, Syed Farooq; Khan, Reamsha; Mahmood, Arif; Hassan, Malik Tahir; Jeon, And Moongu

2018-06-12

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall ( with 2 classes and 3 classes ) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

Effective detection method for falls according to the distance between two tri-axial accelerometers

Science.gov (United States)

Kim, Jae-Hyung; Park, Geun-Chul; Kim, Soo-Hong; Kim, Soo-Sung; Lee, Hae-Rim; Jeon, Gye-Rok

2016-04-01

Falls and fall-related injuries are a significant problem in the elderly population. A number of different approaches for detecting falls and activities of daily living (ADLs) have been conducted in recent years. However, distinguishing between real falls and certain fall-like ADL is often difficult. The aim of this study is to discriminate falls from fall-like ADLs such as jogging, jumping, and jumping down. The distance between two tri-axial accelerometers attached to the abdomen and the sternum was increased from 10 to 30 cm in 10-cm intervals. Experiments for falls and ADLs were performed to investigate the feasibility of the detection system for falls developed in this study. When the distances between the two tri-axial electrometers were 20 and 30 cm, fall-like ADLs were effectively distinguished from falls. The thresholds for three parameters — SVM, Diff Z, and Sum_diff_Z — were set; falls could be distinguished from ADL action sequences when the SVM value was larger than 4 g (TH1), the Diff_Z parameter was larger than 1.25 g (TH2), and the Sum_diff_Z parameter was larger than 15 m/s (TH3). In particular, when the SVM, Diff_Z, and Sum_diff_Z parameter were sequentially applied to thresholds (TH1, TH2, and TH3), fall-like ADL action sequences were accurately discriminated from falls.

Towards a social and context-aware multi-sensor fall detection and risk assessment platform.

Science.gov (United States)

De Backere, F; Ongenae, F; Van den Abeele, F; Nelis, J; Bonte, P; Clement, E; Philpott, M; Hoebeke, J; Verstichel, S; Ackaert, A; De Turck, F

2015-09-01

For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms. In this paper, a social- and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task. The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. The combination of several systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated vehicle for railway track fault detection

Science.gov (United States)

Bhushan, M.; Sujay, S.; Tushar, B.; Chitra, P.

2017-11-01

For the safety reasons, railroad tracks need to be inspected on a regular basis for detecting physical defects or design non compliances. Such track defects and non compliances, if not detected in a certain interval of time, may eventually lead to severe consequences such as train derailments. Inspection must happen twice weekly by a human inspector to maintain safety standards as there are hundreds and thousands of miles of railroad track. But in such type of manual inspection, there are many drawbacks that may result in the poor inspection of the track, due to which accidents may cause in future. So to avoid such errors and severe accidents, this automated system is designed.Such a concept would surely introduce automation in the field of inspection process of railway track and can help to avoid mishaps and severe accidents due to faults in the track.

Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods

Directory of Open Access Journals (Sweden)

Taehwan Kim

2017-05-01

Full Text Available By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors.

A survey on automated wheeze detection systems for asthmatic patients

Directory of Open Access Journals (Sweden)

Syamimi Mardiah Shaharum

2012-11-01

Full Text Available The purpose of this paper is to present an evidence of automated wheeze detection system by a survey that can be very beneficial for asthmatic patients. Generally, for detecting asthma in a patient, stethoscope is used for ascertaining wheezes present. This causes a major problem nowadays because a number of patients tend to delay the interpretation time, which can lead to misinterpretations and in some worst cases to death. Therefore, the development of automated system would ease the burden of medical personnel. A further discussion on automated wheezes detection system will be presented later in the paper. As for the methodology, a systematic search of articles published as early as 1985 to 2012 was conducted. Important details including the hardware used, placement of hardware, and signal processing methods have been presented clearly thus hope to help and encourage future researchers to develop commercial system that will improve the diagnosing and monitoring of asthmatic patients.

Towards an Automated Acoustic Detection System for Free Ranging Elephants.

Science.gov (United States)

Zeppelzauer, Matthias; Hensman, Sean; Stoeger, Angela S

The human-elephant conflict is one of the most serious conservation problems in Asia and Africa today. The involuntary confrontation of humans and elephants claims the lives of many animals and humans every year. A promising approach to alleviate this conflict is the development of an acoustic early warning system. Such a system requires the robust automated detection of elephant vocalizations under unconstrained field conditions. Today, no system exists that fulfills these requirements. In this paper, we present a method for the automated detection of elephant vocalizations that is robust to the diverse noise sources present in the field. We evaluate the method on a dataset recorded under natural field conditions to simulate a real-world scenario. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants. Furthermore, the method may be a useful tool for scientists in bioacoustics for the study of wildlife recordings.

Automated detection of geomagnetic storms with heightened risk of GIC

Science.gov (United States)

Bailey, Rachel L.; Leonhardt, Roman

2016-06-01

Automated detection of geomagnetic storms is of growing importance to operators of technical infrastructure (e.g., power grids, satellites), which is susceptible to damage caused by the consequences of geomagnetic storms. In this study, we compare three methods for automated geomagnetic storm detection: a method analyzing the first derivative of the geomagnetic variations, another looking at the Akaike information criterion, and a third using multi-resolution analysis of the maximal overlap discrete wavelet transform of the variations. These detection methods are used in combination with an algorithm for the detection of coronal mass ejection shock fronts in ACE solar wind data prior to the storm arrival on Earth as an additional constraint for possible storm detection. The maximal overlap discrete wavelet transform is found to be the most accurate of the detection methods. The final storm detection software, implementing analysis of both satellite solar wind and geomagnetic ground data, detects 14 of 15 more powerful geomagnetic storms over a period of 2 years.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

Science.gov (United States)

2018-01-01

ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform by Kwok F Tom Sensors and Electron...1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a

Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

Science.gov (United States)

Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G; Velaga, Swetha B; Ramachandra, Chaithanya A; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R

2018-03-01

We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894). Diabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

3D depth image analysis for indoor fall detection of elderly people

Directory of Open Access Journals (Sweden)

Lei Yang

2016-02-01

Full Text Available This paper presents a new fall detection method of elderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre-processed by a median filter both for background and target. The silhouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor plane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual. The centroids of the human body are calculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are lower than some thresholds, fall incident will be detected. Experiments with different falling direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.

FALL DETECTION AND PREVENTION FOR THE ELDERLY: A REVIEW OF TRENDS AND CHALLENGES

OpenAIRE

El-Bendary, Nashwa; Tan, Qing; C. Pivot, Frédérique; Lam, Anthony

2013-01-01

It is of little surprise that falling is often accepted as a natural part of the aging process. In fact, it is the impact rather than the occurrence of falls in the elderly, which is of most concern. Aging people are typically frailer, more unsteady, and have slower reactions, thus are more likely to fall and be injured than younger individuals. Typically, research and industry presented various practical solutions for assisting the elderly and their caregivers against falls via detecting fal...

Automated detection of a prostate Ni-Ti stent in electronic portal images.

Science.gov (United States)

Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

2006-12-01

Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study. Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7 mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67 of 71 pairs of images. The method is fast, has a high success rate, good accuracy, and has a potential for unsupervised localization of the prostate before radiotherapy, which would enable automated repositioning before treatment and allow for the use of very tight PTV margins.

Automated detection of a prostate Ni-Ti stent in electronic portal images

International Nuclear Information System (INIS)

Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

2006-01-01

Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study. Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7 mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67 of 71 pairs of images. The method is fast, has a high success rate, good accuracy, and has a potential for unsupervised localization of the prostate before radiotherapy, which would enable automated repositioning before treatment and allow for the use of very tight PTV margins

Automated DNA electrophoresis, hybridization and detection

International Nuclear Information System (INIS)

Zapolski, E.J.; Gersten, D.M.; Golab, T.J.; Ledley, R.S.

1986-01-01

A fully automated, computer controlled system for nucleic acid hybridization analysis has been devised and constructed. In practice, DNA is digested with restriction endonuclease enzyme(s) and loaded into the system by pipette; 32 P-labelled nucleic acid probe(s) is loaded into the nine hybridization chambers. Instructions for all the steps in the automated process are specified by answering questions that appear on the computer screen at the start of the experiment. Subsequent steps are performed automatically. The system performs horizontal electrophoresis in agarose gel, fixed the fragments to a solid phase matrix, denatures, neutralizes, prehybridizes, hybridizes, washes, dries and detects the radioactivity according to the specifications given by the operator. The results, printed out at the end, give the positions on the matrix to which radioactivity remains hybridized following stringent washing

Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.

Science.gov (United States)

Campillo-Gimenez, Boris; Garcelon, Nicolas; Jarno, Pascal; Chapplain, Jean Marc; Cuggia, Marc

2013-01-01

The surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI.

Automated asteroseismic peak detections

Science.gov (United States)

García Saravia Ortiz de Montellano, Andrés; Hekker, S.; Themeßl, N.

2018-05-01

Space observatories such as Kepler have provided data that can potentially revolutionize our understanding of stars. Through detailed asteroseismic analyses we are capable of determining fundamental stellar parameters and reveal the stellar internal structure with unprecedented accuracy. However, such detailed analyses, known as peak bagging, have so far been obtained for only a small percentage of the observed stars while most of the scientific potential of the available data remains unexplored. One of the major challenges in peak bagging is identifying how many solar-like oscillation modes are visible in a power density spectrum. Identification of oscillation modes is usually done by visual inspection that is time-consuming and has a degree of subjectivity. Here, we present a peak-detection algorithm especially suited for the detection of solar-like oscillations. It reliably characterizes the solar-like oscillations in a power density spectrum and estimates their parameters without human intervention. Furthermore, we provide a metric to characterize the false positive and false negative rates to provide further information about the reliability of a detected oscillation mode or the significance of a lack of detected oscillation modes. The algorithm presented here opens the possibility for detailed and automated peak bagging of the thousands of solar-like oscillators observed by Kepler.

A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration

International Nuclear Information System (INIS)

Chao, Pei-Kuang; Chan, Hsiao-Lung; Chen, Yu-Chuan; Tang, Fuk-Tan; Wong, May-Kuen

2009-01-01

Falling is an important problem in the health maintenance of people above middle age. Portable accelerometer systems have been designed to detect falls. However, false alarms induced by some dynamic motions, such as walking and jumping, are difficult to avoid. Acceleration cross-product (AC)-related methods are proposed and examined by this study to seek solutions for detecting falls with less motion-evoked false alarms. A set of tri-axial acceleration data is collected during simulated falls, posture transfers and dynamic activities by wireless sensors for making methodological comparisons. The performance of fall detection is evaluated in aspects of parameter comparison, threshold selection, sensor placement and post-fall posture (PP) recruitment. By parameter comparison, AC leads to a larger area under the receiver operating characteristic (ROC) curve than acceleration magnitude (AM). Three strategies of threshold selection, for 100% sensitivity (Sen100), for 100% specificity (Spe100) and for the best sum (BS) of sensitivity and specificity, are evaluated. Selecting a threshold based on Sen100 and BS leads to more practicable results. Simultaneous data recording from sensors in the chest and waist is performed. Fall detection based on the data from the chest shows better global accuracy. PP recruitment leads to lower false alarm ratios (FR) for both AC- and AM-based methods

A study of using smartphone to detect and identify construction workers' near-miss falls based on ANN

Science.gov (United States)

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2018-03-01

As an effective fall accident preventive method, insight into near-miss falls provides an efficient solution to find out the causes of fall accidents, classify the type of near-miss falls and control the potential hazards. In this context, the paper proposes a method to detect and identify near-miss falls that occur when a worker walks in a workplace based on artificial neural network (ANN). The energy variation generated by workers who meet with near-miss falls is measured by sensors embedded in smart phone. Two experiments were designed to train the algorithm to identify various types of near-miss falls and test the recognition accuracy, respectively. At last, a test was conducted by workers wearing smart phones as they walked around a simulated construction workplace. The motion data was collected, processed and inputted to the trained ANN to detect and identify near-miss falls. Thresholds were obtained to measure the relationship between near-miss falls and fall accidents in a quantitate way. This approach, which integrates smart phone and ANN, will help detect near-miss fall events, identify hazardous elements and vulnerable workers, providing opportunities to eliminate dangerous conditions in a construction site or to alert possible victims that need to change their behavior before the occurrence of a fall accident.

A posture recognition based fall detection system for monitoring an elderly person in a smart home environment.

Science.gov (United States)

Yu, Miao; Rhuma, Adel; Naqvi, Syed Mohsen; Wang, Liang; Chambers, Jonathon

2012-11-01

We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

[Research and Design of a System for Detecting Automated External Defbrillator Performance Parameters].

Science.gov (United States)

Wang, Kewu; Xiao, Shengxiang; Jiang, Lina; Hu, Jingkai

2017-09-30

In order to regularly detect the performance parameters of automated external defibrillator (AED), to make sure it is safe before using the instrument, research and design of a system for detecting automated external defibrillator performance parameters. According to the research of the characteristics of its performance parameters, combing the STM32's stability and high speed with PWM modulation control, the system produces a variety of ECG normal and abnormal signals through the digital sampling methods. Completed the design of the hardware and software, formed a prototype. This system can accurate detect automated external defibrillator discharge energy, synchronous defibrillation time, charging time and other key performance parameters.

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

Science.gov (United States)

Skofronick-Jackson, Gail M.; Johnson, Benjamin T.; Munchak, S. Joseph

2013-01-01

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

An automated procedure for covariation-based detection of RNA structure

International Nuclear Information System (INIS)

Winker, S.; Overbeek, R.; Woese, C.R.; Olsen, G.J.; Pfluger, N.

1989-12-01

This paper summarizes our investigations into the computational detection of secondary and tertiary structure of ribosomal RNA. We have developed a new automated procedure that not only identifies potential bondings of secondary and tertiary structure, but also provides the covariation evidence that supports the proposed bondings, and any counter-evidence that can be detected in the known sequences. A small number of previously unknown bondings have been detected in individual RNA molecules (16S rRNA and 7S RNA) through the use of our automated procedure. Currently, we are systematically studying mitochondrial rRNA. Our goal is to detect tertiary structure within 16S rRNA and quaternary structure between 16S and 23S rRNA. Our ultimate hope is that automated covariation analysis will contribute significantly to a refined picture of ribosome structure. Our colleagues in biology have begun experiments to test certain hypotheses suggested by an examination of our program's output. These experiments involve sequencing key portions of the 23S ribosomal RNA for species in which the known 16S ribosomal RNA exhibits variation (from the dominant pattern) at the site of a proposed bonding. The hope is that the 23S ribosomal RNA of these species will exhibit corresponding complementary variation or generalized covariation. 24 refs

An automated procedure for covariation-based detection of RNA structure

Energy Technology Data Exchange (ETDEWEB)

Winker, S.; Overbeek, R.; Woese, C.R.; Olsen, G.J.; Pfluger, N.

1989-12-01

This paper summarizes our investigations into the computational detection of secondary and tertiary structure of ribosomal RNA. We have developed a new automated procedure that not only identifies potential bondings of secondary and tertiary structure, but also provides the covariation evidence that supports the proposed bondings, and any counter-evidence that can be detected in the known sequences. A small number of previously unknown bondings have been detected in individual RNA molecules (16S rRNA and 7S RNA) through the use of our automated procedure. Currently, we are systematically studying mitochondrial rRNA. Our goal is to detect tertiary structure within 16S rRNA and quaternary structure between 16S and 23S rRNA. Our ultimate hope is that automated covariation analysis will contribute significantly to a refined picture of ribosome structure. Our colleagues in biology have begun experiments to test certain hypotheses suggested by an examination of our program's output. These experiments involve sequencing key portions of the 23S ribosomal RNA for species in which the known 16S ribosomal RNA exhibits variation (from the dominant pattern) at the site of a proposed bonding. The hope is that the 23S ribosomal RNA of these species will exhibit corresponding complementary variation or generalized covariation. 24 refs.

Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

Directory of Open Access Journals (Sweden)

Stefan Wiehle

2015-01-01

Full Text Available We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Due to the high number of ships traveling through the Wadden Sea to the largest ports of Germany, frequent monitoring of the bathymetry is also an important task for maritime security. For such an extended area and the required short intervals of a few months, only remote sensing methods can perform this task efficiently. Automating the waterline detection in weather-independent radar images provides a fast and reliable way to spot changes in the coastal topography. The presented algorithm first performs smoothing, brightness thresholding, and edge detection. In the second step, edge drawing and flood filling are iteratively performed to determine optimal thresholds for the edge drawing. In the last step, small misdetections are removed.

Automated asteroseismic peak detections

DEFF Research Database (Denmark)

de Montellano, Andres Garcia Saravia Ortiz; Hekker, S.; Themessl, N.

2018-01-01

Space observatories such as Kepler have provided data that can potentially revolutionize our understanding of stars. Through detailed asteroseismic analyses we are capable of determining fundamental stellar parameters and reveal the stellar internal structure with unprecedented accuracy. However......, such detailed analyses, known as peak bagging, have so far been obtained for only a small percentage of the observed stars while most of the scientific potential of the available data remains unexplored. One of the major challenges in peak bagging is identifying how many solar-like oscillation modes are visible...... of detected oscillation modes. The algorithm presented here opens the possibility for detailed and automated peak bagging of the thousands of solar-like oscillators observed by Kepler....

Real-time monitoring system for elderly people in detecting falling movement using accelerometer and gyroscope

Science.gov (United States)

Siregar, B.; Andayani, U.; Bahri, R. P.; Seniman; Fahmi, F.

2018-03-01

Most of the elderly people is experiencing a decrease in physical quality, especially the weakness in the legs. This will cause elderly easy to fall and can have a serious impact on their health if not getting help very quickly. It is, therefore, necessary to take immediate action against the falling cases experienced by the elderly. One such action is by developing supervision and detecting of falling movements in real-time, which is then the connection to a member of the family. In this research, we used Arduino Uno as a microcontroller, sensor accelerometer, and gyroscope that serves to measure falling movement of the elderly person and supported by GPS technology Ublox Neo 6M to provide information about coordinates. The result was the high accuracy of delivering notification data to server and accuracy of data delivery to family notification equal to 93,75%. The system successfully detects the direction of falling: forward, backward, left or right and able to distinguish between unintentional falling and conscious falling like a bow or prostrate position.

Enhanced detection levels in a semi-automated sandwich ...

African Journals Online (AJOL)

A peptide nucleic acid (PNA) signal probe was tested as a replacement for a typical DNA oligonucleotidebased signal probe in a semi-automated sandwich hybridisation assay designed to detect the harmful phytoplankton species Alexandrium tamarense. The PNA probe yielded consistently higher fluorescent signal ...

Embedded DSP-based telehealth radar system for remote in-door fall detection.

Science.gov (United States)

Garripoli, Carmine; Mercuri, Marco; Karsmakers, Peter; Jack Soh, Ping; Crupi, Giovanni; Vandenbosch, Guy A E; Pace, Calogero; Leroux, Paul; Schreurs, Dominique

2015-01-01

Telehealth systems and applications are extensively investigated nowadays to enhance the quality-of-care and, in particular, to detect emergency situations and to monitor the well-being of elderly people, allowing them to stay at home independently as long as possible. In this paper, an embedded telehealth system for continuous, automatic, and remote monitoring of real-time fall emergencies is presented and discussed. The system, consisting of a radar sensor and base station, represents a cost-effective and efficient healthcare solution. The implementation of the fall detection data processing technique, based on the least-square support vector machines, through a digital signal processor and the management of the communication between radar sensor and base station are detailed. Experimental tests, for a total of 65 mimicked fall incidents, recorded with 16 human subjects (14 men and two women) that have been monitored for 320 min, have been used to validate the proposed system under real circumstances. The subjects' weight is between 55 and 90 kg with heights between 1.65 and 1.82 m, while their age is between 25 and 39 years. The experimental results have shown a sensitivity to detect the fall events in real time of 100% without reporting false positives. The tests have been performed in an area where the radar's operation was not limited by practical situations, namely, signal power, coverage of the antennas, and presence of obstacles between the subject and the antennas.

Automated real-time detection of tonic-clonic seizures using a wearable EMG device

DEFF Research Database (Denmark)

Beniczky, Sándor; Conradsen, Isa; Henning, Oliver

2018-01-01

OBJECTIVE: To determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device. METHODS: We prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device....... The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings...

Vision-Based Fall Detection with Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Adrián Núñez-Marcos

2017-01-01

Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups.

Science.gov (United States)

Saleh, George Michael; Wawrzynski, James; Caputo, Silvestro; Peto, Tunde; Al Turk, Lutfiah Ismail; Wang, Su; Hu, Yin; Da Cruz, Lyndon; Smith, Phil; Tang, Hongying Lilian

2016-01-01

Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs) is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system's performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races.

Optimal fall indicators for slip induced falls on a cross-slope.

Science.gov (United States)

Domone, Sarah; Lawrence, Daniel; Heller, Ben; Hendra, Tim; Mawson, Sue; Wheat, Jonathan

2016-08-01

Slip-induced falls are among the most common cause of major occupational injuries in the UK as well as being a major public health concern in the elderly population. This study aimed to determine the optimal fall indicators for fall detection models which could be used to reduce the detrimental consequences of falls. A total of 264 kinematic variables covering three-dimensional full body model translation and rotational measures were analysed during normal walking, successful recovery from slips and falls on a cross-slope. Large effect sizes were found for three kinematic variables which were able to distinguish falls from normal walking and successful recovery. Further work should consider other types of daily living activities as results show that the optimal kinematic fall indicators can vary considerably between movement types. Practitioner Summary: Fall detection models are used to minimise the adverse consequences of slip-induced falls, a major public health concern. Optimal fall indicators were derived from a comprehensive set of kinematic variables for slips on a cross-slope. Results suggest robust detection of falls is possible on a cross-slope but may be more difficult than level walking.

Involvement of older people in the development of fall detection systems: a scoping review.

Science.gov (United States)

Thilo, Friederike J S; Hürlimann, Barbara; Hahn, Sabine; Bilger, Selina; Schols, Jos M G A; Halfens, Ruud J G

2016-02-11

The involvement of users is recommended in the development of health related technologies, in order to address their needs and preferences and to improve the daily usage of these technologies. The objective of this literature review was to identify the nature and extent of research involving older people in the development of fall detection systems. A scoping review according to the framework of Arksey and O'Malley was carried out. A key term search was employed in eight relevant databases. Included articles were summarized using a predetermined charting form and subsequently thematically analysed. A total of 53 articles was included. In 49 of the 53 articles, older people were involved in the design and/or testing stages, and in 4 of 53 articles, they were involved in the conceptual or market deployment stages. In 38 of the 53 articles, the main focus of the involvement of older people was technical aspects. In 15 of the 53 articles, the perspectives of the elderly related to the fall detection system under development were determined using focus groups, single interviews or questionnaires. Until presently, involvement of older people in the development of fall detection systems has focused mainly on technical aspects. Little attention has been given to the specific needs and views of older people in the context of fall detection system development and usage.

Proof of Concept of Automated Collision Detection Technology in Rugby Sevens.

Science.gov (United States)

Clarke, Anthea C; Anson, Judith M; Pyne, David B

2017-04-01

Clarke, AC, Anson, JM, and Pyne, DB. Proof of concept of automated collision detection technology in rugby sevens. J Strength Cond Res 31(4): 1116-1120, 2017-Developments in microsensor technology allow for automated detection of collisions in various codes of football, removing the need for time-consuming postprocessing of video footage. However, little research is available on the ability of microsensor technology to be used across various sports or genders. Game video footage was matched with microsensor-detected collisions (GPSports) in one men's (n = 12 players) and one women's (n = 12) rugby sevens match. True-positive, false-positive, and false-negative events between video and microsensor-detected collisions were used to calculate recall (ability to detect a collision) and precision (accurately identify a collision). The precision was similar between the men's and women's rugby sevens game (∼0.72; scale 0.00-1.00); however, the recall in the women's game (0.45) was less than that for the men's game (0.69). This resulted in 45% of collisions for men and 62% of collisions for women being incorrectly labeled. Currently, the automated collision detection system in GPSports microtechnology units has only modest utility in rugby sevens, and it seems that a rugby sevens-specific algorithm is needed. Differences in measures between the men's and women's game may be a result of physical size, and strength, and physicality, as well as technical and tactical factors.

Operations management system advanced automation: Fault detection isolation and recovery prototyping

Science.gov (United States)

Hanson, Matt

1990-01-01

The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

Fall detection in the elderly by head-tracking

OpenAIRE

Yu, Miao; Naqvi, Syed Mohsen; Chambers, Jonathan

2009-01-01

In the paper, we propose a fall detection method based on head tracking within a smart home environment equipped with video cameras. A motion history image and code-book background subtraction are combined to determine whether large movement occurs within the scene. Based on the magnitude of the movement information, particle filters with different state models are used to track the head. The head tracking procedure is performed in two video streams taken bytwoseparatecamerasandthree-dimension...

An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups

Directory of Open Access Journals (Sweden)

George Michael Saleh

2016-01-01

Full Text Available Patients without diabetic retinopathy (DR represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system’s performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races.

Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems

Directory of Open Access Journals (Sweden)

Yuwono Mitchell

2012-02-01

Full Text Available Abstract Background Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. Method We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF neural networks. Results Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems.

Comparison of Machine Learning Methods for the Purpose Of Human Fall Detection

Directory of Open Access Journals (Sweden)

Strémy Maximilián

2014-12-01

Full Text Available According to several studies, the European population is rapidly aging far over last years. It is therefore important to ensure that aging population is able to live independently without the support of working-age population. In accordance with the studies, fall is the most dangerous and frequent accident in the everyday life of aging population. In our paper, we present a system to track the human fall by a visual detection, i.e. using no wearable equipment. For this purpose, we used a Kinect sensor, which provides the human body position in the Cartesian coordinates. It is possible to directly capture a human body because the Kinect sensor has a depth and also an infrared camera. The first step in our research was to detect postures and classify the fall accident. We experimented and compared the selected machine learning methods including Naive Bayes, decision trees and SVM method to compare the performance in recognizing the human postures (standing, sitting and lying. The highest classification accuracy of over 93.3% was achieved by the decision tree method.

Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly.

Science.gov (United States)

Hwang, J Y; Kang, J M; Jang, Y W; Kim, H

2004-01-01

Novel algorithm and real-time ambulatory monitoring system for fall detection in elderly people is described. Our system is comprised of accelerometer, tilt sensor and gyroscope. For real-time monitoring, we used Bluetooth. Accelerometer measures kinetic force, tilt sensor and gyroscope estimates body posture. Also, we suggested algorithm using signals which obtained from the system attached to the chest for fall detection. To evaluate our system and algorithm, we experimented on three people aged over 26 years. The experiment of four cases such as forward fall, backward fall, side fall and sit-stand was repeated ten times and the experiment in daily life activity was performed one time to each subject. These experiments showed that our system and algorithm could distinguish between falling and daily life activity. Moreover, the accuracy of fall detection is 96.7%. Our system is especially adapted for long-time and real-time ambulatory monitoring of elderly people in emergency situation.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

Science.gov (United States)

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.

Science.gov (United States)

Shawen, Nicholas; Lonini, Luca; Mummidisetty, Chaithanya Krishna; Shparii, Ilona; Albert, Mark V; Kording, Konrad; Jayaraman, Arun

2017-10-11

Automatically detecting falls with mobile phones provides an opportunity for rapid response to injuries and better knowledge of what precipitated the fall and its consequences. This is beneficial for populations that are prone to falling, such as people with lower limb amputations. Prior studies have focused on fall detection in able-bodied individuals using data from a laboratory setting. Such approaches may provide a limited ability to detect falls in amputees and in real-world scenarios. The aim was to develop a classifier that uses data from able-bodied individuals to detect falls in individuals with a lower limb amputation, while they freely carry the mobile phone in different locations and during free-living. We obtained 861 simulated indoor and outdoor falls from 10 young control (non-amputee) individuals and 6 individuals with a lower limb amputation. In addition, we recorded a broad database of activities of daily living, including data from three participants' free-living routines. Sensor readings (accelerometer and gyroscope) from a mobile phone were recorded as participants freely carried it in three common locations-on the waist, in a pocket, and in the hand. A set of 40 features were computed from the sensors data and four classifiers were trained and combined through stacking to detect falls. We compared the performance of two population-specific models, trained and tested on either able-bodied or amputee participants, with that of a model trained on able-bodied participants and tested on amputees. A simple threshold-based classifier was used to benchmark our machine-learning classifier. The accuracy of fall detection in amputees for a model trained on control individuals (sensitivity: mean 0.989, 1.96*standard error of the mean [SEM] 0.017; specificity: mean 0.968, SEM 0.025) was not statistically different (P=.69) from that of a model trained on the amputee population (sensitivity: mean 0.984, SEM 0.016; specificity: mean 0.965, SEM 0

Automated visual fruit detection for harvest estimation and robotic harvesting

OpenAIRE

Puttemans, Steven; Vanbrabant, Yasmin; Tits, Laurent; Goedemé, Toon

2016-01-01

Fully automated detection and localisation of fruit in orchards is a key component in creating automated robotic harvesting systems, a dream of many farmers around the world to cope with large production and personnel costs. In recent years a lot of research on this topic has been performed, using basic computer vision techniques, like colour based segmentation, as a suggested solution. When not using standard RGB cameras, research tends to resort to other sensors, like hyper spectral or 3D. ...

Fully Automated Lipid Pool Detection Using Near Infrared Spectroscopy

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Elżbieta Pociask

2016-01-01

Full Text Available Background. Detecting and identifying vulnerable plaque, which is prone to rupture, is still a challenge for cardiologist. Such lipid core-containing plaque is still not identifiable by everyday angiography, thus triggering the need to develop a new tool where NIRS-IVUS can visualize plaque characterization in terms of its chemical and morphologic characteristic. The new tool can lead to the development of new methods of interpreting the newly obtained data. In this study, the algorithm to fully automated lipid pool detection on NIRS images is proposed. Method. Designed algorithm is divided into four stages: preprocessing (image enhancement, segmentation of artifacts, detection of lipid areas, and calculation of Lipid Core Burden Index. Results. A total of 31 NIRS chemograms were analyzed by two methods. The metrics, total LCBI, maximal LCBI in 4 mm blocks, and maximal LCBI in 2 mm blocks, were calculated to compare presented algorithm with commercial available system. Both intraclass correlation (ICC and Bland-Altman plots showed good agreement and correlation between used methods. Conclusions. Proposed algorithm is fully automated lipid pool detection on near infrared spectroscopy images. It is a tool developed for offline data analysis, which could be easily augmented for newer functions and projects.

Automated oil spill detection with multispectral imagery

Science.gov (United States)

Bradford, Brian N.; Sanchez-Reyes, Pedro J.

2011-06-01

In this publication we present an automated detection method for ocean surface oil, like that which existed in the Gulf of Mexico as a result of the April 20, 2010 Deepwater Horizon drilling rig explosion. Regions of surface oil in airborne imagery are isolated using red, green, and blue bands from multispectral data sets. The oil shape isolation procedure involves a series of image processing functions to draw out the visual phenomenological features of the surface oil. These functions include selective color band combinations, contrast enhancement and histogram warping. An image segmentation process then separates out contiguous regions of oil to provide a raster mask to an analyst. We automate the detection algorithm to allow large volumes of data to be processed in a short time period, which can provide timely oil coverage statistics to response crews. Geo-referenced and mosaicked data sets enable the largest identified oil regions to be mapped to exact geographic coordinates. In our simulation, multispectral imagery came from multiple sources including first-hand data collected from the Gulf. Results of the simulation show the oil spill coverage area as a raster mask, along with histogram statistics of the oil pixels. A rough square footage estimate of the coverage is reported if the image ground sample distance is available.

Automation of diagnostic genetic testing: mutation detection by cyclic minisequencing.

Science.gov (United States)

Alagrund, Katariina; Orpana, Arto K

2014-01-01

The rising role of nucleic acid testing in clinical decision making is creating a need for efficient and automated diagnostic nucleic acid test platforms. Clinical use of nucleic acid testing sets demands for shorter turnaround times (TATs), lower production costs and robust, reliable methods that can easily adopt new test panels and is able to run rare tests in random access principle. Here we present a novel home-brew laboratory automation platform for diagnostic mutation testing. This platform is based on the cyclic minisequecing (cMS) and two color near-infrared (NIR) detection. Pipetting is automated using Tecan Freedom EVO pipetting robots and all assays are performed in 384-well micro plate format. The automation platform includes a data processing system, controlling all procedures, and automated patient result reporting to the hospital information system. We have found automated cMS a reliable, inexpensive and robust method for nucleic acid testing for a wide variety of diagnostic tests. The platform is currently in clinical use for over 80 mutations or polymorphisms. Additionally to tests performed from blood samples, the system performs also epigenetic test for the methylation of the MGMT gene promoter, and companion diagnostic tests for analysis of KRAS and BRAF gene mutations from formalin fixed and paraffin embedded tumor samples. Automation of genetic test reporting is found reliable and efficient decreasing the work load of academic personnel.

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

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Mohendra Roy

2016-05-01

Full Text Available Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al., we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings.

Fall prevention walker during rehabilitation

Science.gov (United States)

Tee, Kian Sek; E, Chun Zhi; Saim, Hashim; Zakaria, Wan Nurshazwani Wan; Khialdin, Safinaz Binti Mohd; Isa, Hazlita; Awad, M. I.; Soon, Chin Fhong

2017-09-01

This paper proposes on the design of a walker for the prevention of falling among elderlies or patients during rehabilitation whenever they use a walker to assist them. Fall happens due to impaired balance or gait problem. The assistive device is designed by applying stability concept and an accelerometric fall detection system is included. The accelerometric fall detection system acts as an alerting device that acquires body accelerometric data and detect fall. Recorded accelerometric data could be useful for further assessment. Structural strength of the walker was verified via iterations of simulation using finite element analysis, before being fabricated. Experiments were conducted to identify the fall patterns using accelerometric data. The design process and detection of fall pattern demonstrates the design of a walker that could support the user without fail and alerts the helper, thus salvaging the users from injuries due to fall and unattended situation.

Automated gravity gradient tensor inversion for underwater object detection

International Nuclear Information System (INIS)

Wu, Lin; Tian, Jinwen

2010-01-01

Underwater abnormal object detection is a current need for the navigation security of autonomous underwater vehicles (AUVs). In this paper, an automated gravity gradient tensor inversion algorithm is proposed for the purpose of passive underwater object detection. Full-tensor gravity gradient anomalies induced by an object in the partial area can be measured with the technique of gravity gradiometry on an AUV. Then the automated algorithm utilizes the anomalies, using the inverse method to estimate the mass and barycentre location of the arbitrary-shaped object. A few tests on simple synthetic models will be illustrated, in order to evaluate the feasibility and accuracy of the new algorithm. Moreover, the method is applied to a complicated model of an abnormal object with gradiometer and AUV noise, and interference from a neighbouring illusive smaller object. In all cases tested, the estimated mass and barycentre location parameters are found to be in good agreement with the actual values

Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer.

Science.gov (United States)

Lu, Hao; Papathomas, Thomas G; van Zessen, David; Palli, Ivo; de Krijger, Ronald R; van der Spek, Peter J; Dinjens, Winand N M; Stubbs, Andrew P

2014-11-25

In prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate (hotspots) within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importance. In addition to intratumoral heterogeneity in proliferative rate i.e. levels of Ki67 expression within a given ACC, lack of uniformity and reproducibility in the method of quantification of Ki67 LI may confound an accurate assessment of Ki67 LI. We have implemented an open source toolset, Automated Selection of Hotspots (ASH), for automated hotspot detection and quantification of Ki67 LI. ASH utilizes NanoZoomer Digital Pathology Image (NDPI) splitter to convert the specific NDPI format digital slide scanned from the Hamamatsu instrument into a conventional tiff or jpeg format image for automated segmentation and adaptive step finding hotspots detection algorithm. Quantitative hotspot ranking is provided by the functionality from the open source application ImmunoRatio as part of the ASH protocol. The output is a ranked set of hotspots with concomitant quantitative values based on whole slide ranking. We have implemented an open source automated detection quantitative ranking of hotspots to support histopathologists in selecting the 'hottest' hotspot areas in adrenocortical carcinoma. To provide wider community easy access to ASH we implemented a Galaxy virtual machine (VM) of ASH which is available from http://bioinformatics.erasmusmc.nl/wiki/Automated_Selection_of_Hotspots . The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_216.

Automated Fault Detection for DIII-D Tokamak Experiments

International Nuclear Information System (INIS)

Walker, M.L.; Scoville, J.T.; Johnson, R.D.; Hyatt, A.W.; Lee, J.

1999-01-01

An automated fault detection software system has been developed and was used during 1999 DIII-D plasma operations. The Fault Identification and Communication System (FICS) executes automatically after every plasma discharge to check dozens of subsystems for proper operation and communicates the test results to the tokamak operator. This system is now used routinely during DIII-D operations and has led to an increase in tokamak productivity

Automated detection and categorization of genital injuries using digital colposcopy

DEFF Research Database (Denmark)

Fernandes, Kelwin; Cardoso, Jaime S.; Astrup, Birgitte Schmidt

2017-01-01

handcrafted features and deep learning techniques in the automated processing of colposcopic images for genital injury detection. Positive results where achieved by both paradigms in segmentation and classification subtasks, being traditional and deep models the best strategy for each subtask type...

Automated detection of microcalcification clusters in mammograms

Science.gov (United States)

Karale, Vikrant A.; Mukhopadhyay, Sudipta; Singh, Tulika; Khandelwal, Niranjan; Sadhu, Anup

2017-03-01

Mammography is the most efficient modality for detection of breast cancer at early stage. Microcalcifications are tiny bright spots in mammograms and can often get missed by the radiologist during diagnosis. The presence of microcalcification clusters in mammograms can act as an early sign of breast cancer. This paper presents a completely automated computer-aided detection (CAD) system for detection of microcalcification clusters in mammograms. Unsharp masking is used as a preprocessing step which enhances the contrast between microcalcifications and the background. The preprocessed image is thresholded and various shape and intensity based features are extracted. Support vector machine (SVM) classifier is used to reduce the false positives while preserving the true microcalcification clusters. The proposed technique is applied on two different databases i.e DDSM and private database. The proposed technique shows good sensitivity with moderate false positives (FPs) per image on both databases.

Automated detection of analyzable metaphase chromosome cells depicted on scanned digital microscopic images

Science.gov (United States)

Qiu, Yuchen; Wang, Xingwei; Chen, Xiaodong; Li, Yuhua; Liu, Hong; Li, Shibo; Zheng, Bin

2010-02-01

Visually searching for analyzable metaphase chromosome cells under microscopes is quite time-consuming and difficult. To improve detection efficiency, consistency, and diagnostic accuracy, an automated microscopic image scanning system was developed and tested to directly acquire digital images with sufficient spatial resolution for clinical diagnosis. A computer-aided detection (CAD) scheme was also developed and integrated into the image scanning system to search for and detect the regions of interest (ROI) that contain analyzable metaphase chromosome cells in the large volume of scanned images acquired from one specimen. Thus, the cytogeneticists only need to observe and interpret the limited number of ROIs. In this study, the high-resolution microscopic image scanning and CAD performance was investigated and evaluated using nine sets of images scanned from either bone marrow (three) or blood (six) specimens for diagnosis of leukemia. The automated CAD-selection results were compared with the visual selection. In the experiment, the cytogeneticists first visually searched for the analyzable metaphase chromosome cells from specimens under microscopes. The specimens were also automated scanned and followed by applying the CAD scheme to detect and save ROIs containing analyzable cells while deleting the others. The automated selected ROIs were then examined by a panel of three cytogeneticists. From the scanned images, CAD selected more analyzable cells than initially visual examinations of the cytogeneticists in both blood and bone marrow specimens. In general, CAD had higher performance in analyzing blood specimens. Even in three bone marrow specimens, CAD selected 50, 22, 9 ROIs, respectively. Except matching with the initially visual selection of 9, 7, and 5 analyzable cells in these three specimens, the cytogeneticists also selected 41, 15 and 4 new analyzable cells, which were missed in initially visual searching. This experiment showed the feasibility of

Automated microaneurysm detection in diabetic retinopathy using curvelet transform

Science.gov (United States)

Ali Shah, Syed Ayaz; Laude, Augustinus; Faye, Ibrahima; Tang, Tong Boon

2016-10-01

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Older adults' perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review.

Science.gov (United States)

Hawley-Hague, Helen; Boulton, Elisabeth; Hall, Alex; Pfeiffer, Klaus; Todd, Chris

2014-06-01

Over recent years a number of Information and Communication Technologies (ICTs) have emerged aiming at falls prevention, falls detection and alarms for use in case of fall. There are also a range of ICT interventions, which have been created or adapted to be pro-active in preventing falls, such as those which provide strength and balance training to older adults in the prevention of falls. However, there are issues related to the adoption and continued use of these technologies by older adults. This review provides an overview of older adults' perceptions of falls technologies. We undertook systematic searches of MEDLINE, EMBASE, CINAHL and PsychINFO, COMPENDEX and the Cochrane database. Key search terms included 'older adults', 'seniors', 'preference', 'attitudes' and a wide range of technologies, they also included the key word 'fall*'. We considered all studies that included older adults aged 50 and above. Studies had to include technologies related specifically to falls prevention, detection or monitoring. The Joanna Briggs Institute (JBI) tool and the Quality Assessment Tool for Quantitative Studies by the Effective Public Health Practice Project (EPHPP) were used. We identified 76 potentially relevant papers. Some 21 studies were considered for quality review. Twelve qualitative studies, three quantitative studies and 6 mixed methods studies were included. The literature related to technologies aimed at predicting, monitoring and preventing falls suggest that intrinsic factors related to older adults' attitudes around control, independence and perceived need/requirements for safety are important for their motivation to use and continue using technologies. Extrinsic factors such as usability, feedback gained and costs are important elements which support these attitudes and perceptions. Positive messages about the benefits of falls technologies for promoting healthy active ageing and independence are critical, as is ensuring that the technologies are simple

Automating Vendor Fraud Detection in Enterprise Systems

Directory of Open Access Journals (Sweden)

Kishore Singh

2013-06-01

Full Text Available Fraud is a multi-billion dollar industry that continues to grow annually. Many organisations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper we adopt a Design-Science methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated be developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: i automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud, and ii visualisations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: i a model for proactive fraud detection, ii methods for visualising user activities in transaction data, iii a stand-alone MCL-based prototype.

Automated crack detection in conductive smart-concrete structures using a resistor mesh model

Science.gov (United States)

Downey, Austin; D'Alessandro, Antonella; Ubertini, Filippo; Laflamme, Simon

2018-03-01

Various nondestructive evaluation techniques are currently used to automatically detect and monitor cracks in concrete infrastructure. However, these methods often lack the scalability and cost-effectiveness over large geometries. A solution is the use of self-sensing carbon-doped cementitious materials. These self-sensing materials are capable of providing a measurable change in electrical output that can be related to their damage state. Previous work by the authors showed that a resistor mesh model could be used to track damage in structural components fabricated from electrically conductive concrete, where damage was located through the identification of high resistance value resistors in a resistor mesh model. In this work, an automated damage detection strategy that works through placing high value resistors into the previously developed resistor mesh model using a sequential Monte Carlo method is introduced. Here, high value resistors are used to mimic the internal condition of damaged cementitious specimens. The proposed automated damage detection method is experimentally validated using a 500 × 500 × 50 mm3 reinforced cement paste plate doped with multi-walled carbon nanotubes exposed to 100 identical impact tests. Results demonstrate that the proposed Monte Carlo method is capable of detecting and localizing the most prominent damage in a structure, demonstrating that automated damage detection in smart-concrete structures is a promising strategy for real-time structural health monitoring of civil infrastructure.

Automated acoustic analysis in detection of spontaneous swallows in Parkinson's disease.

Science.gov (United States)

Golabbakhsh, Marzieh; Rajaei, Ali; Derakhshan, Mahmoud; Sadri, Saeed; Taheri, Masoud; Adibi, Peyman

2014-10-01

Acoustic monitoring of swallow frequency has become important as the frequency of spontaneous swallowing can be an index for dysphagia and related complications. In addition, it can be employed as an objective quantification of ingestive behavior. Commonly, swallowing complications are manually detected using videofluoroscopy recordings, which require expensive equipment and exposure to radiation. In this study, a noninvasive automated technique is proposed that uses breath and swallowing recordings obtained via a microphone located over the laryngopharynx. Nonlinear diffusion filters were used in which a scale-space decomposition of recorded sound at different levels extract swallows from breath sounds and artifacts. This technique was compared to manual detection of swallows using acoustic signals on a sample of 34 subjects with Parkinson's disease. A speech language pathologist identified five subjects who showed aspiration during the videofluoroscopic swallowing study. The proposed automated method identified swallows with a sensitivity of 86.67 %, a specificity of 77.50 %, and an accuracy of 82.35 %. These results indicate the validity of automated acoustic recognition of swallowing as a fast and efficient approach to objectively estimate spontaneous swallow frequency.

Image-based fall detection and classification of a user with a walking support system

Science.gov (United States)

Taghvaei, Sajjad; Kosuge, Kazuhiro

2017-10-01

The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems.We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user's depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called "RT Walker") by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user's motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.

Systems and Methods for Automated Water Detection Using Visible Sensors

Science.gov (United States)

Rankin, Arturo L. (Inventor); Matthies, Larry H. (Inventor); Bellutta, Paolo (Inventor)

2016-01-01

Systems and methods are disclosed that include automated machine vision that can utilize images of scenes captured by a 3D imaging system configured to image light within the visible light spectrum to detect water. One embodiment includes autonomously detecting water bodies within a scene including capturing at least one 3D image of a scene using a sensor system configured to detect visible light and to measure distance from points within the scene to the sensor system, and detecting water within the scene using a processor configured to detect regions within each of the at least one 3D images that possess at least one characteristic indicative of the presence of water.

Data for automated, high-throughput microscopy analysis of intracellular bacterial colonies using spot detection.

Science.gov (United States)

Ernstsen, Christina L; Login, Frédéric H; Jensen, Helene H; Nørregaard, Rikke; Møller-Jensen, Jakob; Nejsum, Lene N

2017-10-01

Quantification of intracellular bacterial colonies is useful in strategies directed against bacterial attachment, subsequent cellular invasion and intracellular proliferation. An automated, high-throughput microscopy-method was established to quantify the number and size of intracellular bacterial colonies in infected host cells (Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy, Ernstsen et al., 2017 [1]). The infected cells were imaged with a 10× objective and number of intracellular bacterial colonies, their size distribution and the number of cell nuclei were automatically quantified using a spot detection-tool. The spot detection-output was exported to Excel, where data analysis was performed. In this article, micrographs and spot detection data are made available to facilitate implementation of the method.

iFall: an Android application for fall monitoring and response.

Science.gov (United States)

Sposaro, Frank; Tyson, Gary

2009-01-01

Injuries due to falls are among the leading causes of hospitalization in elderly persons, often resulting in a rapid decline in quality of life or death. Rapid response can improve the patients outcome, but this is often lacking when the injured person lives alone and the nature of the injury complicates calling for help. This paper presents an alert system for fall detection using common commercially available electronic devices to both detect the fall and alert authorities. We use an Android-based smart phone with an integrated tri-axial accelerometer. Data from the accelerometer is evaluated with several threshold based algorithms and position data to determine a fall. The threshold is adaptive based on user provided parameters such as: height, weight, and level of activity. The algorithm adapts to unique movements that a phone experiences as opposed to similar systems which require users to mount accelerometers to their chest or trunk. If a fall is suspected a notification is raised requiring the user's response. If the user does not respond, the system alerts pre-specified social contacts with an informational message via SMS. If a contact responds the system commits an audible notification, automatically connects, and enables the speakerphone. If a social contact confirms a fall, an appropriate emergency service is alerted. Our system provides a realizable, cost effective solution to fall detection using a simple graphical interface while not overwhelming the user with uncomfortable sensors.

A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

Science.gov (United States)

Chambert, Thierry A.; Waddle, J. Hardin; Miller, David A.W.; Walls, Susan; Nichols, James D.

2018-01-01

The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

Automated detection of actinic keratoses in clinical photographs.

Science.gov (United States)

Hames, Samuel C; Sinnya, Sudipta; Tan, Jean-Marie; Morze, Conrad; Sahebian, Azadeh; Soyer, H Peter; Prow, Tarl W

2015-01-01

Clinical diagnosis of actinic keratosis is known to have intra- and inter-observer variability, and there is currently no non-invasive and objective measure to diagnose these lesions. The aim of this pilot study was to determine if automatically detecting and circumscribing actinic keratoses in clinical photographs is feasible. Photographs of the face and dorsal forearms were acquired in 20 volunteers from two groups: the first with at least on actinic keratosis present on the face and each arm, the second with no actinic keratoses. The photographs were automatically analysed using colour space transforms and morphological features to detect erythema. The automated output was compared with a senior consultant dermatologist's assessment of the photographs, including the intra-observer variability. Performance was assessed by the correlation between total lesions detected by automated method and dermatologist, and whether the individual lesions detected were in the same location as the dermatologist identified lesions. Additionally, the ability to limit false positives was assessed by automatic assessment of the photographs from the no actinic keratosis group in comparison to the high actinic keratosis group. The correlation between the automatic and dermatologist counts was 0.62 on the face and 0.51 on the arms, compared to the dermatologist's intra-observer variation of 0.83 and 0.93 for the same. Sensitivity of automatic detection was 39.5% on the face, 53.1% on the arms. Positive predictive values were 13.9% on the face and 39.8% on the arms. Significantly more lesions (p<0.0001) were detected in the high actinic keratosis group compared to the no actinic keratosis group. The proposed method was inferior to assessment by the dermatologist in terms of sensitivity and positive predictive value. However, this pilot study used only a single simple feature and was still able to achieve sensitivity of detection of 53.1% on the arms.This suggests that image analysis is

From drafting guideline to error detection: Automating style checking for legislative texts

OpenAIRE

Höfler Stefan; Sugisaki Kyoko

2012-01-01

This paper reports on the development of methods for the automated detection of violations of style guidelines for legislative texts, and their implementation in a prototypical tool. To this aim, the approach of error modelling employed in automated style checkers for technical writing is enhanced to meet the requirements of legislative editing. The paper identifies and discusses the two main sets of challenges that have to be tackled in this process: (i) the provision of domain-specific NLP ...

Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.

Science.gov (United States)

Rajalakshmi, Ramachandran; Subashini, Radhakrishnan; Anjana, Ranjit Mohan; Mohan, Viswanathan

2018-06-01

To assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologist's grading. Three hundred and one patients with type 2 diabetes underwent retinal photography with Remidio 'Fundus on phone' (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArt TM ) designed to identify DR, referable DR (moderate non-proliferative DR or worse and/or DME) or STDR. The sensitivity and specificity of automated grading were assessed and validated against the ophthalmologists' grading. Retinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.9-98.7) sensitivity and 80.2% (95% CI 72.6-87.8) specificity for detecting any DR and 99.1% (95% CI 95.1-99.9) sensitivity and 80.4% (95% CI 73.9-85.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively. Automated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes.

Automation of Classical QEEG Trending Methods for Early Detection of Delayed Cerebral Ischemia: More Work to Do.

Science.gov (United States)

Wickering, Ellis; Gaspard, Nicolas; Zafar, Sahar; Moura, Valdery J; Biswal, Siddharth; Bechek, Sophia; OʼConnor, Kathryn; Rosenthal, Eric S; Westover, M Brandon

2016-06-01

The purpose of this study is to evaluate automated implementations of continuous EEG monitoring-based detection of delayed cerebral ischemia based on methods used in classical retrospective studies. We studied 95 patients with either Fisher 3 or Hunt Hess 4 to 5 aneurysmal subarachnoid hemorrhage who were admitted to the Neurosciences ICU and underwent continuous EEG monitoring. We implemented several variations of two classical algorithms for automated detection of delayed cerebral ischemia based on decreases in alpha-delta ratio and relative alpha variability. Of 95 patients, 43 (45%) developed delayed cerebral ischemia. Our automated implementation of the classical alpha-delta ratio-based trending method resulted in a sensitivity and specificity (Se,Sp) of (80,27)%, compared with the values of (100,76)% reported in the classic study using similar methods in a nonautomated fashion. Our automated implementation of the classical relative alpha variability-based trending method yielded (Se,Sp) values of (65,43)%, compared with (100,46)% reported in the classic study using nonautomated analysis. Our findings suggest that improved methods to detect decreases in alpha-delta ratio and relative alpha variability are needed before an automated EEG-based early delayed cerebral ischemia detection system is ready for clinical use.

Automated Detection of Client-State Manipulation Vulnerabilities

DEFF Research Database (Denmark)

Møller, Anders; Schwarz, Mathias

2012-01-01

automated tools that can assist the programmers in the application development process by detecting weaknesses. Many vulnerabilities are related to web application code that stores references to application state in the generated HTML documents to work around the statelessness of the HTTP protocol....... In this paper, we show that such client-state manipulation vulnerabilities are amenable to tool supported detection. We present a static analysis for the widely used frameworks Java Servlets, JSP, and Struts. Given a web application archive as input, the analysis identifies occurrences of client state...... and infers the information flow between the client state and the shared application state on the server. This makes it possible to check how client-state manipulation performed by malicious users may affect the shared application state and cause leakage or modifications of sensitive information. The warnings...

Automated detection of cavities present in the high explosive filler of artillery shells

International Nuclear Information System (INIS)

Kruger, R.P.; Janney, D.H.; Breedlove, J.R. Jr.

1976-01-01

Initial research has been conducted into the use of digital image analysis techniques for automated detection and characterization of piping cavities present in the high explosive (HE) filler region of 105-mm artillery shells. Experimental work utilizing scene segmentation techniques followed by a sequential similarity detection algorithm for cavitation detection have yielded promising initial results. This work is described with examples of computer-detected defects

The Automated Assessment of Postural Stability: Balance Detection Algorithm.

Science.gov (United States)

Napoli, Alessandro; Glass, Stephen M; Tucker, Carole; Obeid, Iyad

2017-12-01

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect ® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0

Automated 3D-Printed Unibody Immunoarray for Chemiluminescence Detection of Cancer Biomarker Proteins

Science.gov (United States)

Tang, C. K.; Vaze, A.; Rusling, J. F.

2017-01-01

A low cost three-dimensional (3D) printed clear plastic microfluidic device was fabricated for fast, low cost automated protein detection. The unibody device features three reagent reservoirs, an efficient 3D network for passive mixing, and an optically transparent detection chamber housing a glass capture antibody array for measuring chemiluminescence output with a CCD camera. Sandwich type assays were built onto the glass arrays using a multi-labeled detection antibody-polyHRP (HRP = horseradish peroxidase). Total assay time was ~30 min in a complete automated assay employing a programmable syringe pump so that the protocol required minimal operator intervention. The device was used for multiplexed detection of prostate cancer biomarker proteins prostate specific antigen (PSA) and platelet factor 4 (PF-4). Detection limits of 0.5 pg mL−1 were achieved for these proteins in diluted serum with log dynamic ranges of four orders of magnitude. Good accuracy vs ELISA was validated by analyzing human serum samples. This prototype device holds good promise for further development as a point-of-care cancer diagnostics tool. PMID:28067370

A self-adapting system for the automated detection of inter-ictal epileptiform discharges.

Directory of Open Access Journals (Sweden)

Shaun S Lodder

Full Text Available PURPOSE: Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search. METHODS: Our solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form "IED nominations", each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections. KEY FINDINGS: Using the described method and fifteen evaluation EEGs (241 IEDs, one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20-30 min recordings 1 took approximately 5 min. SIGNIFICANCE: The proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents.

Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network

Directory of Open Access Journals (Sweden)

Mineichi Kudo

2012-12-01

Full Text Available An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the “pixel values” as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc. performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% (F1 value, the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone.

Automated Incident Detection Using Real-Time Floating Car Data

Directory of Open Access Journals (Sweden)

Maarten Houbraken

2017-01-01

Full Text Available The aim of this paper is to demonstrate the feasibility of a live Automated Incident Detection (AID system using only Floating Car Data (FCD in one of the first large-scale FCD AID field trials. AID systems detect traffic events and alert upcoming drivers to improve traffic safety without human monitoring. These automated systems traditionally rely on traffic monitoring sensors embedded in the road. FCD allows for finer spatial granularity of traffic monitoring. However, low penetration rates of FCD probe vehicles and the data latency have historically hindered FCD AID deployment. We use a live country-wide FCD system monitoring an estimated 5.93% of all vehicles. An FCD AID system is presented and compared to the installed AID system (using loop sensor data on 2 different highways in Netherlands. Our results show the FCD AID can adequately monitor changing traffic conditions and follow the AID benchmark. The presented FCD AID is integrated with the road operator systems as part of an innovation project, making this, to the best of our knowledge, the first full chain technical feasibility trial of an FCD-only AID system. Additionally, FCD allows for AID on roads without installed sensors, allowing road safety improvements at low cost.

Automated image based prominent nucleoli detection.

Science.gov (United States)

Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

2015-01-01

Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

Automated image based prominent nucleoli detection

Directory of Open Access Journals (Sweden)

Choon K Yap

2015-01-01

Full Text Available Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

M. Ehlers

2012-08-01

Full Text Available A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST of the change algorithms is applied to calculate the probability of change for a particular location. CEST

Early detection of pharmacovigilance signals with automated methods based on false discovery rates: a comparative study.

Science.gov (United States)

Ahmed, Ismaïl; Thiessard, Frantz; Miremont-Salamé, Ghada; Haramburu, Françoise; Kreft-Jais, Carmen; Bégaud, Bernard; Tubert-Bitter, Pascale

2012-06-01

Improving the detection of drug safety signals has led several pharmacovigilance regulatory agencies to incorporate automated quantitative methods into their spontaneous reporting management systems. The three largest worldwide pharmacovigilance databases are routinely screened by the lower bound of the 95% confidence interval of proportional reporting ratio (PRR₀₂.₅), the 2.5% quantile of the Information Component (IC₀₂.₅) or the 5% quantile of the Gamma Poisson Shrinker (GPS₀₅). More recently, Bayesian and non-Bayesian False Discovery Rate (FDR)-based methods were proposed that address the arbitrariness of thresholds and allow for a built-in estimate of the FDR. These methods were also shown through simulation studies to be interesting alternatives to the currently used methods. The objective of this work was twofold. Based on an extensive retrospective study, we compared PRR₀₂.₅, GPS₀₅ and IC₀₂.₅ with two FDR-based methods derived from the Fisher's exact test and the GPS model (GPS(pH0) [posterior probability of the null hypothesis H₀ calculated from the Gamma Poisson Shrinker model]). Secondly, restricting the analysis to GPS(pH0), we aimed to evaluate the added value of using automated signal detection tools compared with 'traditional' methods, i.e. non-automated surveillance operated by pharmacovigilance experts. The analysis was performed sequentially, i.e. every month, and retrospectively on the whole French pharmacovigilance database over the period 1 January 1996-1 July 2002. Evaluation was based on a list of 243 reference signals (RSs) corresponding to investigations launched by the French Pharmacovigilance Technical Committee (PhVTC) during the same period. The comparison of detection methods was made on the basis of the number of RSs detected as well as the time to detection. Results comparing the five automated quantitative methods were in favour of GPS(pH0) in terms of both number of detections of true signals and

AAAI 1993 Fall Symposium Reports

OpenAIRE

Levinson, Robert; Epstein, Susan; Terveen, Loren; Bonasso, R. Peter; Miller, David P.; Bowyer, Kevin; Hall, Lawrence

1994-01-01

The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?

Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

Directory of Open Access Journals (Sweden)

Rami Alazrai

2017-03-01

Full Text Available This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD. Furthermore, we have developed a histogram-based representation (HBR based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93 . 6 % , 77 . 6 % and 65 . 1 % , in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.

Automated detection of acute haemorrhagic stroke in non-contrasted CT images

International Nuclear Information System (INIS)

Meetz, K.; Buelow, T.

2007-01-01

An efficient treatment of stroke patients implies a profound differential diagnosis that includes the detection of acute haematoma. The proposed approach provides an automated detection of acute haematoma, assisting the non-stroke expert in interpreting non-contrasted CT images. It consists of two steps: First, haematoma candidates are detected applying multilevel region growing approach based on a typical grey value characteristic. Second, true haematomas are differentiated from partial volume artefacts, relying on spatial features derived from distance-based histograms. This approach achieves a specificity of 77% and a sensitivity of 89.7% in detecting acute haematoma in non-contrasted CT images when applied to a set of 25 non-contrasted CT images. (orig.)

Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

International Nuclear Information System (INIS)

Linguraru, Marius George; Panjwani, Neil; Fletcher, Joel G.; Summer, Ronald M.

2011-01-01

Purpose: To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm. Methods: An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm. Results: The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan. Conclusions: An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.

Multiplex RT-PCR and Automated Microarray for Detection of Eight Bovine Viruses.

Science.gov (United States)

Lung, O; Furukawa-Stoffer, T; Burton Hughes, K; Pasick, J; King, D P; Hodko, D

2017-12-01

Microarrays can be a useful tool for pathogen detection as it allow for simultaneous interrogation of the presence of a large number of genetic sequences in a sample. However, conventional microarrays require extensive manual handling and multiple pieces of equipment for printing probes, hybridization, washing and signal detection. In this study, a reverse transcription (RT)-PCR with an accompanying novel automated microarray for simultaneous detection of eight viruses that affect cattle [vesicular stomatitis virus (VSV), bovine viral diarrhoea virus type 1 and type 2, bovine herpesvirus 1, bluetongue virus, malignant catarrhal fever virus, rinderpest virus (RPV) and parapox viruses] is described. The assay accurately identified a panel of 37 strains of the target viruses and identified a mixed infection. No non-specific reactions were observed with a panel of 23 non-target viruses associated with livestock. Vesicular stomatitis virus was detected as early as 2 days post-inoculation in oral swabs from experimentally infected animals. The limit of detection of the microarray assay was as low as 1 TCID 50 /ml for RPV. The novel microarray platform automates the entire post-PCR steps of the assay and integrates electrophoretic-driven capture probe printing in a single user-friendly instrument that allows array layout and assay configuration to be user-customized on-site. © 2016 Her Majesty the Queen in Right of Canada.

Accuracy of automated software-guided detection of significant coronary artery stenosis by CT angiography: comparison with invasive catheterisation

International Nuclear Information System (INIS)

Anders, Katharina; Uder, Michael; Achenbach, Stephan; Petit, Isabel; Daniel, Werner G.; Pflederer, Tobias

2013-01-01

True automated detection of coronary artery stenoses might be useful whenever expert evaluation is not available, or as a ''second reader'' to enhance diagnostic confidence. We evaluated the accuracy of a PC-based stenosis detection tool alone and combined with expert interpretation. One hundred coronary CT angiography datasets were evaluated with the automated software alone, by manual interpretation (axial images, multiplanar reformations and maximum intensity projections in free double-oblique planes), and by expert interpretation aware of the automated findings. Stenoses ≥ 50 % were noted per-vessel and per-patient, and compared with invasive angiography. Automated post-processing was successful in 90 % of patients (88 % of vessels). When excluding uninterpretable datasets, per-patient sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 89 %, 79 %, 74 % and 92 % (per-vessel: 82 %, 85 %, 48 % and 96 %). All 100 datasets were evaluable by expert interpretation. Per-patient sensitivity, specificity, PPV and NPV were 95 %, 95 %, 93 % and 97 % (per-vessel: 89 %,98 %, 88 % and 98 %). Knowing the results of automated interpretation did not improve the performance of expert readers. Automated off-line post-processing of coronary CT angiography shows adequate sensitivity, but relatively low specificity in coronary stenosis detection. It does not increase accuracy of expert interpretation. Failure of post-processing in 10 % of all patients necessitates additional manual image work-up. (orig.)

Automated detection of macular drusen using geometric background leveling and threshold selection.

Science.gov (United States)

Smith, R Theodore; Chan, Jackie K; Nagasaki, Takayuki; Ahmad, Umer F; Barbazetto, Irene; Sparrow, Janet; Figueroa, Marta; Merriam, Joanna

2005-02-01

Age-related macular degeneration (ARMD) is the most prevalent cause of visual loss in patients older than 60 years in the United States. Observation of drusen is the hallmark finding in the clinical evaluation of ARMD. To segment and quantify drusen found in patients with ARMD using image analysis and to compare the efficacy of image analysis segmentation with that of stereoscopic manual grading of drusen. Retrospective study. University referral center.Patients Photographs were randomly selected from an available database of patients with known ARMD in the ongoing Columbia University Macular Genetics Study. All patients were white and older than 60 years. Twenty images from 17 patients were selected as representative of common manifestations of drusen. Image preprocessing included automated color balancing and, where necessary, manual segmentation of confounding lesions such as geographic atrophy (3 images). The operator then chose among 3 automated processing options suggested by predominant drusen type. Automated processing consisted of elimination of background variability by a mathematical model and subsequent histogram-based threshold selection. A retinal specialist using a graphic tablet while viewing stereo pairs constructed digital drusen drawings for each image. The sensitivity and specificity of drusen segmentation using the automated method with respect to manual stereoscopic drusen drawings were calculated on a rigorous pixel-by-pixel basis. The median sensitivity and specificity of automated segmentation were 70% and 81%, respectively. After preprocessing and option choice, reproducibility of automated drusen segmentation was necessarily 100%. Automated drusen segmentation can be reliably performed on digital fundus photographs and result in successful quantification of drusen in a more precise manner than is traditionally possible with manual stereoscopic grading of drusen. With only minor preprocessing requirements, this automated detection

Development of a platform to combine sensor networks and home robots to improve fall detection in the home environment.

Science.gov (United States)

Della Toffola, Luca; Patel, Shyamal; Chen, Bor-rong; Ozsecen, Yalgin M; Puiatti, Alessandro; Bonato, Paolo

2011-01-01

Over the last decade, significant progress has been made in the development of wearable sensor systems for continuous health monitoring in the home and community settings. One of the main areas of application for these wearable sensor systems is in detecting emergency events such as falls. Wearable sensors like accelerometers are increasingly being used to monitor daily activities of individuals at a risk of falls, detect emergency events and send alerts to caregivers. However, such systems tend to have a high rate of false alarms, which leads to low compliance levels. Home robots can enable caregivers with the ability to quickly make an assessment and intervene if an emergency event is detected. This can provide an additional layer for detecting false positives, which can lead to improve compliance. In this paper, we present preliminary work on the development of a fall detection system based on a combination sensor networks and home robots. The sensor network architecture comprises of body worn sensors and ambient sensors distributed in the environment. We present the software architecture and conceptual design home robotic platform. We also perform preliminary characterization of the sensor network in terms of latencies and battery lifetime.

Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy.

Science.gov (United States)

Martins, Cristina; Moreira da Silva, Nadia; Silva, Guilherme; Rozanski, Verena E; Silva Cunha, Joao Paulo

2016-08-01

Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the "Advanced Brain Imaging Lab" (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician.

FahamecV1:A Low Cost Automated Metaphase Detection System

Directory of Open Access Journals (Sweden)

H. Yilmaz

2017-12-01

Full Text Available In this study, FahamecV1 is introduced and investigated as a low cost and high accuracy solution for metaphase detection. Chromosome analysis is performed at the metaphase stage and high accuracy and automated detection of the metaphase stage plays an active role in decreasing analysis time. FahamecV1 includes an optic microscope, a motorized microscope stage, an electronic control unit, a camera, a computer and a software application. Printing components of the motorized microscope stage (using a 3D printer is of the main reasons for cost reduction. Operations such as stepper motor calibration, are detection, focusing, scanning, metaphase detection and saving of coordinates into a database are automatically performed. To detect metaphases, a filter named Metafilter is developed and applied. Average scanning time per preparate is 77 sec/cm2. True positive rate is calculated as 95.1%, true negative rate is calculated as 99.0% and accuracy is calculated as 98.8%.

Home Automation

OpenAIRE

Ahmed, Zeeshan

2010-01-01

In this paper I briefly discuss the importance of home automation system. Going in to the details I briefly present a real time designed and implemented software and hardware oriented house automation research project, capable of automating house's electricity and providing a security system to detect the presence of unexpected behavior.

Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods

DEFF Research Database (Denmark)

Warby, Simon C.; Wendt, Sabrina Lyngbye; Welinder, Peter

2014-01-01

to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance...... of event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed...... that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects....

Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

Directory of Open Access Journals (Sweden)

Akara Sopharak

2013-07-01

Full Text Available Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed.

Sunglass detection method for automation of video surveillance system

Science.gov (United States)

Sikandar, Tasriva; Samsudin, Wan Nur Azhani W.; Hawari Ghazali, Kamarul; Mohd, Izzeldin I.; Fazle Rabbi, Mohammad

2018-04-01

Wearing sunglass to hide face from surveillance camera is a common activity in criminal incidences. Therefore, sunglass detection from surveillance video has become a demanding issue in automation of security systems. In this paper we propose an image processing method to detect sunglass from surveillance images. Specifically, a unique feature using facial height and width has been employed to identify the covered region of the face. The presence of covered area by sunglass is evaluated using facial height-width ratio. Threshold value of covered area percentage is used to classify the glass wearing face. Two different types of glasses have been considered i.e. eye glass and sunglass. The results of this study demonstrate that the proposed method is able to detect sunglasses in two different illumination conditions such as, room illumination as well as in the presence of sunlight. In addition, due to the multi-level checking in facial region, this method has 100% accuracy of detecting sunglass. However, in an exceptional case where fabric surrounding the face has similar color as skin, the correct detection rate was found 93.33% for eye glass.

Foreign object detection and removal to improve automated analysis of chest radiographs

International Nuclear Information System (INIS)

Hogeweg, Laurens; Sánchez, Clara I.; Melendez, Jaime; Maduskar, Pragnya; Ginneken, Bram van; Story, Alistair; Hayward, Andrew

2013-01-01

Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The method is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A z value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis

Automated detection of optical counterparts to GRBs with RAPTOR

International Nuclear Information System (INIS)

Wozniak, P. R.; Vestrand, W. T.; Evans, S.; White, R.; Wren, J.

2006-01-01

The RAPTOR system (RAPid Telescopes for Optical Response) is an array of several distributed robotic telescopes that automatically respond to GCN localization alerts. Raptor-S is a 0.4-m telescope with 24 arc min. field of view employing a 1k x 1k Marconi CCD detector, and has already detected prompt optical emission from several GRBs within the first minute of the explosion. We present a real-time data analysis and alert system for automated identification of optical transients in Raptor-S GRB response data down to the sensitivity limit of ∼ 19 mag. Our custom data processing pipeline is designed to minimize the time required to reliably identify transients and extract actionable information. The system utilizes a networked PostgreSQL database server for catalog access and distributes email alerts with successful detections

Automated detection of a prostate Ni-Ti stent in electronic portal images

OpenAIRE

Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

2006-01-01

  Udgivelsesdato: DEC  Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is ba...

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

Science.gov (United States)

Kerlikowske, Karla; Scott, Christopher G; Mahmoudzadeh, Amir P; Ma, Lin; Winham, Stacey; Jensen, Matthew R; Wu, Fang Fang; Malkov, Serghei; Pankratz, V Shane; Cummings, Steven R; Shepherd, John A; Brandt, Kathleen R; Miglioretti, Diana L; Vachon, Celine M

2018-06-05

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead. To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures. Case-control. San Francisco Mammography Registry and Mayo Clinic. 1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants. Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity. Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively. Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method. Automated and clinical BI

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

Science.gov (United States)

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

Computer-aided detection and automated CT volumetry of pulmonary nodules

International Nuclear Information System (INIS)

Marten, Katharina; Engelke, Christoph

2007-01-01

With use of multislice computed tomography (MSCT), small pulmonary nodules are being detected in vast numbers, constituting the majority of all noncalcified lung nodules. Although the prevalence of lung cancers among such lesions in lung cancer screening populations is low, their isolation may contribute to increased patient survival. Computer-aided diagnosis (CAD) has emerged as a diverse set of diagnostic tools to handle the large number of images in MSCT datasets and most importantly, includes automated detection and volumetry of pulmonary nodules. Current CAD systems can significantly enhance experienced radiologists' performance and outweigh human limitations in identifying small lesions and manually measuring their diameters, augment observer consistency in the interpretation of such examinations and may thus help to detect significantly higher rates of early malignomas and give more precise estimates on chemotherapy response than can radiologists alone. In this review, we give an overview of current CAD in lung nodule detection and volumetry and discuss their relative merits and limitations. (orig.)

Automated Detection of Oscillating Regions in the Solar Atmosphere

Science.gov (United States)

Ireland, J.; Marsh, M. S.; Kucera, T. A.; Young, C. A.

2010-01-01

Recently observed oscillations in the solar atmosphere have been interpreted and modeled as magnetohydrodynamic wave modes. This has allowed for the estimation of parameters that are otherwise hard to derive, such as the coronal magnetic-field strength. This work crucially relies on the initial detection of the oscillations, which is commonly done manually. The volume of Solar Dynamics Observatory (SDO) data will make manual detection inefficient for detecting all of the oscillating regions. An algorithm is presented that automates the detection of areas of the solar atmosphere that support spatially extended oscillations. The algorithm identifies areas in the solar atmosphere whose oscillation content is described by a single, dominant oscillation within a user-defined frequency range. The method is based on Bayesian spectral analysis of time series and image filtering. A Bayesian approach sidesteps the need for an a-priori noise estimate to calculate rejection criteria for the observed signal, and it also provides estimates of oscillation frequency, amplitude, and noise, and the error in all of these quantities, in a self-consistent way. The algorithm also introduces the notion of quality measures to those regions for which a positive detection is claimed, allowing for simple post-detection discrimination by the user. The algorithm is demonstrated on two Transition Region and Coronal Explorer (TRACE) datasets, and comments regarding its suitability for oscillation detection in SDO are made.

Performance evaluation of three automated identification systems in detecting carbapenem-resistant Enterobacteriaceae.

Science.gov (United States)

He, Qingwen; Chen, Weiyuan; Huang, Liya; Lin, Qili; Zhang, Jingling; Liu, Rui; Li, Bin

2016-06-21

Carbapenem-resistant Enterobacteriaceae (CRE) is prevalent around the world. Rapid and accurate detection of CRE is urgently needed to provide effective treatment. Automated identification systems have been widely used in clinical microbiology laboratories for rapid and high-efficient identification of pathogenic bacteria. However, critical evaluation and comparison are needed to determine the specificity and accuracy of different systems. The aim of this study was to evaluate the performance of three commonly used automated identification systems on the detection of CRE. A total of 81 non-repetitive clinical CRE isolates were collected from August 2011 to August 2012 in a Chinese university hospital, and all the isolates were confirmed to be resistant to carbapenems by the agar dilution method. The potential presence of carbapenemase genotypes of the 81 isolates was detected by PCR and sequencing. Using 81 clinical CRE isolates, we evaluated and compared the performance of three automated identification systems, MicroScan WalkAway 96 Plus, Phoenix 100, and Vitek 2 Compact, which are commonly used in China. To identify CRE, the comparator methodology was agar dilution method, while the PCR and sequencing was the comparator one to identify CPE. PCR and sequencing analysis showed that 48 of the 81 CRE isolates carried carbapenemase genes, including 23 (28.4 %) IMP-4, 14 (17.3 %) IMP-8, 5 (6.2 %) NDM-1, and 8 (9.9 %) KPC-2. Notably, one Klebsiella pneumoniae isolate produced both IMP-4 and NDM-1. One Klebsiella oxytoca isolate produced both KPC-2 and IMP-8. Of the 81 clinical CRE isolates, 56 (69.1 %), 33 (40.7 %) and 77 (95.1 %) were identified as CRE by MicroScan WalkAway 96 Plus, Phoenix 100, and Vitek 2 Compact, respectively. The sensitivities/specificities of MicroScan WalkAway, Phoenix 100 and Vitek 2 were 93.8/42.4 %, 54.2/66.7 %, and 75.0/36.4 %, respectively. The MicroScan WalkAway and Viteck2 systems are more reliable in clinical identification of

Automated Detection of Small Bodies by Space Based Observation

Science.gov (United States)

Bidstrup, P. R.; Grillmayer, G.; Andersen, A. C.; Haack, H.; Jorgensen, J. L.

The number of known comets and asteroids is increasing every year. Up till now this number is including approximately 250,000 of the largest minor planets, as they are usually referred. These discoveries are due to the Earth-based observation which has intensified over the previous decades. Additionally larger telescopes and arrays of telescopes are being used for exploring our Solar System. It is believed that all near- Earth and Main-Belt asteroids of diameters above 10 to 30 km have been discovered, leaving these groups of objects as observationally complete. However, the cataloguing of smaller bodies is incomplete as only a very small fraction of the expected number has been discovered. It is estimated that approximately 1010 main belt asteroids in the size range 1 m to 1 km are too faint to be observed using Earth-based telescopes. In order to observe these small bodies, space-based search must be initiated to remove atmospheric disturbances and to minimize the distance to the asteroids and thereby minimising the requirement for long camera integration times. A new method of space-based detection of moving non-stellar objects is currently being developed utilising the Advanced Stellar Compass (ASC) built for spacecraft attitude determination by Ørsted, Danish Technical University. The ASC serves as a backbone technology in the project as it is capable of fully automated distinction of known and unknown celestial objects. By only processing objects of particular interest, i.e. moving objects, it will be possible to discover small bodies with a minimum of ground control, with the ultimate ambition of a fully automated space search probe. Currently, the ASC is being mounted on the Flying Laptop satellite of the Institute of Space Systems, Universität Stuttgart. It will, after a launch into a low Earth polar orbit in 2008, test the detection method with the ASC equipment that already had significant in-flight experience. A future use of the ASC based automated

Climate Prediction Center (CPC) U.S. Daily Snow Fall Observations

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Observational reports of daily snow fall (1200 UTC to 1200 UTC) are made by members of the NWS Automated Surface Observing Systems (ASOS) network and NWS Cooperative...

Automated detection of unfilled pauses in speech of healthy and brain-damaged individuals

NARCIS (Netherlands)

Ossewaarde, Roelant; Jonkers, Roel; Jalvingh, Fedor; Bastiaanse, Yvonne

Automated detection of un lled pauses in speech of healthy and brain-damaged individuals Roelant Ossewaardea,b, Roel Jonkersa, Fedor Jalvingha,c, Roelien Bastiaansea aCenter for Language and Cognition, University of Groningen; bInstitute for ICT, HU University of Applied Science, Utrecht; cSt.

Automated electrochemical detection of iron ions in erythrocytes from melim minipigs suffering from melanoma

Czech Academy of Sciences Publication Activity Database

Kremplová, M.; Krejcová, l.; Hynek, D.; Barath, P.; Majzlík, P.; Horák, Vratislav; Adam, V.; Sochor, J.; Cernei, N.; Hubálek, J.; Vrba, R.; Kižek, R.

2012-01-01

Roč. 7, č. 7 (2012), s. 5893-5909 ISSN 1452-3981 Institutional research plan: CEZ:AV0Z50450515 Keywords : Automation * Biological sample * Electrochemical detection Subject RIV: CG - Electrochemistry Impact factor: 3.729, year: 2011

Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy.

Science.gov (United States)

Scotland, G S; McNamee, P; Fleming, A D; Goatman, K A; Philip, S; Prescott, G J; Sharp, P F; Williams, G J; Wykes, W; Leese, G P; Olson, J A

2010-06-01

To assess the cost-effectiveness of an improved automated grading algorithm for diabetic retinopathy against a previously described algorithm, and in comparison with manual grading. Efficacy of the alternative algorithms was assessed using a reference graded set of images from three screening centres in Scotland (1253 cases with observable/referable retinopathy and 6333 individuals with mild or no retinopathy). Screening outcomes and grading and diagnosis costs were modelled for a cohort of 180 000 people, with prevalence of referable retinopathy at 4%. Algorithm (b), which combines image quality assessment with detection algorithms for microaneurysms (MA), blot haemorrhages and exudates, was compared with a simpler algorithm (a) (using image quality assessment and MA/dot haemorrhage (DH) detection), and the current practice of manual grading. Compared with algorithm (a), algorithm (b) would identify an additional 113 cases of referable retinopathy for an incremental cost of pound 68 per additional case. Compared with manual grading, automated grading would be expected to identify between 54 and 123 fewer referable cases, for a grading cost saving between pound 3834 and pound 1727 per case missed. Extrapolation modelling over a 20-year time horizon suggests manual grading would cost between pound 25,676 and pound 267,115 per additional quality adjusted life year gained. Algorithm (b) is more cost-effective than the algorithm based on quality assessment and MA/DH detection. With respect to the value of introducing automated detection systems into screening programmes, automated grading operates within the recommended national standards in Scotland and is likely to be considered a cost-effective alternative to manual disease/no disease grading.

Phase II: Automated System for Aneuploidy Detection in Sperm Final Report CRADA No. TC-1554-98

Energy Technology Data Exchange (ETDEWEB)

Wyrobek, W. J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Dunlay, R. T. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-09-28

This was a collaborative effort between the University of California, Lawrence Livermore National Laboratory (LLNL) and Cellomics, Inc. (formerly BioDx and Biological Detection, Inc.) to develop an automated system for detecting human sperm aneuploidy. Aneuploidy (an abnormal number of chromosomes) is one of the major categories of chromosomally abnormal sperm, which results in chromosomally defective pregnancies and babies. An automated system would be used for testing the effects of toxic agents and for other research and clinical applications. This collaborated effort was funded by a National Institutes of Environmental Health Services, Phase II, Small Business Innovation Research Program (SBIR) grant to Cellornics (Contract No. N44-ES-82004).

Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease

Directory of Open Access Journals (Sweden)

Jing Wu

2016-01-01

Full Text Available In macular spectral domain optical coherence tomography (SD-OCT volumes, detection of the foveal center is required for accurate and reproducible follow-up studies, structure function correlation, and measurement grid positioning. However, disease can cause severe obscuring or deformation of the fovea, thus presenting a major challenge in automated detection. We propose a fully automated fovea detection algorithm to extract the fovea position in SD-OCT volumes of eyes with exudative maculopathy. The fovea is classified into 3 main appearances to both specify the detection algorithm used and reduce computational complexity. Based on foveal type classification, the fovea position is computed based on retinal nerve fiber layer thickness. Mean absolute distance between system and clinical expert annotated fovea positions from a dataset comprised of 240 SD-OCT volumes was 162.3 µm in cystoid macular edema and 262 µm in nAMD. The presented method has cross-vendor functionality, while demonstrating accurate and reliable performance close to typical expert interobserver agreement. The automatically detected fovea positions may be used as landmarks for intra- and cross-patient registration and to create a joint reference frame for extraction of spatiotemporal features in “big data.” Furthermore, reliable analyses of retinal thickness, as well as retinal structure function correlation, may be facilitated.

Improved automated lumen contour detection by novel multifrequency processing algorithm with current intravascular ultrasound system.

Science.gov (United States)

Kume, Teruyoshi; Kim, Byeong-Keuk; Waseda, Katsuhisa; Sathyanarayana, Shashidhar; Li, Wenguang; Teo, Tat-Jin; Yock, Paul G; Fitzgerald, Peter J; Honda, Yasuhiro

2013-02-01

The aim of this study was to evaluate a new fully automated lumen border tracing system based on a novel multifrequency processing algorithm. We developed the multifrequency processing method to enhance arterial lumen detection by exploiting the differential scattering characteristics of blood and arterial tissue. The implementation of the method can be integrated into current intravascular ultrasound (IVUS) hardware. This study was performed in vivo with conventional 40-MHz IVUS catheters (Atlantis SR Pro™, Boston Scientific Corp, Natick, MA) in 43 clinical patients with coronary artery disease. A total of 522 frames were randomly selected, and lumen areas were measured after automatically tracing lumen borders with the new tracing system and a commercially available tracing system (TraceAssist™) referred to as the "conventional tracing system." The data assessed by the two automated systems were compared with the results of manual tracings by experienced IVUS analysts. New automated lumen measurements showed better agreement with manual lumen area tracings compared with those of the conventional tracing system (correlation coefficient: 0.819 vs. 0.509). When compared against manual tracings, the new algorithm also demonstrated improved systematic error (mean difference: 0.13 vs. -1.02 mm(2) ) and random variability (standard deviation of difference: 2.21 vs. 4.02 mm(2) ) compared with the conventional tracing system. This preliminary study showed that the novel fully automated tracing system based on the multifrequency processing algorithm can provide more accurate lumen border detection than current automated tracing systems and thus, offer a more reliable quantitative evaluation of lumen geometry. Copyright © 2011 Wiley Periodicals, Inc.

An Automated, Image Processing System for Concrete Evaluation

International Nuclear Information System (INIS)

Baumgart, C.W.; Cave, S.P.; Linder, K.E.

1998-01-01

Allied Signal Federal Manufacturing ampersand Technologies (FM ampersand T) was asked to perform a proof-of-concept study for the Missouri Highway and Transportation Department (MHTD), Research Division, in June 1997. The goal of this proof-of-concept study was to ascertain if automated scanning and imaging techniques might be applied effectively to the problem of concrete evaluation. In the current evaluation process, a concrete sample core is manually scanned under a microscope. Voids (or air spaces) within the concrete are then detected visually by a human operator by incrementing the sample under the cross-hairs of a microscope and by counting the number of ''pixels'' which fall within a void. Automation of the scanning and image analysis processes is desired to improve the speed of the scanning process, to improve evaluation consistency, and to reduce operator fatigue. An initial, proof-of-concept image analysis approach was successfully developed and demonstrated using acquired black and white imagery of concrete samples. In this paper, the automated scanning and image capture system currently under development will be described and the image processing approach developed for the proof-of-concept study will be demonstrated. A development update and plans for future enhancements are also presented

Automated processing integrated with a microflow cytometer for pathogen detection in clinical matrices.

Science.gov (United States)

Golden, J P; Verbarg, J; Howell, P B; Shriver-Lake, L C; Ligler, F S

2013-02-15

A spinning magnetic trap (MagTrap) for automated sample processing was integrated with a microflow cytometer capable of simultaneously detecting multiple targets to provide an automated sample-to-answer diagnosis in 40 min. After target capture on fluorescently coded magnetic microspheres, the magnetic trap automatically concentrated the fluorescently coded microspheres, separated the captured target from the sample matrix, and exposed the bound target sequentially to biotinylated tracer molecules and streptavidin-labeled phycoerythrin. The concentrated microspheres were then hydrodynamically focused in a microflow cytometer capable of 4-color analysis (two wavelengths for microsphere identification, one for light scatter to discriminate single microspheres and one for phycoerythrin bound to the target). A three-fold decrease in sample preparation time and an improved detection limit, independent of target preconcentration, was demonstrated for detection of Escherichia coli 0157:H7 using the MagTrap as compared to manual processing. Simultaneous analysis of positive and negative controls, along with the assay reagents specific for the target, was used to obtain dose-response curves, demonstrating the potential for quantification of pathogen load in buffer and serum. Published by Elsevier B.V.

An automated detection for axonal boutons in vivo two-photon imaging of mouse

Science.gov (United States)

Li, Weifu; Zhang, Dandan; Xie, Qiwei; Chen, Xi; Han, Hua

2017-02-01

Activity-dependent changes in the synaptic connections of the brain are tightly related to learning and memory. Previous studies have shown that essentially all new synaptic contacts were made by adding new partners to existing synaptic elements. To further explore synaptic dynamics in specific pathways, concurrent imaging of pre and postsynaptic structures in identified connections is required. Consequently, considerable attention has been paid for the automated detection of axonal boutons. Different from most previous methods proposed in vitro data, this paper considers a more practical case in vivo neuron images which can provide real time information and direct observation of the dynamics of a disease process in mouse. Additionally, we present an automated approach for detecting axonal boutons by starting with deconvolving the original images, then thresholding the enhanced images, and reserving the regions fulfilling a series of criteria. Experimental result in vivo two-photon imaging of mouse demonstrates the effectiveness of our proposed method.

A longitudinal evaluation of performance of automated BCR-ABL1 quantitation using cartridge-based detection system.

Science.gov (United States)

Enjeti, Anoop; Granter, Neil; Ashraf, Asma; Fletcher, Linda; Branford, Susan; Rowlings, Philip; Dooley, Susan

2015-10-01

An automated cartridge-based detection system (GeneXpert; Cepheid) is being widely adopted in low throughput laboratories for monitoring BCR-ABL1 transcript in chronic myelogenous leukaemia. This Australian study evaluated the longitudinal performance specific characteristics of the automated system.The automated cartridge-based system was compared prospectively with the manual qRT-PCR-based reference method at SA Pathology, Adelaide, over a period of 2.5 years. A conversion factor determination was followed by four re-validations. Peripheral blood samples (n = 129) with international scale (IS) values within detectable range were selected for assessment. The mean bias, proportion of results within specified fold difference (2-, 3- and 5-fold), the concordance rate of major molecular remission (MMR) and concordance across a range of IS values on paired samples were evaluated.The initial conversion factor for the automated system was determined as 0.43. Except for the second re-validation, where a negative bias of 1.9-fold was detected, all other biases fell within desirable limits. A cartridge-specific conversion factor and efficiency value was introduced and the conversion factor was confirmed to be stable in subsequent re-validation cycles. Concordance with the reference method/laboratory at >0.1-≤10 IS was 78.2% and at ≤0.001 was 80%, compared to 86.8% in the >0.01-≤0.1 IS range. The overall and MMR concordance were 85.7% and 94% respectively, for samples that fell within ± 5-fold of the reference laboratory value over the entire period of study.Conversion factor and performance specific characteristics for the automated system were longitudinally stable in the clinically relevant range, following introduction by the manufacturer of lot specific efficiency values.

Evaluation of a New Digital Automated Glycemic Pattern Detection Tool.

Science.gov (United States)

Comellas, María José; Albiñana, Emma; Artes, Maite; Corcoy, Rosa; Fernández-García, Diego; García-Alemán, Jorge; García-Cuartero, Beatriz; González, Cintia; Rivero, María Teresa; Casamira, Núria; Weissmann, Jörg

2017-11-01

Blood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks. Multicenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns. eDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification. eDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study.

Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review

NARCIS (Netherlands)

Silva de Lima, A.L.; Evers, L.J.W.; Hahn, T.; Bataille, L.; Hamilton, J.L.; Little, M.A.; Okuma, Y.; Bloem, B.R.; Faber, M.J.

2017-01-01

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable

A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

Science.gov (United States)

Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

2014-03-01

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

Detection of a meteorite 'stream' - Observations of a second meteorite fall from the orbit of the Innisfree chondrite

Science.gov (United States)

Halliday, I.

1987-03-01

The first observational evidence of multiple meteorite falls from the same orbit is adduced from the February 6, 1980 fall of a meteorite precisely 3 yr after the fall of the Innisfree meteorite. Due consideration of the detection probability for two related objects with the meteorite camera network in western Canada suggests that the Innisfree brecciated LL chondrite was a near-surface fragment from a parent object whose radius was of the order of several tens of meters. A meteorite mass of 1.8 kg is predicted for the new object, whose recovery in the vicinity of Ridgedale, Saskatchewan, is now sought for the sake of comparison with the Innisfree chondrite.

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

Science.gov (United States)

Santoyo-Ramón, José Antonio

2018-01-01

This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA). PMID:29642638

Automated multi-lesion detection for referable diabetic retinopathy in indigenous health care.

Science.gov (United States)

Pires, Ramon; Carvalho, Tiago; Spurling, Geoffrey; Goldenstein, Siome; Wainer, Jacques; Luckie, Alan; Jelinek, Herbert F; Rocha, Anderson

2015-01-01

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.

AN AUTOMATED ROAD ROUGHNESS DETECTION FROM MOBILE LASER SCANNING DATA

Directory of Open Access Journals (Sweden)

P. Kumar

2017-05-01

Full Text Available Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

An Automated Road Roughness Detection from Mobile Laser Scanning Data

Science.gov (United States)

Kumar, P.; Angelats, E.

2017-05-01

Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

Detection of virus-specific intrathecally synthesised immunoglobulin G with a fully automated enzyme immunoassay system

Directory of Open Access Journals (Sweden)

Weissbrich Benedikt

2007-05-01

Full Text Available Abstract Background The determination of virus-specific immunoglobulin G (IgG antibodies in cerebrospinal fluid (CSF is useful for the diagnosis of virus associated diseases of the central nervous system (CNS and for the detection of a polyspecific intrathecal immune response in patients with multiple sclerosis. Quantification of virus-specific IgG in the CSF is frequently performed by calculation of a virus-specific antibody index (AI. Determination of the AI is a demanding and labour-intensive technique and therefore automation is desirable. We evaluated the precision and the diagnostic value of a fully automated enzyme immunoassay for the detection of virus-specific IgG in serum and CSF using the analyser BEP2000 (Dade Behring. Methods The AI for measles, rubella, varicella-zoster, and herpes simplex virus IgG was determined from pairs of serum and CSF samples of patients with viral CNS infections, multiple sclerosis and of control patients. CSF and serum samples were tested simultaneously with reference to a standard curve. Starting dilutions were 1:6 and 1:36 for CSF and 1:1386 and 1:8316 for serum samples. Results The interassay coefficient of variation was below 10% for all parameters tested. There was good agreement between AIs obtained with the BEP2000 and AIs derived from the semi-automated reference method. Conclusion Determination of virus-specific IgG in serum-CSF-pairs for calculation of AI has been successfully automated on the BEP2000. Current limitations of the assay layout imposed by the analyser software should be solved in future versions to offer more convenience in comparison to manual or semi-automated methods.

[Automated detection and volumetric segmentation of the spleen in CT scans].

Science.gov (United States)

Hammon, M; Dankerl, P; Kramer, M; Seifert, S; Tsymbal, A; Costa, M J; Janka, R; Uder, M; Cavallaro, A

2012-08-01

To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like "marginal space learning" and "boosting algorithms". 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm³ + 0.58 (width × length × thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 (± 2.4) seconds. The average splenic volume measured by aV was 268.21 ± 114.67 cm³ compared to 281.58 ± 130.21 cm³ in mV and 268.93 ± 104.60 cm³ in eV. The correlation coefficient was 0.99 (coefficient of determination (R²) = 0.98) for aV and mV, 0.91 (R² = 0.83) for mV and eV and 0.91 (R² = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R² = 0.84), mV and eV (0.95; R² = 0.91) and aV and eV (0.83; R² = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. © Georg Thieme Verlag KG Stuttgart · New York.

Automated detection and volumetric segmentation of the spleen in CT scans

International Nuclear Information System (INIS)

Hammon, M.; Dankerl, P.; Janka, R.; Uder, M.; Cavallaro, A.; Kramer, M.; Seifert, S.; Tsymbal, A.; Costa, M.J.

2012-01-01

To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like ''marginal space learning'' and ''boosting algorithms''. 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm 3 + 0.58 (width x length x thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 (± 2.4) seconds. The average splenic volume measured by aV was 268.21 ± 114.67 cm 3 compared to 281.58 ± 130.21 cm 3 in mV and 268.93 ± 104.60 cm 3 in eV. The correlation coefficient was 0.99 (coefficient of determination (R 2 ) = 0.98) for aV and mV, 0.91 (R 2 = 0.83) for mV and eV and 0.91 (R 2 = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R 2 = 0.84), mV and eV (0.95; R 2 = 0.91) and aV and eV (0.83; R 2 = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. (orig.)

Development and Evaluation of an Automated, Home-Based, Electronic Questionnaire for Detecting COPD Exacerbations

Directory of Open Access Journals (Sweden)

Francisco de B. Velazquez-Peña

2015-01-01

Full Text Available Collaboration between patients and their medical and technical experts enabled the development of an automated questionnaire for the early detection of COPD exacerbations (AQCE. The questionnaire consisted of fourteen questions and was implemented on a computer system for use by patients at home in an un-supervised environment. Psychometric evaluation was conducted after a 6-month field trial. Fifty-two patients were involved in the development of the questionnaire. Reproducibility was studied using 19 patients (ICC = 0.94. Sixteen out of the 19 subjects started the 6 month-field trial with the computer application. Cronbach’s alpha of 0.81 was achieved. In the concurrent validity analysis, a correlation of 0.80 (p = 0.002 with the CCQ was reported. The results suggest that AQCE is a valid and reliable questionnaire, showing that an automated home-based electronic questionnaire may enable early detection of exacerbations of COPD.

Fall detection with body-worn sensors : A systematic review

NARCIS (Netherlands)

Schwickert, L.; Becker, C.; Lindemann, U.; Marechal, C.; Bourke, A.; Chiari, L.; Helbostad, J. L.; Zijlstra, Wiebren; Aminian, K.; Todd, C.; Bandinelli, S.; Klenk, J.

2013-01-01

Background and aims. Falls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information

Automated Breast Ultrasound Lesions Detection using Convolutional Neural Networks.

Science.gov (United States)

Yap, Moi Hoon; Pons, Gerard; Marti, Joan; Ganau, Sergi; Sentis, Melcior; Zwiggelaar, Reyer; Davison, Adrian K; Marti, Robert

2017-08-07

Breast lesion detection using ultrasound imaging is considered an important step of Computer-Aided Diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e. Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

Infrared thermal imaging for automated detection of diabetic foot complications.

Science.gov (United States)

van Netten, Jaap J; van Baal, Jeff G; Liu, Chanjuan; van der Heijden, Ferdi; Bus, Sicco A

2013-09-01

Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had diffuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). No differences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with diffuse complications, mean temperature differences of >3 °C between ipsilateral and contralateral foot were found. With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or diffuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings. © 2013 Diabetes Technology Society.

Quest for automated land cover change detection using satellite time series data

CSIR Research Space (South Africa)

Salmon, BP

2009-07-01

Full Text Available and surface climate in the next fifty years,” Global Change Biology, vol. 8, no. 5, pp. 438–458, May 2002. [3] J. A. Foley et al., “Global consequences of land use,” Science, vol. 309, no. 5734, pp. 570–574, July 2005. [4] R. S. Lunetta et al., “Land... (class 1). These four subsets were used to produce a confusion matrix to test if the operational MLP can detect change reliably in an automated fashion on subsets 1 and 2, while not falsely detecting change for subsets 3 and 4. This particular splic...

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

Science.gov (United States)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

Automated detection and characterization of harmonic tremor in continuous seismic data

Science.gov (United States)

Roman, Diana C.

2017-06-01

Harmonic tremor is a common feature of volcanic, hydrothermal, and ice sheet seismicity and is thus an important proxy for monitoring changes in these systems. However, no automated methods for detecting harmonic tremor currently exist. Because harmonic tremor shares characteristics with speech and music, digital signal processing techniques for analyzing these signals can be adapted. I develop a novel pitch-detection-based algorithm to automatically identify occurrences of harmonic tremor and characterize their frequency content. The algorithm is applied to seismic data from Popocatepetl Volcano, Mexico, and benchmarked against a monthlong manually detected catalog of harmonic tremor events. During a period of heightened eruptive activity from December 2014 to May 2015, the algorithm detects 1465 min of harmonic tremor, which generally precede periods of heightened explosive activity. These results demonstrate the algorithm's ability to accurately characterize harmonic tremor while highlighting the need for additional work to understand its causes and implications at restless volcanoes.

Automated Detection of Heuristics and Biases among Pathologists in a Computer-Based System

Science.gov (United States)

Crowley, Rebecca S.; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-01-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to…

Filament Chirality over an Entire Cycle Determined with an Automated Detection Module -- a Neat Surprise!

Science.gov (United States)

Martens, Petrus C.; Yeates, A. R.; Mackay, D.; Pillai, K. G.

2013-07-01

Using metadata produced by automated solar feature detection modules developed for SDO (Martens et al. 2012) we have discovered some trends in filament chirality and filament-sigmoid relations that are new and in part contradict the current consensus. Automated detection of solar features has the advantage over manual detection of having the detection criteria applied consistently, and in being able to deal with enormous amounts of data, like the 1 Terabyte per day that SDO produces. Here we use the filament detection module developed by Bernasconi, which has metadata from 2000 on, and the sigmoid sniffer, which has been producing metadata from AIA 94 A images since October 2011. The most interesting result we find is that the hemispheric chirality preference for filaments (dextral in the north, and v.v.), studied in detail for a three year period by Pevtsov et al. (2003) seems to disappear during parts of the decline of cycle 23 and during the extended solar minimum that followed. Moreover the hemispheric chirality rule seems to be much less pronounced during the onset of cycle 24. For sigmoids we find the expected correlation between chirality and handedness (S or Z) shape but not as strong as expected.

Automated detection of multiple sclerosis lesions in serial brain MRI

International Nuclear Information System (INIS)

Llado, Xavier; Ganiler, Onur; Oliver, Arnau; Marti, Robert; Freixenet, Jordi; Valls, Laia; Vilanova, Joan C.; Ramio-Torrenta, Lluis; Rovira, Alex

2012-01-01

Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI. (orig.)

Automated detection of multiple sclerosis lesions in serial brain MRI

Energy Technology Data Exchange (ETDEWEB)

Llado, Xavier; Ganiler, Onur; Oliver, Arnau; Marti, Robert; Freixenet, Jordi [University of Girona, Computer Vision and Robotics Group, Girona (Spain); Valls, Laia [Dr. Josep Trueta University Hospital, Department of Radiology, Girona (Spain); Vilanova, Joan C. [Girona Magnetic Resonance Center, Girona (Spain); Ramio-Torrenta, Lluis [Dr. Josep Trueta University Hospital, Institut d' Investigacio Biomedica de Girona, Multiple Sclerosis and Neuroimmunology Unit, Girona (Spain); Rovira, Alex [Vall d' Hebron University Hospital, Magnetic Resonance Unit, Department of Radiology, Barcelona (Spain)

2012-08-15

Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI. (orig.)

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

Science.gov (United States)

Dereymaeker, Anneleen; Pillay, Kirubin; Vervisch, Jan; Van Huffel, Sabine; Naulaers, Gunnar; Jansen, Katrien; De Vos, Maarten

2017-09-01

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27-42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], median MF 0-0.25, median Sensitivity 0.93-1.0, and median Specificity 0.80-0.91 across this age range), with minimal misclassifications at 35-36 weeks (median [Formula: see text]). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images.

Science.gov (United States)

Seghier, Mohamed L; Kolanko, Magdalena A; Leff, Alexander P; Jäger, Hans R; Gregoire, Simone M; Werring, David J

2011-03-23

Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.

Molecular Detection of Bladder Cancer by Fluorescence Microsatellite Analysis and an Automated Genetic Analyzing System

Directory of Open Access Journals (Sweden)

Sarel Halachmi

2007-01-01

Full Text Available To investigate the ability of an automated fluorescent analyzing system to detect microsatellite alterations, in patients with bladder cancer. We investigated 11 with pathology proven bladder Transitional Cell Carcinoma (TCC for microsatellite alterations in blood, urine, and tumor biopsies. DNA was prepared by standard methods from blood, urine and resected tumor specimens, and was used for microsatellite analysis. After the primers were fluorescent labeled, amplification of the DNA was performed with PCR. The PCR products were placed into the automated genetic analyser (ABI Prism 310, Perkin Elmer, USA and were subjected to fluorescent scanning with argon ion laser beams. The fluorescent signal intensity measured by the genetic analyzer measured the product size in terms of base pairs. We found loss of heterozygocity (LOH or microsatellite alterations (a loss or gain of nucleotides, which alter the original normal locus size in all the patients by using fluorescent microsatellite analysis and an automated analyzing system. In each case the genetic changes found in urine samples were identical to those found in the resected tumor sample. The studies demonstrated the ability to detect bladder tumor non-invasively by fluorescent microsatellite analysis of urine samples. Our study supports the worldwide trend for the search of non-invasive methods to detect bladder cancer. We have overcome major obstacles that prevented the clinical use of an experimental system. With our new tested system microsatellite analysis can be done cheaper, faster, easier and with higher scientific accuracy.

Microscope image based fully automated stomata detection and pore measurement method for grapevines

Directory of Open Access Journals (Sweden)

Hiranya Jayakody

2017-11-01

Full Text Available Abstract Background Stomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve manual measurement of stomatal features. The main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input. The proposed approach, which employs machine learning and image processing techniques, can outperform available manual and semi-automatic methods used to identify and estimate stomatal morphological features. Results First, a cascade object detection learning algorithm is developed to correctly identify multiple stomata in a large microscopic image. Once the regions of interest which contain stomata are identified and extracted, a combination of image processing techniques are applied to estimate the pore dimensions of the stomata. The stomata detection approach was compared with an existing fully automated template matching technique and a semi-automatic maximum stable extremal regions approach, with the proposed method clearly surpassing the performance of the existing techniques with a precision of 91.68% and an F1-score of 0.85. Next, the morphological features of the detected stomata were measured. Contrary to existing approaches, the proposed image segmentation and skeletonization method allows us to estimate the pore dimensions even in cases where the stomatal pore boundary is only partially visible in the microscope image. A test conducted using 1267 images of stomata showed that the segmentation and skeletonization approach was able to correctly identify the stoma opening 86.27% of the time. Further comparisons made with manually traced stoma openings indicated that the proposed method is able to estimate stomata morphological features with accuracies of 89.03% for area

Automated detection of retinal whitening in malarial retinopathy

Science.gov (United States)

Joshi, V.; Agurto, C.; Barriga, S.; Nemeth, S.; Soliz, P.; MacCormick, I.; Taylor, T.; Lewallen, S.; Harding, S.

2016-03-01

Cerebral malaria (CM) is a severe neurological complication associated with malarial infection. Malaria affects approximately 200 million people worldwide, and claims 600,000 lives annually, 75% of whom are African children under five years of age. Because most of these mortalities are caused by the high incidence of CM misdiagnosis, there is a need for an accurate diagnostic to confirm the presence of CM. The retinal lesions associated with malarial retinopathy (MR) such as retinal whitening, vessel discoloration, and hemorrhages, are highly specific to CM, and their detection can improve the accuracy of CM diagnosis. This paper will focus on development of an automated method for the detection of retinal whitening which is a unique sign of MR that manifests due to retinal ischemia resulting from CM. We propose to detect the whitening region in retinal color images based on multiple color and textural features. First, we preprocess the image using color and textural features of the CMYK and CIE-XYZ color spaces to minimize camera reflex. Next, we utilize color features of the HSL, CMYK, and CIE-XYZ channels, along with the structural features of difference of Gaussians. A watershed segmentation algorithm is used to assign each image region a probability of being inside the whitening, based on extracted features. The algorithm was applied to a dataset of 54 images (40 with whitening and 14 controls) that resulted in an image-based (binary) classification with an AUC of 0.80. This provides 88% sensitivity at a specificity of 65%. For a clinical application that requires a high specificity setting, the algorithm can be tuned to a specificity of 89% at a sensitivity of 82%. This is the first published method for retinal whitening detection and combining it with the detection methods for vessel discoloration and hemorrhages can further improve the detection accuracy for malarial retinopathy.

Automated multi-lesion detection for referable diabetic retinopathy in indigenous health care.

Directory of Open Access Journals (Sweden)

Ramon Pires

Full Text Available Diabetic Retinopathy (DR is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.

Automated detection of microaneurysms using robust blob descriptors

Science.gov (United States)

Adal, K.; Ali, S.; Sidibé, D.; Karnowski, T.; Chaum, E.; Mériaudeau, F.

2013-03-01

Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

Automated lung module detection at low-dose CT: preliminary experience

International Nuclear Information System (INIS)

Goo, Jin-Mo; Lee, Jeong-Won; Lee, Hyun-Ju; Kim, Seung-Wan; Kim, Jong-Hyo; Im, Jung-Gi

2003-01-01

To determine the usefulness of a computer-aided diagnosis (CAD) system for the automated detection of lung nodules at low-dose CT. A CAD system developed for detecting lung nodules was used to process the data provided by 50 consecutive low-dose CT scans. The results of an initial report, a second look review by two chest radiologists, and those obtained by the CAD system were compared, and by reviewing all of these, a gold standard was established. By applying the gold standard, a total of 52 nodules were identified (26 with a diameter ≤ 5 mm; 26 with a diameter > 5 mm). Compared to an initial report, four additional nodules were detected by the CAD system. Three of these, identified only at CAD, formed part of the data used to derive the gold standard. For the detection of nodules > 5 mm in diameter, sensitivity was 77% for the initial report, for the second look review, and 88% for the second look review,and 65% for the CAD system. There were 8.0 ± 5.2 false-positive CAD results per CT study. These preliminary results indicate that a CAD system may improve the detection of pulmonary nodules at low-dose CT

SU-G-201-03: Automation of High Dose Rate Brachytherapy Quality Assurance: Development of a Radioluminescent Detection System for Simultaneous Detection of Activity, Timing, and Positioning

Energy Technology Data Exchange (ETDEWEB)

Jenkins, C; Xing, L; Fahimian, B [Stanford University, Stanford, CA (United States)

2016-06-15

Purpose: Accuracy of positioning, timing and activity is of critical importance for High Dose Rate (HDR) brachytherapy delivery. Respective measurements via film autoradiography, stop-watches and well chambers can be cumbersome, crude or lack dynamic source evaluation capabilities. To address such limitations, a single device radioluminescent detection system enabling automated real-time quantification of activity, position and timing accuracy is presented and experimentally evaluated. Methods: A radioluminescent sheet was fabricated by mixing Gd?O?S:Tb with PDMS and incorporated into a 3D printed device where it was fixated below a CMOS digital camera. An Ir-192 HDR source (VS2000, VariSource iX) with an effective active length of 5 mm was introduced using a 17-gauge stainless steel needle below the sheet. Pixel intensity values for determining activity were taken from an ROI centered on the source location. A calibration curve relating intensity values to activity was generated and used to evaluate automated activity determination with data gathered over 6 weeks. Positioning measurements were performed by integrating images for an entire delivery and fitting peaks to the resulting profile. Timing measurements were performed by evaluating source location and timestamps from individual images. Results: Average predicted activity error over 6 weeks was .35 ± .5%. The distance between four dwell positions was determined by the automated system to be 1.99 ± .02 cm. The result from autoradiography was 2.00 ± .03 cm. The system achieved a time resolution of 10 msec and determined the dwell time to be 1.01 sec ± .02 sec. Conclusion: The system was able to successfully perform automated detection of activity, positioning and timing concurrently under a single setup. Relative to radiochromic and radiographic film-based autoradiography, which can only provide a static evaluation positioning, optical detection of temporary radiation induced luminescence enables dynamic

Digital tripwire: a small automated human detection system

Science.gov (United States)

Fischer, Amber D.; Redd, Emmett; Younger, A. Steven

2009-05-01

A low cost, lightweight, easily deployable imaging sensor that can dependably discriminate threats from other activities within its field of view and, only then, alert the distant duty officer by transmitting a visual confirmation of the threat would provide a valuable asset to modern defense. At present, current solutions suffer from a multitude of deficiencies - size, cost, power endurance, but most notably, an inability to assess an image and conclude that it contains a threat. The human attention span cannot maintain critical surveillance over banks of displays constantly conveying such images from the field. DigitalTripwire is a small, self-contained, automated human-detection system capable of running for 1-5 days on two AA batteries. To achieve such long endurance, the DigitalTripwire system utilizes an FPGA designed with sleep functionality. The system uses robust vision algorithms, such as a partially unsupervised innovative backgroundmodeling algorithm, which employ several data reduction strategies to operate in real-time, and achieve high detection rates. When it detects human activity, either mounted or dismounted, it sends an alert including images to notify the command center. In this paper, we describe the hardware and software design of the DigitalTripwire system. In addition, we provide detection and false alarm rates across several challenging data sets demonstrating the performance of the vision algorithms in autonomously analyzing the video stream and classifying moving objects into four primary categories - dismounted human, vehicle, non-human, or unknown. Performance results across several challenging data sets are provided.

Sensitivity of hemozoin detection by automated flow cytometry in non- and semi-immune malaria patients

NARCIS (Netherlands)

Grobusch, Martin P.; Hänscheid, Thomas; Krämer, Benedikt; Neukammer, Jörg; May, Jürgen; Seybold, Joachim; Kun, Jürgen F. J.; Suttorp, Norbert

2003-01-01

BACKGROUND: Cell-Dyn automated blood cell analyzers use laser flow cytometry technology, allowing detection of malaria pigment (hemozoin) in monocytes. We evaluated the value of such an instrument to diagnose malaria in febrile travelers returning to Berlin, Germany, the relation between the

Falls and falls efficacy: the role of sustained attention in older adults

LENUS (Irish Health Repository)

O'Halloran, Aisling M

2011-12-19

Abstract Background Previous evidence indicates that older people allocate more of their attentional resources toward their gait and that the attention-related changes that occur during aging increase the risk of falls. The aim of this study was to investigate whether performance and variability in sustained attention is associated with falls and falls efficacy in older adults. Methods 458 community-dwelling adults aged ≥ 60 years underwent a comprehensive geriatric assessment. Mean and variability of reaction time (RT), commission errors and omission errors were recorded during a fixed version of the Sustained Attention to Response Task (SART). RT variability was decomposed using the Fast Fourier Transform (FFT) procedure, to help characterise variability associated with the arousal and vigilance aspects of sustained attention. The number of self-reported falls in the previous twelve months, and falls efficacy (Modified Falls Efficacy Scale) were also recorded. Results Significant increases in the mean and variability of reaction time on the SART were significantly associated with both falls (p < 0.01) and reduced falls efficacy (p < 0.05) in older adults. An increase in omission errors was also associated with falls (p < 0.01) and reduced falls efficacy (p < 0.05). Upon controlling for age and gender affects, logistic regression modelling revealed that increasing variability associated with the vigilance (top-down) aspect of sustained attention was a retrospective predictor of falling (p < 0.01, OR = 1.14, 95% CI: 1.03 - 1.26) in the previous year and was weakly correlated with reduced falls efficacy in non-fallers (p = 0.07). Conclusions Greater variability in sustained attention is strongly correlated with retrospective falls and to a lesser degree with reduced falls efficacy. This cognitive measure may provide a novel and valuable biomarker for falls in older adults, potentially allowing for early detection and the implementation of preventative intervention

Automated night/day standoff detection, tracking, and identification of personnel for installation protection

Science.gov (United States)

Lemoff, Brian E.; Martin, Robert B.; Sluch, Mikhail; Kafka, Kristopher M.; McCormick, William; Ice, Robert

2013-06-01

The capability to positively and covertly identify people at a safe distance, 24-hours per day, could provide a valuable advantage in protecting installations, both domestically and in an asymmetric warfare environment. This capability would enable installation security officers to identify known bad actors from a safe distance, even if they are approaching under cover of darkness. We will describe an active-SWIR imaging system being developed to automatically detect, track, and identify people at long range using computer face recognition. The system illuminates the target with an eye-safe and invisible SWIR laser beam, to provide consistent high-resolution imagery night and day. SWIR facial imagery produced by the system is matched against a watch-list of mug shots using computer face recognition algorithms. The current system relies on an operator to point the camera and to review and interpret the face recognition results. Automation software is being developed that will allow the system to be cued to a location by an external system, automatically detect a person, track the person as they move, zoom in on the face, select good facial images, and process the face recognition results, producing alarms and sharing data with other systems when people are detected and identified. Progress on the automation of this system will be presented along with experimental night-time face recognition results at distance.

A nationwide web-based automated system for early outbreak detection and rapid response in China

Directory of Open Access Journals (Sweden)

Yilan Liao

2011-03-01

Full Text Available Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and to transmit that information either in real-time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC in China at the county, prefecture, provincial and national levels are involved in the system. It assists with early outbreak detection at the local level and prompts reporting of unusual disease occurrences or potential outbreaks to CDCs throughout the country.

Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

OpenAIRE

Veldin A. Talorete Jr.; Sherwin A Guirnaldo

2017-01-01

This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifie...

[Automated analyzer of enzyme immunoassay].

Science.gov (United States)

Osawa, S

1995-09-01

Automated analyzers for enzyme immunoassay can be classified by several points of view: the kind of labeled antibodies or enzymes, detection methods, the number of tests per unit time, analytical time and speed per run. In practice, it is important for us consider the several points such as detection limits, the number of tests per unit time, analytical range, and precision. Most of the automated analyzers on the market can randomly access and measure samples. I will describe the recent advance of automated analyzers reviewing their labeling antibodies and enzymes, the detection methods, the number of test per unit time and analytical time and speed per test.

Automated analysis for detecting beams in laser wakefield simulations

International Nuclear Information System (INIS)

Ushizima, Daniela M.; Rubel, Oliver; Prabhat, Mr.; Weber, Gunther H.; Bethel, E. Wes; Aragon, Cecilia R.; Geddes, Cameron G.R.; Cormier-Michel, Estelle; Hamann, Bernd; Messmer, Peter; Hagen, Hans

2008-01-01

Laser wakefield particle accelerators have shown the potential to generate electric fields thousands of times higher than those of conventional accelerators. The resulting extremely short particle acceleration distance could yield a potential new compact source of energetic electrons and radiation, with wide applications from medicine to physics. Physicists investigate laser-plasma internal dynamics by running particle-in-cell simulations; however, this generates a large dataset that requires time-consuming, manual inspection by experts in order to detect key features such as beam formation. This paper describes a framework to automate the data analysis and classification of simulation data. First, we propose a new method to identify locations with high density of particles in the space-time domain, based on maximum extremum point detection on the particle distribution. We analyze high density electron regions using a lifetime diagram by organizing and pruning the maximum extrema as nodes in a minimum spanning tree. Second, we partition the multivariate data using fuzzy clustering to detect time steps in a experiment that may contain a high quality electron beam. Finally, we combine results from fuzzy clustering and bunch lifetime analysis to estimate spatially confined beams. We demonstrate our algorithms successfully on four different simulation datasets

Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review.

Science.gov (United States)

Silva de Lima, Ana Lígia; Evers, Luc J W; Hahn, Tim; Bataille, Lauren; Hamilton, Jamie L; Little, Max A; Okuma, Yasuyuki; Bloem, Bastiaan R; Faber, Marjan J

2017-08-01

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

Application of Novel Software Algorithms to Spectral-Domain Optical Coherence Tomography for Automated Detection of Diabetic Retinopathy.

Science.gov (United States)

Adhi, Mehreen; Semy, Salim K; Stein, David W; Potter, Daniel M; Kuklinski, Walter S; Sleeper, Harry A; Duker, Jay S; Waheed, Nadia K

2016-05-01

To present novel software algorithms applied to spectral-domain optical coherence tomography (SD-OCT) for automated detection of diabetic retinopathy (DR). Thirty-one diabetic patients (44 eyes) and 18 healthy, nondiabetic controls (20 eyes) who underwent volumetric SD-OCT imaging and fundus photography were retrospectively identified. A retina specialist independently graded DR stage. Trained automated software generated a retinal thickness score signifying macular edema and a cluster score signifying microaneurysms and/or hard exudates for each volumetric SD-OCT. Of 44 diabetic eyes, 38 had DR and six eyes did not have DR. Leave-one-out cross-validation using a linear discriminant at missed detection/false alarm ratio of 3.00 computed software sensitivity and specificity of 92% and 69%, respectively, for DR detection when compared to clinical assessment. Novel software algorithms applied to commercially available SD-OCT can successfully detect DR and may have potential as a viable screening tool for DR in future. [Ophthalmic Surg Lasers Imaging Retina. 2016;47:410-417.]. Copyright 2016, SLACK Incorporated.

Developing an Automated Machine Learning Marine Oil Spill Detection System with Synthetic Aperture Radar

Science.gov (United States)

Pinales, J. C.; Graber, H. C.; Hargrove, J. T.; Caruso, M. J.

2016-02-01

Previous studies have demonstrated the ability to detect and classify marine hydrocarbon films with spaceborne synthetic aperture radar (SAR) imagery. The dampening effects of hydrocarbon discharges on small surface capillary-gravity waves renders the ocean surface "radar dark" compared with the standard wind-borne ocean surfaces. Given the scope and impact of events like the Deepwater Horizon oil spill, the need for improved, automated and expedient monitoring of hydrocarbon-related marine anomalies has become a pressing and complex issue for governments and the extraction industry. The research presented here describes the development, training, and utilization of an algorithm that detects marine oil spills in an automated, semi-supervised manner, utilizing X-, C-, or L-band SAR data as the primary input. Ancillary datasets include related radar-borne variables (incidence angle, etc.), environmental data (wind speed, etc.) and textural descriptors. Shapefiles produced by an experienced human-analyst served as targets (validation) during the training portion of the investigation. Training and testing datasets were chosen for development and assessment of algorithm effectiveness as well as optimal conditions for oil detection in SAR data. The algorithm detects oil spills by following a 3-step methodology: object detection, feature extraction, and classification. Previous oil spill detection and classification methodologies such as machine learning algorithms, artificial neural networks (ANN), and multivariate classification methods like partial least squares-discriminant analysis (PLS-DA) are evaluated and compared. Statistical, transform, and model-based image texture techniques, commonly used for object mapping directly or as inputs for more complex methodologies, are explored to determine optimal textures for an oil spill detection system. The influence of the ancillary variables is explored, with a particular focus on the role of strong vs. weak wind forcing.

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

Science.gov (United States)

Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

2009-09-01

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

Automated approach to detecting behavioral states using EEG-DABS

Directory of Open Access Journals (Sweden)

Zachary B. Loris

2017-07-01

Full Text Available Electrocorticographic (ECoG signals represent cortical electrical dipoles generated by synchronous local field potentials that result from simultaneous firing of neurons at distinct frequencies (brain waves. Since different brain waves correlate to different behavioral states, ECoG signals presents a novel strategy to detect complex behaviors. We developed a program, EEG Detection Analysis for Behavioral States (EEG-DABS that advances Fast Fourier Transforms through ECoG signals time series, separating it into (user defined frequency bands and normalizes them to reduce variability. EEG-DABS determines events if segments of an experimental ECoG record have significantly different power bands than a selected control pattern of EEG. Events are identified at every epoch and frequency band and then are displayed as output graphs by the program. Certain patterns of events correspond to specific behaviors. Once a predetermined pattern was selected for a behavioral state, EEG-DABS correctly identified the desired behavioral event. The selection of frequency band combinations for detection of the behavior affects accuracy of the method. All instances of certain behaviors, such as freezing, were correctly identified from the event patterns generated with EEG-DABS. Detecting behaviors is typically achieved by visually discerning unique animal phenotypes, a process that is time consuming, unreliable, and subjective. EEG-DABS removes variability by using defined parameters of EEG/ECoG for a desired behavior over chronic recordings. EEG-DABS presents a simple and automated approach to quantify different behavioral states from ECoG signals.

Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

Directory of Open Access Journals (Sweden)

Veldin A. Talorete Jr.

2017-03-01

Full Text Available This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifier is in charge of classifying the detected subject’s posture patterns from subject’s silhouette. Moreover, the Intruder Localization Classifier is in charge of classifying the detected pattern’s location classifier will estimate the location of the intruder with respect to the fence using geometric feature from images as inputs. The system is capable of activating the alarm, display the actual image and depict the location of the intruder when an intruder is detected. In detecting intruder posture, the system’s success rate of 88%. Overall system accuracy for day-time intruder localization is 83% and an accuracy of 88% for night-time intruder localization

Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children.

Science.gov (United States)

Serras Pereira, Mariana; Cozijn, Reinier; Postma, Eric; Shahid, Suleman; Swerts, Marc

2016-01-01

The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology - the combination of perception studies (in which eye-tracking technology is being used) and automated movement analysis. Film fragments from truthful and deceptive children were shown to human judges who were given the task to decide whether the recorded child was being truthful or not. Results reveal that judges are able to accurately distinguish truthful clips from lying clips in both perception studies. Even though the automated movement analysis for overall and specific body regions did not yield significant results between the experimental conditions, we did find a positive correlation between the amount of movement in a child and the perception of lies, i.e., the more movement the children exhibited during a clip, the higher the chance that the clip was perceived as a lie. The eye-tracking study revealed that, even when there is movement happening in different body regions, judges tend to focus their attention mainly on the face region. This is the first study that compares a perceptual and an automated method for the detection of deceptive behavior in children whose data have been elicited through an ecologically valid paradigm.

[Automated detection of estrus and mastitis in dairy cows].

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de Mol, R M

2001-02-15

The development and test of detection models for oestrus and mastitis in dairy cows is described in a PhD thesis that was defended in Wageningen on June 5, 2000. These models were based on sensors for milk yield, milk temperature, electrical conductivity of milk, and cow activity and concentrate intake, and on combined processing of the sensor data. The models alert farmers to cows that need attention, because of possible oestrus or mastitis. A first detection model for cows, milked twice a day, was based on time series models for the sensor variables. A time series model describes the dependence between successive observations. The parameters of the time series models were fitted on-line for each cow after each milking by means of a Kalman filter, a mathematical method to estimate the state of a system on-line. The Kalman filter gives the best estimate of the current state of a system based on all preceding observations. This model was tested for 2 years on two experimental farms, and under field conditions on four farms over several years. A second detection model, for cow milked in an automatic milking system (AMS), was based on a generalization of the first model. Two data sets (one small, one large) were used for testing. The results for oestrus detection were good for both models. The results for mastitis detection were varying (in some cases good, in other cases moderate). Fuzzy logic was used to classify mastitis and oestrus alerts with both detection models, to reduce the number of false positive alerts. Fuzzy logic makes approximate reasoning possible, where statements can be partly true or false. Input for the fuzzy logic model were alerts from the detection models and additional information. The number of false positive alerts decreased considerably, while the number of detected cases remained at the same level. These models make automated detection possible in practice.

Intrinsic factors associated with pregnancy falls.

Science.gov (United States)

Wu, Xuefang; Yeoh, Han T

2014-10-01

Approximately 25% to 27% of women sustain a fall during pregnancy, and falls are associated with serious injuries and can affect pregnancy outcomes. The objective of the current study was to identify intrinsic factors associated with pregnancy that may contribute to women's increased risk of falls. A literature search (Medline and Pubmed) identified articles published between January 1980 and June 2013 that measured associations between pregnancy and fall risks, using an existing fall accident investigation framework. The results indicated that physiological, biomechanical, and psychological changes associated with pregnancy may influence the initiation, detection, and recovery phases of falls and increase the risk of falls in this population. Considering the logistic difficulties and ethnic concerns in recruiting pregnant women to participate in this investigation of fall risk factors, identification of these factors could establish effective fall prevention and intervention programs for pregnant women and improve birth outcomes. [Workplace Health Saf 2014;62(10):403-408.]. Copyright 2014, SLACK Incorporated.

Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm

Science.gov (United States)

Hibert, C.; Michéa, D.; Provost, F.; Malet, J. P.; Geertsema, M.

2017-12-01

of continuous seismic record by the Alaskan permanent seismic network and Hi-Climb trans-Himalayan seismic network. The processing chain we developed also opens the possibility for a near-real time seismic detection of landslides, in association with remote-sensing automated detection from Sentinel 2 images for example.

Automated eddy-current installation AVD-01 for detecting flaws in fuel element cans

International Nuclear Information System (INIS)

Varvaritsa, V.P.; Martishchenko, L.G.; Popov, V.K.; Romanov, M.L.; Shlepnev, I.O.; Shmatok, V.P.

1986-01-01

This paper describes an automated installation for eddy-current flaw detection in thin-walled pipes with small diameter; its unified transport system makes it possible to use the installation in inspection lines and production lines of fuel elements. The article describes the structural diagrams of the installation and presents the results of investigations connected with the selection for establishing the optimum regimes and sensitivity of feedthrough transducers with focusing screens

Anomaly detection in an automated safeguards system using neural networks

International Nuclear Information System (INIS)

Whiteson, R.; Howell, J.A.

1992-01-01

An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

Comparison of automated processing of flocked swabs with manual processing of fiber swabs for detection of nasal carriage of Staphylococcus aureus.

Science.gov (United States)

Jones, Gillian; Matthews, Roger; Cunningham, Richard; Jenks, Peter

2011-07-01

The sensitivity of automated culture of Staphylococcus aureus from flocked swabs versus that of manual culture of fiber swabs was prospectively compared using nasal swabs from 867 patients. Automated culture from flocked swabs significantly increased the detection rate, by 13.1% for direct culture and 10.2% for enrichment culture.

A novel fully automated molecular diagnostic system (AMDS for colorectal cancer mutation detection.

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Shiro Kitano

Full Text Available BACKGROUND: KRAS, BRAF and PIK3CA mutations are frequently observed in colorectal cancer (CRC. In particular, KRAS mutations are strong predictors for clinical outcomes of EGFR-targeted treatments such as cetuximab and panitumumab in metastatic colorectal cancer (mCRC. For mutation analysis, the current methods are time-consuming, and not readily available to all oncologists and pathologists. We have developed a novel, simple, sensitive and fully automated molecular diagnostic system (AMDS for point of care testing (POCT. Here we report the results of a comparison study between AMDS and direct sequencing (DS in the detection of KRAS, BRAF and PI3KCA somatic mutations. METHODOLOGY/PRINCIPAL FINDING: DNA was extracted from a slice of either frozen (n = 89 or formalin-fixed and paraffin-embedded (FFPE CRC tissue (n = 70, and then used for mutation analysis by AMDS and DS. All mutations (n = 41 among frozen and 27 among FFPE samples detected by DS were also successfully (100% detected by the AMDS. However, 8 frozen and 6 FFPE samples detected as wild-type in the DS analysis were shown as mutants in the AMDS analysis. By cloning-sequencing assays, these discordant samples were confirmed as true mutants. One sample had simultaneous "hot spot" mutations of KRAS and PIK3CA, and cloning assay comfirmed that E542K and E545K were not on the same allele. Genotyping call rates for DS were 100.0% (89/89 and 74.3% (52/70 in frozen and FFPE samples, respectively, for the first attempt; whereas that of AMDS was 100.0% for both sample sets. For automated DNA extraction and mutation detection by AMDS, frozen tissues (n = 41 were successfully detected all mutations within 70 minutes. CONCLUSIONS/SIGNIFICANCE: AMDS has superior sensitivity and accuracy over DS, and is much easier to execute than conventional labor intensive manual mutation analysis. AMDS has great potential for POCT equipment for mutation analysis.

Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

International Nuclear Information System (INIS)

Drukker, Karen; Sennett, Charlene A.; Giger, Maryellen L.

2014-01-01

Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V ® ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation

Fully automated atlas-based hippocampal volumetry for detection of Alzheimer's disease in a memory clinic setting.

Science.gov (United States)

Suppa, Per; Anker, Ulrich; Spies, Lothar; Bopp, Irene; Rüegger-Frey, Brigitte; Klaghofer, Richard; Gocke, Carola; Hampel, Harald; Beck, Sacha; Buchert, Ralph

2015-01-01

Hippocampal volume is a promising biomarker to enhance the accuracy of the diagnosis of dementia due to Alzheimer's disease (AD). However, whereas hippocampal volume is well studied in patient samples from clinical trials, its value in clinical routine patient care is still rather unclear. The aim of the present study, therefore, was to evaluate fully automated atlas-based hippocampal volumetry for detection of AD in the setting of a secondary care expert memory clinic for outpatients. One-hundred consecutive patients with memory complaints were clinically evaluated and categorized into three diagnostic groups: AD, intermediate AD, and non-AD. A software tool based on open source software (Statistical Parametric Mapping SPM8) was employed for fully automated tissue segmentation and stereotactical normalization of high-resolution three-dimensional T1-weighted magnetic resonance images. Predefined standard masks were used for computation of grey matter volume of the left and right hippocampus which then was scaled to the patient's total grey matter volume. The right hippocampal volume provided an area under the receiver operating characteristic curve of 84% for detection of AD patients in the whole sample. This indicates that fully automated MR-based hippocampal volumetry fulfills the requirements for a relevant core feasible biomarker for detection of AD in everyday patient care in a secondary care memory clinic for outpatients. The software used in the present study has been made freely available as an SPM8 toolbox. It is robust and fast so that it is easily integrated into routine workflow.

Automated rice leaf disease detection using color image analysis

Science.gov (United States)

Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

2011-06-01

In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

A New Paradigm of Technology-Enabled ‘Vital Signs’ for Early Detection of Health Change for Older Adults.

Science.gov (United States)

Rantz, Marilyn J; Skubic, Marjorie; Popescu, Mihail; Galambos, Colleen; Koopman, Richelle J; Alexander, Gregory L; Phillips, Lorraine J; Musterman, Katy; Back, Jessica; Miller, Steven J

2015-01-01

Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of ‘vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status ‘vital signs', developing automated methods for interpreting them, and alerting others when changes occur have the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption.

Automated detection and classification of cryptographic algorithms in binary programs through machine learning

OpenAIRE

Hosfelt, Diane Duros

2015-01-01

Threats from the internet, particularly malicious software (i.e., malware) often use cryptographic algorithms to disguise their actions and even to take control of a victim's system (as in the case of ransomware). Malware and other threats proliferate too quickly for the time-consuming traditional methods of binary analysis to be effective. By automating detection and classification of cryptographic algorithms, we can speed program analysis and more efficiently combat malware. This thesis wil...

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population.

Science.gov (United States)

Bourke, A K; Klenk, J; Schwickert, L; Aminian, K; Ihlen, E A F; Helbostad, J L; Chiari, L; Becker, C

2015-01-01

Automatic fall detection will reduce the consequences of falls in the elderly and promote independent living, ensuring people can confidently live safely at home. Inertial sensor technology can distinguish falls from normal activities. However, fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events. We have extracted temporal and kinematic parameters to further improve the development of fall detection algorithms. A total of 100 real-world falls were analysed. Subjects with a known fall history were recruited, inertial sensors were attached to L5 and a fall report, following a fall, was used to extract the fall signal. This data-set was examined, and variables were extracted that include upper and lower impact peak values, posture angle change during the fall and time of occurrence. These extracted parameters, can be used to inform the design of fall-detection algorithms for real-world falls detection in the elderly.

Automated Detection of Branch Shaking Locations for Robotic Cherry Harvesting Using Machine Vision

Directory of Open Access Journals (Sweden)

Suraj Amatya

2017-10-01

Full Text Available Automation in cherry harvesting is essential to reduce the demand for seasonal labor for cherry picking and reduce the cost of production. The mechanical shaking of tree branches is one of the widely studied and used techniques for harvesting small tree fruit crops like cherries. To automate the branch shaking operation, different methods of detecting branches and cherries in full foliage canopies of the cherry tree have been developed previously. The next step in this process is the localization of shaking positions in the detected tree branches for mechanical shaking. In this study, a method of locating shaking positions for automated cherry harvesting was developed based on branch and cherry pixel locations determined using RGB images and 3D camera images. First, branch and cherry regions were located in 2D RGB images. Depth information provided by a 3D camera was then mapped on to the RGB images using a standard stereo calibration method. The overall root mean square error in estimating the distance to desired shaking points was 0.064 m. Cherry trees trained in two different canopy architectures, Y-trellis and vertical trellis systems, were used in this study. Harvesting testing was carried out by shaking tree branches at the locations selected by the algorithm. For the Y-trellis system, the maximum fruit removal efficiency of 92.9% was achieved using up to five shaking events per branch. However, maximum fruit removal efficiency for the vertical trellis system was 86.6% with up to four shakings per branch. However, it was found that only three shakings per branch would achieve a fruit removal percentage of 92.3% and 86.4% in Y and vertical trellis systems respectively.

An automated and integrated framework for dust storm detection based on ogc web processing services

Science.gov (United States)

Xiao, F.; Shea, G. Y. K.; Wong, M. S.; Campbell, J.

2014-11-01

Dust storms are known to have adverse effects on public health. Atmospheric dust loading is also one of the major uncertainties in global climatic modelling as it is known to have a significant impact on the radiation budget and atmospheric stability. The complexity of building scientific dust storm models is coupled with the scientific computation advancement, ongoing computing platform development, and the development of heterogeneous Earth Observation (EO) networks. It is a challenging task to develop an integrated and automated scheme for dust storm detection that combines Geo-Processing frameworks, scientific models and EO data together to enable the dust storm detection and tracking processes in a dynamic and timely manner. This study develops an automated and integrated framework for dust storm detection and tracking based on the Web Processing Services (WPS) initiated by Open Geospatial Consortium (OGC). The presented WPS framework consists of EO data retrieval components, dust storm detecting and tracking component, and service chain orchestration engine. The EO data processing component is implemented based on OPeNDAP standard. The dust storm detecting and tracking component combines three earth scientific models, which are SBDART model (for computing aerosol optical depth (AOT) of dust particles), WRF model (for simulating meteorological parameters) and HYSPLIT model (for simulating the dust storm transport processes). The service chain orchestration engine is implemented based on Business Process Execution Language for Web Service (BPEL4WS) using open-source software. The output results, including horizontal and vertical AOT distribution of dust particles as well as their transport paths, were represented using KML/XML and displayed in Google Earth. A serious dust storm, which occurred over East Asia from 26 to 28 Apr 2012, is used to test the applicability of the proposed WPS framework. Our aim here is to solve a specific instance of a complex EO data

Semi-automated, occupationally safe immunofluorescence microtip sensor for rapid detection of Mycobacterium cells in sputum.

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Shinnosuke Inoue

Full Text Available An occupationally safe (biosafe sputum liquefaction protocol was developed for use with a semi-automated antibody-based microtip immunofluorescence sensor. The protocol effectively liquefied sputum and inactivated microorganisms including Mycobacterium tuberculosis, while preserving the antibody-binding activity of Mycobacterium cell surface antigens. Sputum was treated with a synergistic chemical-thermal protocol that included moderate concentrations of NaOH and detergent at 60°C for 5 to 10 min. Samples spiked with M. tuberculosis complex cells showed approximately 10(6-fold inactivation of the pathogen after treatment. Antibody binding was retained post-treatment, as determined by analysis with a microtip immunosensor. The sensor correctly distinguished between Mycobacterium species and other cell types naturally present in biosafe-treated sputum, with a detection limit of 100 CFU/mL for M. tuberculosis, in a 30-minute sample-to-result process. The microtip device was also semi-automated and shown to be compatible with low-cost, LED-powered fluorescence microscopy. The device and biosafe sputum liquefaction method opens the door to rapid detection of tuberculosis in settings with limited laboratory infrastructure.

Preclinical Alzheimer disease and risk of falls.

Science.gov (United States)

Stark, Susan L; Roe, Catherine M; Grant, Elizabeth A; Hollingsworth, Holly; Benzinger, Tammie L; Fagan, Anne M; Buckles, Virginia D; Morris, John C

2013-07-30

We determined the rate of falls among cognitively normal, community-dwelling older adults, some of whom had presumptive preclinical Alzheimer disease (AD) as detected by in vivo imaging of fibrillar amyloid plaques using Pittsburgh compound B (PiB) and PET and/or by assays of CSF to identify Aβ₄₂, tau, and phosphorylated tau. We conducted a 12-month prospective cohort study to examine the cumulative incidence of falls. Participants were evaluated clinically and underwent PiB PET imaging and lumbar puncture. Falls were reported monthly using an individualized calendar journal returned by mail. A Cox proportional hazards model was used to test whether time to first fall was associated with each biomarker and the ratio of CSF tau/Aβ₄₂ and CSF phosphorylated tau/Aβ₄₂, after adjustment for common fall risk factors. The sample (n = 125) was predominately female (62.4%) and white (96%) with a mean age of 74.4 years. When controlled for ability to perform activities of daily living, higher levels of PiB retention (hazard ratio = 2.95 [95% confidence interval 1.01-6.45], p = 0.05) and of CSF biomarker ratios (p risk factor for falls in older adults. This study suggests that subtle noncognitive changes that predispose older adults to falls are associated with AD and may precede detectable cognitive changes.

Automated Detection of Firearms and Knives in a CCTV Image

Science.gov (United States)

Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

2016-01-01

Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims. PMID:26729128

Automated Detection of Firearms and Knives in a CCTV Image

Directory of Open Access Journals (Sweden)

Michał Grega

2016-01-01

Full Text Available Closed circuit television systems (CCTV are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

Automated Detection of Firearms and Knives in a CCTV Image.

Science.gov (United States)

Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

2016-01-01

Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs.

Science.gov (United States)

Chakrabarty, Lipi; Joshi, Gopal Datt; Chakravarty, Arunava; Raman, Ganesh V; Krishnadas, S R; Sivaswamy, Jayanthi

2016-07-01

To describe and evaluate the performance of an automated CAD system for detection of glaucoma from color fundus photographs. Color fundus photographs of 2252 eyes from 1126 subjects were collected from 2 centers: Aravind Eye Hospital, Madurai and Coimbatore, India. The images of 1926 eyes (963 subjects) were used to train an automated image analysis-based system, which was developed to provide a decision on a given fundus image. A total of 163 subjects were clinically examined by 2 ophthalmologists independently and their diagnostic decisions were recorded. The consensus decision was defined to be the clinical reference (gold standard). Fundus images of eyes with disagreement in diagnosis were excluded from the study. The fundus images of the remaining 314 eyes (157 subjects) were presented to 4 graders and their diagnostic decisions on the same were collected. The performance of the system was evaluated on the 314 images, using the reference standard. The sensitivity and specificity of the system and 4 independent graders were determined against the clinical reference standard. The system achieved an area under receiver operating characteristic curve of 0.792 with a sensitivity of 0.716 and specificity of 0.717 at a selected threshold for the detection of glaucoma. The agreement with the clinical reference standard as determined by Cohen κ is 0.45 for the proposed system. This is comparable to that of the image-based decisions of 4 ophthalmologists. An automated system was presented for glaucoma detection from color fundus photographs. The overall evaluation results indicated that the presented system was comparable in performance to glaucoma classification by a manual grader solely based on fundus image examination.

Sensors and Automated Analyzers for Radionuclides

International Nuclear Information System (INIS)

Grate, Jay W.; Egorov, Oleg B.

2003-01-01

The production of nuclear weapons materials has generated large quantities of nuclear waste and significant environmental contamination. We have developed new, rapid, automated methods for determination of radionuclides using sequential injection methodologies to automate extraction chromatographic separations, with on-line flow-through scintillation counting for real time detection. This work has progressed in two main areas: radionuclide sensors for water monitoring and automated radiochemical analyzers for monitoring nuclear waste processing operations. Radionuclide sensors have been developed that collect and concentrate radionuclides in preconcentrating minicolumns with dual functionality: chemical selectivity for radionuclide capture and scintillation for signal output. These sensors can detect pertechnetate to below regulatory levels and have been engineered into a prototype for field testing. A fully automated process monitor has been developed for total technetium in nuclear waste streams. This instrument performs sample acidification, speciation adjustment, separation and detection in fifteen minutes or less

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.

Science.gov (United States)

Ni, Yizhao; Lingren, Todd; Hall, Eric S; Leonard, Matthew; Melton, Kristin; Kirkendall, Eric S

2018-05-01

Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools). Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P patient exposure to potential harm following MAE events.

Low power multi-camera system and algorithms for automated threat detection

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Huber, David J.; Khosla, Deepak; Chen, Yang; Van Buer, Darrel J.; Martin, Kevin

2013-05-01

A key to any robust automated surveillance system is continuous, wide field-of-view sensor coverage and high accuracy target detection algorithms. Newer systems typically employ an array of multiple fixed cameras that provide individual data streams, each of which is managed by its own processor. This array can continuously capture the entire field of view, but collecting all the data and back-end detection algorithm consumes additional power and increases the size, weight, and power (SWaP) of the package. This is often unacceptable, as many potential surveillance applications have strict system SWaP requirements. This paper describes a wide field-of-view video system that employs multiple fixed cameras and exhibits low SWaP without compromising the target detection rate. We cycle through the sensors, fetch a fixed number of frames, and process them through a modified target detection algorithm. During this time, the other sensors remain powered-down, which reduces the required hardware and power consumption of the system. We show that the resulting gaps in coverage and irregular frame rate do not affect the detection accuracy of the underlying algorithms. This reduces the power of an N-camera system by up to approximately N-fold compared to the baseline normal operation. This work was applied to Phase 2 of DARPA Cognitive Technology Threat Warning System (CT2WS) program and used during field testing.

Automated multi-radionuclide separation and analysis with combined detection capability

Science.gov (United States)

Plionis, Alexander Asterios

The radiological dispersal device (RDD) is a weapon of great concern to those agencies responsible for protecting the public from the modern age of terrorism. In order to effectively respond to an RDD event, these agencies need to possess the capability to rapidly identify the radiological agents involved in the incident and assess the uptake of each individual victim. Since medical treatment for internal radiation poisoning is radionuclide-specific, it is critical to identify and quantify the radiological uptake of each individual victim. This dissertation describes the development of automated analytical components that could be used to determine and quantify multiple radionuclides in human urine bioassays. This is accomplished through the use of extraction chromatography that is plumbed in-line with one of a variety of detection instruments. Flow scintillation analysis is used for 90Sr and 210Po determination, flow gamma analysis is used assess 60 Co and 137Cs, and inductively coupled plasma mass spectrometry is used to determine actinides. Detection limits for these analytes were determined for the appropriate technique and related to their implications for health physics.

Automated detection of lung nodules in low-dose computed tomography

International Nuclear Information System (INIS)

Cascio, D.; Cheran, S.C.; Chincarini, A.; De Nunzio, G.; Delogu, P.; Fantacci, M.E.; Gargano, G.; Gori, I.; Retico, A.; Masala, G.L.; Preite Martinez, A.; Santoro, M.; Spinelli, C.; Tarantino, T.

2007-01-01

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (∝300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scan. (orig.)

Automated detection of extradural and subdural hematoma for contrast-enhanced CT images in emergency medical care

Science.gov (United States)

Hara, Takeshi; Matoba, Naoto; Zhou, Xiangrong; Yokoi, Shinya; Aizawa, Hiroaki; Fujita, Hiroshi; Sakashita, Keiji; Matsuoka, Tetsuya

2007-03-01

We have been developing the CAD scheme for head and abdominal injuries for emergency medical care. In this work, we have developed an automated method to detect typical head injuries, rupture or strokes of brain. Extradural and subdural hematoma region were detected by comparing technique after the brain areas were registered using warping. We employ 5 normal and 15 stroke cases to estimate the performance after creating the brain model with 50 normal cases. Some of the hematoma regions were detected correctly in all of the stroke cases with no false positive findings on normal cases.

Comparison of Automated Processing of Flocked Swabs with Manual Processing of Fiber Swabs for Detection of Nasal Carriage of Staphylococcus aureus▿‡

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Jones, Gillian; Matthews, Roger; Cunningham, Richard; Jenks, Peter

2011-01-01

The sensitivity of automated culture of Staphylococcus aureus from flocked swabs versus that of manual culture of fiber swabs was prospectively compared using nasal swabs from 867 patients. Automated culture from flocked swabs significantly increased the detection rate, by 13.1% for direct culture and 10.2% for enrichment culture. PMID:21525218

Development of an Automated Microfluidic System for DNA Collection, Amplification, and Detection of Pathogens

Energy Technology Data Exchange (ETDEWEB)

Hagan, Bethany S.; Bruckner-Lea, Cynthia J.

2002-12-01

This project was focused on developing and testing automated routines for a microfluidic Pathogen Detection System. The basic pathogen detection routine has three primary components; cell concentration, DNA amplification, and detection. In cell concentration, magnetic beads are held in a flow cell by an electromagnet. Sample liquid is passed through the flow cell and bacterial cells attach to the beads. These beads are then released into a small volume of fluid and delivered to the peltier device for cell lysis and DNA amplification. The cells are lysed during initial heating in the peltier device, and the released DNA is amplified using polymerase chain reaction (PCR) or strand displacement amplification (SDA). Once amplified, the DNA is then delivered to a laser induced fluorescence detection unit in which the sample is detected. These three components create a flexible platform that can be used for pathogen detection in liquid and sediment samples. Future developments of the system will include on-line DNA detection during DNA amplification and improved capture and release methods for the magnetic beads during cell concentration.

Automated location detection of injection site for preclinical stereotactic neurosurgery procedure

Science.gov (United States)

Abbaszadeh, Shiva; Wu, Hemmings C. H.

2017-03-01

Currently, during stereotactic neurosurgery procedures, the manual task of locating the proper area for needle insertion or implantation of electrode/cannula/optic fiber can be time consuming. The requirement of the task is to quickly and accurately find the location for insertion. In this study we investigate an automated method to locate the entry point of region of interest. This method leverages a digital image capture system, pattern recognition, and motorized stages. Template matching of known anatomical identifiable regions is used to find regions of interest (e.g. Bregma) in rodents. For our initial study, we tackle the problem of automatically detecting the entry point.

An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice

Directory of Open Access Journals (Sweden)

Ahmet Turan Özdemir

2016-07-01

Full Text Available Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer’s movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN classifier, Bayesian decision making (BDM, support vector machines (SVM, least squares method (LSM, dynamic time warping (DTW and artificial neural networks (ANNs. Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today’s wearable applications.

An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice.

Science.gov (United States)

Özdemir, Ahmet Turan

2016-07-25

Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer's movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today's wearable applications.

Shape based automated detection of pulmonary nodules with surface feature based false positive reduction

International Nuclear Information System (INIS)

Nomura, Y.; Itoh, H.; Masutani, Y.; Ohtomo, K.; Maeda, E.; Yoshikawa, T.; Hayashi, N.

2007-01-01

We proposed a shape based automated detection of pulmonary nodules with surface feature based false positive (FP) reduction. In the proposed system, the FP existing in internal of vessel bifurcation is removed using extracted surface of vessels and nodules. From the validation with 16 chest CT scans, we find that the proposed CAD system achieves 18.7 FPs/scan at 90% sensitivity, and 7.8 FPs/scan at 80% sensitivity. (orig.)

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting

Directory of Open Access Journals (Sweden)

Christoph eSchmitz

2014-05-01

Full Text Available Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D cell counting approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38–99% and false-positive rates from 3.6–82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.

Automated Detection of Salt Marsh Platforms : a Topographic Method

Science.gov (United States)

Goodwin, G.; Mudd, S. M.; Clubb, F. J.

2017-12-01

Monitoring the topographic evolution of coastal marshes is a crucial step toward improving the management of these valuable landscapes under the pressure of relative sea level rise and anthropogenic modification. However, determining their geometrically complex boundaries currently relies on spectral vegetation detection methods or requires labour-intensive field surveys and digitisation.We propose a novel method to reproducibly isolate saltmarsh scarps and platforms from a DEM. Field observations and numerical models show that saltmarshes mature into sub-horizontal platforms delineated by sub-vertical scarps: based on this premise, we identify scarps as lines of local maxima on a slope*relief raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. Non-dimensional search parameters allow batch-processing of data without recalibration. We test our method using lidar-derived DEMs of six saltmarshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and automatic segregation exceeds 90% for resolutions of 1m, with all but one sites maintaining this performance for resolutions up to 3.5m. For resolutions of 1m, automatically detected platforms are comparable in surface area and elevation distribution to digitised platforms. We also find that our method allows the accurate detection of local bloc failures 3 times larger than the DEM resolution.Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, automatic detection classifies them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method would benefit from a combination with existing creek detection algorithms. Fallen blocs and pioneer zones are inconsistently identified, particularly in macro-tidal marshes, leading to differences between digitisation and the automated method

Errors detected in pediatric oral liquid medication doses prepared in an automated workflow management system.

Science.gov (United States)

Bledsoe, Sarah; Van Buskirk, Alex; Falconer, R James; Hollon, Andrew; Hoebing, Wendy; Jokic, Sladan

2018-02-01

The effectiveness of barcode-assisted medication preparation (BCMP) technology on detecting oral liquid dose preparation errors. From June 1, 2013, through May 31, 2014, a total of 178,344 oral doses were processed at Children's Mercy, a 301-bed pediatric hospital, through an automated workflow management system. Doses containing errors detected by the system's barcode scanning system or classified as rejected by the pharmacist were further reviewed. Errors intercepted by the barcode-scanning system were classified as (1) expired product, (2) incorrect drug, (3) incorrect concentration, and (4) technological error. Pharmacist-rejected doses were categorized into 6 categories based on the root cause of the preparation error: (1) expired product, (2) incorrect concentration, (3) incorrect drug, (4) incorrect volume, (5) preparation error, and (6) other. Of the 178,344 doses examined, 3,812 (2.1%) errors were detected by either the barcode-assisted scanning system (1.8%, n = 3,291) or a pharmacist (0.3%, n = 521). The 3,291 errors prevented by the barcode-assisted system were classified most commonly as technological error and incorrect drug, followed by incorrect concentration and expired product. Errors detected by pharmacists were also analyzed. These 521 errors were most often classified as incorrect volume, preparation error, expired product, other, incorrect drug, and incorrect concentration. BCMP technology detected errors in 1.8% of pediatric oral liquid medication doses prepared in an automated workflow management system, with errors being most commonly attributed to technological problems or incorrect drugs. Pharmacists rejected an additional 0.3% of studied doses. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

The neurobiology of falls.

Science.gov (United States)

Fasano, Alfonso; Plotnik, Meir; Bove, Francesco; Berardelli, Alfredo

2012-12-01

Falling is a major clinical problem; especially, in elderly population as it often leads to fractures, immobilization, poor quality of life and life-span reduction. Given the growing body of evidences on the physiopathology of balance disorders in humans, in recent years the approach of research on falls has completely changed and new instruments and new definitions have been formulated. Among them, the definition of "idiopathic faller" (i.e. no overt cause for falling in a given subject) represented a milestone in building the "science of falling". This review deals with the new determinants of the neurobiology of falling: (1) the role of motor impairment and particularly of those "mild parkinsonian signs" frequently detectable in elderly subjects, (2) the role of executive and attentive resources when coping with obstacles, (3) the role of vascular lesions in "highest level gait disorder" (a condition tightly connected with senile gait, cautious gait and frailty), (4) the role of the failure of automaticity or inter-limbs coordination/symmetry during walking and such approach would definitely help the development of screening instrument for subjects at risk (still lacking in present days). This translational approach will lead to the development of specific therapeutic interventions.

Geohazard reconnaissance mapping for potential rock boulder fall using low altitude UAV photogrammetry

Science.gov (United States)

Sharan Kumar, N.; Ashraf Mohamad Ismail, Mohd; Sukor, Nur Sabahiah Abdul; Cheang, William

2018-05-01

This paper discusses potential applications of unmanned aerial vehicles (UAVs) for evaluation of risk immediately with photos and 3-dimensional digital element. Aerial photography using UAV ready to give a powerful technique for potential rock boulder fall recognition. High-resolution outputs from this method give the chance to evaluate the site for potential rock boulder falls spatially. The utilization of UAV to capture the aerial photos is a quick, reliable, and cost-effective technique contrasted with terrestrial laser scanning method. Reconnaissance of potential rock boulder susceptible to fall is very crucial during the geotechnical investigation. This process is essential in the view of the rock fall hazards nearby site before the beginning of any preliminary work. Photogrammetric applications have empowered the automated way to deal with identification of rock boulder susceptible to fall by recognizing the location, size, and position. A developing examination of the utilization of digital photogrammetry gives numerous many benefits for civil engineering application. These advancements have made important contributions to our capabilities to create the geohazard map on potential rock boulder fall.

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection

International Nuclear Information System (INIS)

Zelst, J.C.M. van; Tan, T.; Platel, B.; Jong, M. de; Steenbakkers, A.; Mourits, M.; Grivegnee, A.; Borelli, C.; Karssemeijer, N.; Mann, R.M.

2017-01-01

Objective: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. Methods: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n = 40) with >1 year of follow up, benign (n = 30) lesions that were either biopsied or remained stable, and malignant lesions (n = 20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. Results: Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p = 0.001). Sensitivity of all readers improved (range 5.2–10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4–5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. Conclusions: Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer.

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection

Energy Technology Data Exchange (ETDEWEB)

Zelst, J.C.M. van, E-mail: Jan.vanZelst@radboudumc.nl [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands); Tan, T.; Platel, B. [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands); Jong, M. de [Jeroen Bosch Medical Centre, Department of Radiology, ‘s-Hertogenbosch (Netherlands); Steenbakkers, A. [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands); Mourits, M. [Jeroen Bosch Medical Centre, Department of Radiology, ‘s-Hertogenbosch (Netherlands); Grivegnee, A. [Jules Bordet Institute, Department of Radiology, Brussels (Belgium); Borelli, C. [Catholic University of the Sacred Heart, Department of Radiological Sciences, Rome (Italy); Karssemeijer, N.; Mann, R.M. [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands)

2017-04-15

Objective: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. Methods: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n = 40) with >1 year of follow up, benign (n = 30) lesions that were either biopsied or remained stable, and malignant lesions (n = 20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. Results: Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p = 0.001). Sensitivity of all readers improved (range 5.2–10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4–5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. Conclusions: Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer.

History of falls, gait, balance, and fall risks in older cancer survivors living in the community.

Science.gov (United States)

Huang, Min H; Shilling, Tracy; Miller, Kara A; Smith, Kristin; LaVictoire, Kayle

2015-01-01

Older cancer survivors may be predisposed to falls because cancer-related sequelae affect virtually all body systems. The use of a history of falls, gait speed, and balance tests to assess fall risks remains to be investigated in this population. This study examined the relationship of previous falls, gait, and balance with falls in community-dwelling older cancer survivors. At the baseline, demographics, health information, and the history of falls in the past year were obtained through interviewing. Participants performed tests including gait speed, Balance Evaluation Systems Test, and short-version of Activities-specific Balance Confidence scale. Falls were tracked by mailing of monthly reports for 6 months. A "faller" was a person with ≥1 fall during follow-up. Univariate analyses, including independent sample t-tests and Fisher's exact tests, compared baseline demographics, gait speed, and balance between fallers and non-fallers. For univariate analyses, Bonferroni correction was applied for multiple comparisons. Baseline variables with Pfalls with age as covariate. Sensitivity and specificity of each predictor of falls in the model were calculated. Significance level for the regression analysis was Pfalls. Baseline demographics, health information, history of falls, gaits speed, and balance tests did not differ significantly between fallers and non-fallers. Forward logistic regression revealed that a history of falls was a significant predictor of falls in the final model (odds ratio =6.81; 95% confidence interval =1.594-29.074) (Pfalls were 74% and 69%, respectively. Current findings suggested that for community-dwelling older cancer survivors with mixed diagnoses, asking about the history of falls may help detect individuals at risk of falling.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Directory of Open Access Journals (Sweden)

Ramesh Rajagopalan

2017-11-01

Full Text Available Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Science.gov (United States)

Rajagopalan, Ramesh; Litvan, Irene; Jung, Tzyy-Ping

2017-11-01

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

Automated thermochemolysis reactor for detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry

Energy Technology Data Exchange (ETDEWEB)

Li, Dan [Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602 (United States); Rands, Anthony D.; Losee, Scott C. [Torion Technologies, American Fork, UT 84003 (United States); Holt, Brian C. [Department of Statistics, Brigham Young University, Provo, UT 84602 (United States); Williams, John R. [Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602 (United States); Lammert, Stephen A. [Torion Technologies, American Fork, UT 84003 (United States); Robison, Richard A. [Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602 (United States); Tolley, H. Dennis [Department of Statistics, Brigham Young University, Provo, UT 84602 (United States); Lee, Milton L., E-mail: milton_lee@byu.edu [Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602 (United States)

2013-05-02

Graphical abstract: -- Highlights: •An automated sample preparation system for Bacillus anthracis endospores was developed. •A thermochemolysis method was applied to produce and derivatize biomarkers for Bacillus anthracis detection. •The autoreactor controlled the precise delivery of reagents, and TCM reaction times and temperatures. •Solid phase microextraction was used to extract biomarkers, and GC–MS was used for final identification. •This autoreactor was successfully applied to the identification of Bacillus anthracis endospores. -- Abstract: An automated sample preparation system was developed and tested for the rapid detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry (GC–MS) for eventual use in the field. This reactor is capable of automatically processing suspected bio-threat agents to release and derivatize unique chemical biomarkers by thermochemolysis (TCM). The system automatically controls the movement of sample vials from one position to another, crimping of septum caps onto the vials, precise delivery of reagents, and TCM reaction times and temperatures. The specific operations of introduction of sample vials, solid phase microextraction (SPME) sampling, injection into the GC–MS system, and ejection of used vials from the system were performed manually in this study, although they can be integrated into the automated system. Manual SPME sampling is performed by following visual and audible signal prompts for inserting the fiber into and retracting it from the sampling port. A rotating carousel design allows for simultaneous sample collection, reaction, biomarker extraction and analysis of sequential samples. Dipicolinic acid methyl ester (DPAME), 3-methyl-2-butenoic acid methyl ester (a fragment of anthrose) and two methylated sugars were used to compare the performance of the autoreactor with manual TCM. Statistical algorithms were used to construct reliable bacterial endospore signatures, and 24

An automated microfluidic DNA microarray platform for genetic variant detection in inherited arrhythmic diseases.

Science.gov (United States)

Huang, Shu-Hong; Chang, Yu-Shin; Juang, Jyh-Ming Jimmy; Chang, Kai-Wei; Tsai, Mong-Hsun; Lu, Tzu-Pin; Lai, Liang-Chuan; Chuang, Eric Y; Huang, Nien-Tsu

2018-03-12

In this study, we developed an automated microfluidic DNA microarray (AMDM) platform for point mutation detection of genetic variants in inherited arrhythmic diseases. The platform allows for automated and programmable reagent sequencing under precise conditions of hybridization flow and temperature control. It is composed of a commercial microfluidic control system, a microfluidic microarray device, and a temperature control unit. The automated and rapid hybridization process can be performed in the AMDM platform using Cy3 labeled oligonucleotide exons of SCN5A genetic DNA, which produces proteins associated with sodium channels abundant in the heart (cardiac) muscle cells. We then introduce a graphene oxide (GO)-assisted DNA microarray hybridization protocol to enable point mutation detection. In this protocol, a GO solution is added after the staining step to quench dyes bound to single-stranded DNA or non-perfectly matched DNA, which can improve point mutation specificity. As proof-of-concept we extracted the wild-type and mutant of exon 12 and exon 17 of SCN5A genetic DNA from patients with long QT syndrome or Brugada syndrome by touchdown PCR and performed a successful point mutation discrimination in the AMDM platform. Overall, the AMDM platform can greatly reduce laborious and time-consuming hybridization steps and prevent potential contamination. Furthermore, by introducing the reciprocating flow into the microchannel during the hybridization process, the total assay time can be reduced to 3 hours, which is 6 times faster than the conventional DNA microarray. Given the automatic assay operation, shorter assay time, and high point mutation discrimination, we believe that the AMDM platform has potential for low-cost, rapid and sensitive genetic testing in a simple and user-friendly manner, which may benefit gene screening in medical practice.

Automated thermochemolysis reactor for detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry

International Nuclear Information System (INIS)

Li, Dan; Rands, Anthony D.; Losee, Scott C.; Holt, Brian C.; Williams, John R.; Lammert, Stephen A.; Robison, Richard A.; Tolley, H. Dennis; Lee, Milton L.

2013-01-01

Graphical abstract: -- Highlights: •An automated sample preparation system for Bacillus anthracis endospores was developed. •A thermochemolysis method was applied to produce and derivatize biomarkers for Bacillus anthracis detection. •The autoreactor controlled the precise delivery of reagents, and TCM reaction times and temperatures. •Solid phase microextraction was used to extract biomarkers, and GC–MS was used for final identification. •This autoreactor was successfully applied to the identification of Bacillus anthracis endospores. -- Abstract: An automated sample preparation system was developed and tested for the rapid detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry (GC–MS) for eventual use in the field. This reactor is capable of automatically processing suspected bio-threat agents to release and derivatize unique chemical biomarkers by thermochemolysis (TCM). The system automatically controls the movement of sample vials from one position to another, crimping of septum caps onto the vials, precise delivery of reagents, and TCM reaction times and temperatures. The specific operations of introduction of sample vials, solid phase microextraction (SPME) sampling, injection into the GC–MS system, and ejection of used vials from the system were performed manually in this study, although they can be integrated into the automated system. Manual SPME sampling is performed by following visual and audible signal prompts for inserting the fiber into and retracting it from the sampling port. A rotating carousel design allows for simultaneous sample collection, reaction, biomarker extraction and analysis of sequential samples. Dipicolinic acid methyl ester (DPAME), 3-methyl-2-butenoic acid methyl ester (a fragment of anthrose) and two methylated sugars were used to compare the performance of the autoreactor with manual TCM. Statistical algorithms were used to construct reliable bacterial endospore signatures, and 24

Performance evaluation of an automated single-channel sleep–wake detection algorithm

Directory of Open Access Journals (Sweden)

Kaplan RF

2014-10-01

Full Text Available Richard F Kaplan,1 Ying Wang,1 Kenneth A Loparo,1,2 Monica R Kelly,3 Richard R Bootzin3 1General Sleep Corporation, Euclid, OH, USA; 2Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA; 3Department of Psychology, University of Arizona, Tucson, AZ, USA Background: A need exists, from both a clinical and a research standpoint, for objective sleep measurement systems that are both easy to use and can accurately assess sleep and wake. This study evaluates the output of an automated sleep–wake detection algorithm (Z-ALG used in the Zmachine (a portable, single-channel, electroencephalographic [EEG] acquisition and analysis system against laboratory polysomnography (PSG using a consensus of expert visual scorers. Methods: Overnight laboratory PSG studies from 99 subjects (52 females/47 males, 18–60 years, median age 32.7 years, including both normal sleepers and those with a variety of sleep disorders, were assessed. PSG data obtained from the differential mastoids (A1–A2 were assessed by Z-ALG, which determines sleep versus wake every 30 seconds using low-frequency, intermediate-frequency, and high-frequency and time domain EEG features. PSG data were independently scored by two to four certified PSG technologists, using standard Rechtschaffen and Kales guidelines, and these score files were combined on an epoch-by-epoch basis, using a majority voting rule, to generate a single score file per subject to compare against the Z-ALG output. Both epoch-by-epoch and standard sleep indices (eg, total sleep time, sleep efficiency, latency to persistent sleep, and wake after sleep onset were compared between the Z-ALG output and the technologist consensus score files. Results: Overall, the sensitivity and specificity for detecting sleep using the Z-ALG as compared to the technologist consensus are 95.5% and 92.5%, respectively, across all subjects, and the positive predictive value and the

Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists' workload.

Science.gov (United States)

Soto-Pedre, Enrique; Navea, Amparo; Millan, Saray; Hernaez-Ortega, Maria C; Morales, Jesús; Desco, Maria C; Pérez, Pablo

2015-02-01

To assess the safety and workload reduction of an automated 'disease/no disease' grading system for diabetic retinopathy (DR) within a systematic screening programme. Single 45° macular field image per eye was obtained from consecutive patients attending a regional primary care based DR screening programme in Valencia (Spain). The sensitivity and specificity of automated system operating as 'one or more than one microaneurysm detection for disease presence' grader were determined relative to a manual grading as gold standard. Data on age, gender and diabetes mellitus were also recorded. A total of 5278 patients with diabetes were screened. The median age and duration of diabetes was 69 years and 6.9 years, respectively. Estimated prevalence of DR was 15.6%. The software classified 43.9% of the patients as having no DR and 26.1% as having ungradable images. Detection of DR was achieved with 94.5% sensitivity (95% CI 92.6- 96.5) and 68.8% specificity (95%CI 67.2-70.4). The overall accuracy of the automated system was 72.5% (95%CI 71.1-73.9). The present retinal image processing algorithm that can act as prefilter to flag out images with pathological lesions can be implemented in practice. Our results suggest that it could be considered when implementing DR screening programmes. © 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

The "Sigmoid Sniffer” and the "Advanced Automated Solar Filament Detection and Characterization Code” Modules

Science.gov (United States)

Raouafi, Noureddine; Bernasconi, P. N.; Georgoulis, M. K.

2010-05-01

We present two pattern recognition algorithms, the "Sigmoid Sniffer” and the "Advanced Automated Solar Filament Detection and Characterization Code,” that are among the Feature Finding modules of the Solar Dynamic Observatory: 1) Coronal sigmoids visible in X-rays and the EUV are the result of highly twisted magnetic fields. They can occur anywhere on the solar disk and are closely related to solar eruptive activity (e.g., flares, CMEs). Their appearance is typically synonym of imminent solar eruptions, so they can serve as a tool to forecast solar activity. Automatic X-ray sigmoid identification offers an unbiased way of detecting short-to-mid term CME precursors. The "Sigmoid Sniffer” module is capable of automatically detecting sigmoids in full-disk X-ray images and determining their chirality, as well as other characteristics. It uses multiple thresholds to identify persistent bright structures on a full-disk X-ray image of the Sun. We plan to apply the code to X-ray images from Hinode/XRT, as well as on SDO/AIA images. When implemented in a near real-time environment, the Sigmoid Sniffer could allow 3-7 day forecasts of CMEs and their potential to cause major geomagnetic storms. 2)The "Advanced Automated Solar Filament Detection and Characterization Code” aims to identify, classify, and track solar filaments in full-disk Hα images. The code can reliably identify filaments; determine their chirality and other relevant parameters like filament area, length, and average orientation with respect to the equator. It is also capable of tracking the day-by-day evolution of filaments as they traverse the visible disk. The code was tested by analyzing daily Hα images taken at the Big Bear Solar Observatory from mid-2000 to early-2005. It identified and established the chirality of thousands of filaments without human intervention.

31 CFR 205.17 - Are funds transfers delayed by automated payment systems restrictions based on the size and...

Science.gov (United States)

2010-07-01

... automated payment systems restrictions based on the size and timing of the drawdown request subject to this... Treasury-State Agreement § 205.17 Are funds transfers delayed by automated payment systems restrictions... to payment processes that automatically reject drawdown requests that fall outside a pre-determined...

AUTOMATED DETECTION OF OIL DEPOTS FROM HIGH RESOLUTION IMAGES: A NEW PERSPECTIVE

Directory of Open Access Journals (Sweden)

A. O. Ok

2015-03-01

Full Text Available This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012. The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

Energy Technology Data Exchange (ETDEWEB)

Adal, Kedir M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sidebe, Desire [Univ. of Burgundy, Dijon (France); Ali, Sharib [Univ. of Burgundy, Dijon (France); Chaum, Edward [Univ. of Tennessee, Knoxville, TN (United States); Karnowski, Thomas Paul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Meriaudeau, Fabrice [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2014-01-07

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

Automated detection of extended sources in radio maps: progress from the SCORPIO survey

Science.gov (United States)

Riggi, S.; Ingallinera, A.; Leto, P.; Cavallaro, F.; Bufano, F.; Schillirò, F.; Trigilio, C.; Umana, G.; Buemi, C. S.; Norris, R. P.

2016-08-01

Automated source extraction and parametrization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper, we present a new algorithm, called CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parametrization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, also including different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the Evolutionary Map of the Universe (EMU) survey at the Australian Square Kilometre Array Pathfinder (ASKAP). The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.

Flow cytometric-membrane potential detection of sodium channel active marine toxins: application to ciguatoxins in fish muscle and feasibility of automating saxitoxin detection.

Science.gov (United States)

Manger, Ronald; Woodle, Doug; Berger, Andrew; Dickey, Robert W; Jester, Edward; Yasumoto, Takeshi; Lewis, Richard; Hawryluk, Timothy; Hungerford, James

2014-01-01

Ciguatoxins are potent neurotoxins with a significant public health impact. Cytotoxicity assays have allowed the most sensitive means of detection of ciguatoxin-like activity without reliance on mouse bioassays and have been invaluable in studying outbreaks. An improvement of these cell-based assays is presented here in which rapid flow cytometric detection of ciguatoxins and saxitoxins is demonstrated using fluorescent voltage sensitive dyes. A depolarization response can be detected directly due to ciguatoxin alone; however, an approximate 1000-fold increase in sensitivity is observed in the presence of veratridine. These results demonstrate that flow cytometric assessment of ciguatoxins is possible at levels approaching the trace detection limits of our earlier cytotoxicity assays, however, with a significant reduction in analysis time. Preliminary results are also presented for detection of brevetoxins and for automation and throughput improvements to a previously described method for detecting saxitoxins in shellfish extracts.

Automated detection of fluorescent cells in in-resin fluorescence sections for integrated light and electron microscopy.

Science.gov (United States)

Delpiano, J; Pizarro, L; Peddie, C J; Jones, M L; Griffin, L D; Collinson, L M

2018-04-26

Integrated array tomography combines fluorescence and electron imaging of ultrathin sections in one microscope, and enables accurate high-resolution correlation of fluorescent proteins to cell organelles and membranes. Large numbers of serial sections can be imaged sequentially to produce aligned volumes from both imaging modalities, thus producing enormous amounts of data that must be handled and processed using novel techniques. Here, we present a scheme for automated detection of fluorescent cells within thin resin sections, which could then be used to drive automated electron image acquisition from target regions via 'smart tracking'. The aim of this work is to aid in optimization of the data acquisition process through automation, freeing the operator to work on other tasks and speeding up the process, while reducing data rates by only acquiring images from regions of interest. This new method is shown to be robust against noise and able to deal with regions of low fluorescence. © 2018 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society.

Automated colorimetric in situ hybridization (CISH) detection of immunoglobulin (Ig) light chain mRNA expression in plasma cell (PC) dyscrasias and non-Hodgkin lymphoma.

Science.gov (United States)

Beck, Rose C; Tubbs, Raymond R; Hussein, Mohamad; Pettay, James; Hsi, Eric D

2003-03-01

Immunohistochemistry (IHC) is frequently used to detect plasma cell (PC) or B cell monoclonality in histologic sections, but its interpretation is often confounded by background staining. We evaluated a new automated method for colorimetric in situ hybridization (CISH) detection of clonality in PC dyscrasias and small B cell lymphomas. Cases of PC dyscrasia included multiple myeloma (MM; 31 cases), plasmacytoma (seven cases), or amyloidosis (one case), while cases of lymphoma included small lymphocytic (three cases), marginal zone (four cases), lymphoplasmacytic (three cases), and mantle cell lymphomas (three cases). Tissue sections were stained for kappa and lambda light chains by IHC and for light chain mRNA by automated CISH using haptenated probes. Twenty-eight of 31 MM cases had detectable light chain restriction by IHC. Thirty of 31 MM cases demonstrated light chain restriction by CISH, including 2 cases with uninterpretable IHC and one case of nonsecretory myeloma, which was negative for light chains by IHC. Seven of 7 plasmacytoma cases had detectable light chain restriction by CISH, including one case of nonsecretory plasmacytoma in which IHC was noninformative. Automated CISH demonstrated monoclonality in 9 of 13 cases of B cell non-Hodgkin lymphoma and had a slightly higher sensitivity than IHC (6 of 13 cases), especially in cases of lymphoplasmacytic and marginal zone lymphoma. Overall, there were no discrepancies in light chain restriction results between IHC, CISH, or serum paraprotein analysis. Automated CISH is useful in detecting light chain expression in paraffin sections and appeared superior to IHC for light chain detection in PC dyscrasias and B cell non-Hodgkin lymphomas, predominantly due to lack of background staining.

Statistical techniques for automating the detection of anomalous performance in rotating machinery

International Nuclear Information System (INIS)

Piety, K.R.; Magette, T.E.

1979-01-01

The level of technology utilized in automated systems that monitor industrial rotating equipment and the potential of alternative surveillance methods are assessed. It is concluded that changes in surveillance methodology would upgrade ongoing programs and yet still be practical for implementation. An improved anomaly recognition methodology is formulated and implemented on a minicomputer system. The effectiveness of the monitoring system was evaluated in laboratory tests on a small rotor assembly, using vibrational signals from both displacement probes and accelerometers. Time and frequency domain descriptors are selected to compose an overall signature that characterizes the monitored equipment. Limits for normal operation of the rotor assembly are established automatically during an initial learning period. Thereafter, anomaly detection is accomplished by applying an approximate statistical test to each signature descriptor. As demonstrated over months of testing, this monitoring system is capable of detecting anomalous conditions while exhibiting a false alarm rate below 0.5%

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

Science.gov (United States)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network

Science.gov (United States)

Hiramatsu, Yuya; Muramatsu, Chisako; Kobayashi, Hironobu; Hara, Takeshi; Fujita, Hiroshi

2017-03-01

Breast cancer screening with mammography and ultrasonography is expected to improve sensitivity compared with mammography alone, especially for women with dense breast. An automated breast volume scanner (ABVS) provides the operator-independent whole breast data which facilitate double reading and comparison with past exams, contralateral breast, and multimodality images. However, large volumetric data in screening practice increase radiologists' workload. Therefore, our goal is to develop a computer-aided detection scheme of breast masses in ABVS data for assisting radiologists' diagnosis and comparison with mammographic findings. In this study, false positive (FP) reduction scheme using deep convolutional neural network (DCNN) was investigated. For training DCNN, true positive and FP samples were obtained from the result of our initial mass detection scheme using the vector convergence filter. Regions of interest including the detected regions were extracted from the multiplanar reconstraction slices. We investigated methods to select effective FP samples for training the DCNN. Based on the free response receiver operating characteristic analysis, simple random sampling from the entire candidates was most effective in this study. Using DCNN, the number of FPs could be reduced by 60%, while retaining 90% of true masses. The result indicates the potential usefulness of DCNN for FP reduction in automated mass detection on ABVS images.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

Science.gov (United States)

Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

2018-05-01

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

Detection of early behavioral markers of Huntington's disease in R6/2 mice employing an automated social home cage

DEFF Research Database (Denmark)

Rudenko, Olga; Tkach, Vadim; Berezin, Vladimir

2009-01-01

developed behavior screening system, the IntelliCage, allows automated testing of mouse behavior in the home cage employing individual recognition of animals living in social groups. The present study validates the ability of the IntelliCage system to detect behavioral and cognitive dysfunction in R6/2 mice......Huntington's disease (HD) is an autosomal-dominant neurodegenerative disorder, for which no known cure or effective treatment exists. To facilitate the search for new potential treatments of HD, an automated system for analyzing the behavior of transgenic HD mice is urgently needed. A recently...

Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home

Directory of Open Access Journals (Sweden)

Mau-Tsuen Yang

2013-12-01

Full Text Available Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second.

Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home

Science.gov (United States)

Yang, Mau-Tsuen; Chuang, Min-Wen

2013-01-01

Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM) classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second. PMID:24335727

Fall risk assessment and early-warning for toddler behaviors at home.

Science.gov (United States)

Yang, Mau-Tsuen; Chuang, Min-Wen

2013-12-10

Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM) classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second.

Using Akka Platform in Unidentified Falling Object Detection on the LHC.

CERN Document Server

Motesnitsalis, Evangelos

2013-01-01

During my participation in the CERN Summer Student Program 2013, I worked under the Technology Department of CERN and, more specifically, in the Machine Protection and Electrical Integrity (MPE) Group. The MPE Group supports LHC operation and maintains state‐of‐the art technology for magnet circuit protection and interlock systems for the present and future accelerators, magnet test facilities and CERN hosted experiments. Within this context, we developed an application that parallelizes the Unidentified Falling Object Detection Algorithm on the LHC Operational Data Analysis Software. For this reason, we used a JVM-based toolkit, named Akka, which parallelizes the execution by creating a number of actors that run simultaneously. The results of the new approach are presented on the last part of this report. They tend to be quite interesting and promising as we managed to reduce the execution time of the analysis by a factor of 10 on a local machine and the first attempts to execute the program on a cluster...

Low-power operation of a barometric pressure sensor for use in an automatic fall detector.

Science.gov (United States)

Wei Lu; Changhong Wang; Stevens, Michael C; Redmond, Stephen J; Lovell, Nigel H

2016-08-01

The use of a barometric pressure sensor in a wearable fall detector has been shown to improve the detection accuracy by determining the altitude change associated with the fall event. However, the barometer is a high-power-consuming sensor. This paper proposes a fall detection approach using a hermetically sealed and waterproof enclosure incorporating a small window covered by a semi-permeable membrane (SPM) to delay the equilibrium of internal and external pressures. This feature can be utilized to limit the time the barometer is powered but still capturing critical pressure information to discriminate fall and non-fall events. The proposed fall detection system is evaluated with an existing data set of simulated fall and activities of daily living in which the barometric pressure data are delayed using a mathematical model of the enclosure and SPM assembly. Also, a benchtop test is performed to estimate the power and battery life. The proposed fall detection system achieves 94.0% sensitivity and 90.0% specificity with an estimated battery life of 995.7 days.

Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT.

Science.gov (United States)

Burns, Joseph E; Yao, Jianhua; Muñoz, Hector; Summers, Ronald M

2016-01-01

To design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT). This retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14-88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed. Training set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]). The fully automated computer system detects and anatomically localizes vertebral body fractures in the thoracic and lumbar spine on CT images with a

Automated detection of malaria pigment: feasibility for malaria diagnosing in an area with seasonal malaria in northern Namibia

NARCIS (Netherlands)

de Langen, Adrianus J.; van Dillen, Jeroen; de Witte, Piet; Mucheto, Samson; Nagelkerke, Nico; Kager, Piet

2006-01-01

OBJECTIVE: To evaluate the feasibility of automated malaria detection with the Cell-Dyn 3700 (Abbott Diagnostics, Santa Clara, CA, USA) haematology analyser for diagnosing malaria in northern Namibia. METHODS: From April to June 2003, all patients with a positive blood smear result and a subset of

[Development of an automated processing method to detect coronary motion for coronary magnetic resonance angiography].

Science.gov (United States)

Asou, Hiroya; Imada, N; Sato, T

2010-06-20

On coronary MR angiography (CMRA), cardiac motions worsen the image quality. To improve the image quality, detection of cardiac especially for individual coronary motion is very important. Usually, scan delay and duration were determined manually by the operator. We developed a new evaluation method to calculate static time of individual coronary artery. At first, coronary cine MRI was taken at the level of about 3 cm below the aortic valve (80 images/R-R). Chronological change of the signals were evaluated with Fourier transformation of each pixel of the images were done. Noise reduction with subtraction process and extraction process were done. To extract higher motion such as coronary arteries, morphological filter process and labeling process were added. Using these imaging processes, individual coronary motion was extracted and individual coronary static time was calculated automatically. We compared the images with ordinary manual method and new automated method in 10 healthy volunteers. Coronary static times were calculated with our method. Calculated coronary static time was shorter than that of ordinary manual method. And scan time became about 10% longer than that of ordinary method. Image qualities were improved in our method. Our automated detection method for coronary static time with chronological Fourier transformation has a potential to improve the image quality of CMRA and easy processing.

An Architecture for Automated Fire Detection Early Warning System Based on Geoprocessing Service Composition

Science.gov (United States)

Samadzadegan, F.; Saber, M.; Zahmatkesh, H.; Joze Ghazi Khanlou, H.

2013-09-01

Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA) to take advantages of workflow of services provides an efficient, flexible and reliable implementations to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS) has guided geospatial data processing in a Service Oriented Architecture (SOA) platform to become a widely accepted solution for processing remotely sensed data on the web. This paper presents an architecture design based on OGC web services for automated workflow for acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks for an automated fire detection early warning system are represented using web-based processing of remote sensing imageries utilizing MODIS data. A composition of WPS processes is proposed as a WPS service to extract fire events from MODIS data. Subsequently, the paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. A GeoPortal client with open-source software was developed to manage data, metadata, processes, and authorities. Investigating feasibility and benefits of proposed framework shows that this framework can be used for wide area of geospatial applications specially disaster management and environmental monitoring.

AN ARCHITECTURE FOR AUTOMATED FIRE DETECTION EARLY WARNING SYSTEM BASED ON GEOPROCESSING SERVICE COMPOSITION

Directory of Open Access Journals (Sweden)

F. Samadzadegan

2013-09-01

Full Text Available Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA to take advantages of workflow of services provides an efficient, flexible and reliable implementations to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS has guided geospatial data processing in a Service Oriented Architecture (SOA platform to become a widely accepted solution for processing remotely sensed data on the web. This paper presents an architecture design based on OGC web services for automated workflow for acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks for an automated fire detection early warning system are represented using web-based processing of remote sensing imageries utilizing MODIS data. A composition of WPS processes is proposed as a WPS service to extract fire events from MODIS data. Subsequently, the paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. A GeoPortal client with open-source software was developed to manage data, metadata, processes, and authorities. Investigating feasibility and benefits of proposed framework shows that this framework can be used for wide area of geospatial applications specially disaster management and environmental monitoring.

Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

International Nuclear Information System (INIS)

Wang Xingwei; Zheng Bin; Wood, Marc; Li Shibo; Chen Wei; Liu Hong

2005-01-01

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed

Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

Energy Technology Data Exchange (ETDEWEB)

Wang Xingwei [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States); Zheng Bin [Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA (United States); Wood, Marc [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States); Li Shibo [Department of Pediatrics, University of Oklahoma Medical Center, Oklahoma City, OK (United States); Chen Wei [Department of Physics and Engineering, University of Central Oklahoma, Edmond, OK (United States); Liu Hong [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States)

2005-08-07

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed.

Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: feasibility and prospects.

Science.gov (United States)

Muszynski, C; Happillon, T; Azudin, K; Tylcz, J-B; Istrate, D; Marque, C

2018-05-08

Preterm birth is a major public health problem in developed countries. In this context, we have conducted research into outpatient monitoring of uterine electrical activity in women at risk of preterm delivery. The objective of this preliminary study was to perform automated detection of uterine contractions (without human intervention or tocographic signal, TOCO) by processing the EHG recorded on the abdomen of pregnant women. The feasibility and accuracy of uterine contraction detection based on EHG processing were tested and compared to expert decision using external tocodynamometry (TOCO) . The study protocol was approved by local Ethics Committees under numbers ID-RCB 2016-A00663-48 for France and VSN 02-0006-V2 for Iceland. Two populations of women were included (threatened preterm birth and labour) in order to test our system of recognition of the various types of uterine contractions. EHG signal acquisition was performed according to a standardized protocol to ensure optimal reproducibility of EHG recordings. A system of 18 Ag/AgCl surface electrodes was used by placing 16 recording electrodes between the woman's pubis and umbilicus according to a 4 × 4 matrix. TOCO was recorded simultaneously with EHG recording. EHG signals were analysed in real-time by calculation of the nonlinear correlation coefficient H 2 . A curve representing the number of correlated pairs of signals according to the value of H 2 calculated between bipolar signals was then plotted. High values of H 2 indicated the presence of an event that may correspond to a contraction. Two tests were performed after detection of an event (fusion and elimination of certain events) in order to increase the contraction detection rate. The EHG database contained 51 recordings from pregnant women, with a total of 501 contractions previously labelled by analysis of the corresponding tocographic recording. The percentage recognitions obtained by application of the method based on coefficient H 2 was

Automated valve fault detection based on acoustic emission parameters and support vector machine

Directory of Open Access Journals (Sweden)

Salah M. Ali

2018-03-01

Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

Assessment of hearing threshold in adults with hearing loss using an automated system of cortical auditory evoked potential detection

Directory of Open Access Journals (Sweden)

Alessandra Spada Durante

Full Text Available Abstract Introduction: The use of hearing aids by individuals with hearing loss brings a better quality of life. Access to and benefit from these devices may be compromised in patients who present difficulties or limitations in traditional behavioral audiological evaluation, such as newborns and small children, individuals with auditory neuropathy spectrum, autism, and intellectual deficits, and in adults and the elderly with dementia. These populations (or individuals are unable to undergo a behavioral assessment, and generate a growing demand for objective methods to assess hearing. Cortical auditory evoked potentials have been used for decades to estimate hearing thresholds. Current technological advances have lead to the development of equipment that allows their clinical use, with features that enable greater accuracy, sensitivity, and specificity, and the possibility of automated detection, analysis, and recording of cortical responses. Objective: To determine and correlate behavioral auditory thresholds with cortical auditory thresholds obtained from an automated response analysis technique. Methods: The study included 52 adults, divided into two groups: 21 adults with moderate to severe hearing loss (study group; and 31 adults with normal hearing (control group. An automated system of detection, analysis, and recording of cortical responses (HEARLab® was used to record the behavioral and cortical thresholds. The subjects remained awake in an acoustically treated environment. Altogether, 150 tone bursts at 500, 1000, 2000, and 4000 Hz were presented through insert earphones in descending-ascending intensity. The lowest level at which the subject detected the sound stimulus was defined as the behavioral (hearing threshold (BT. The lowest level at which a cortical response was observed was defined as the cortical electrophysiological threshold. These two responses were correlated using linear regression. Results: The cortical

Assessment of hearing threshold in adults with hearing loss using an automated system of cortical auditory evoked potential detection.

Science.gov (United States)

Durante, Alessandra Spada; Wieselberg, Margarita Bernal; Roque, Nayara; Carvalho, Sheila; Pucci, Beatriz; Gudayol, Nicolly; de Almeida, Kátia

The use of hearing aids by individuals with hearing loss brings a better quality of life. Access to and benefit from these devices may be compromised in patients who present difficulties or limitations in traditional behavioral audiological evaluation, such as newborns and small children, individuals with auditory neuropathy spectrum, autism, and intellectual deficits, and in adults and the elderly with dementia. These populations (or individuals) are unable to undergo a behavioral assessment, and generate a growing demand for objective methods to assess hearing. Cortical auditory evoked potentials have been used for decades to estimate hearing thresholds. Current technological advances have lead to the development of equipment that allows their clinical use, with features that enable greater accuracy, sensitivity, and specificity, and the possibility of automated detection, analysis, and recording of cortical responses. To determine and correlate behavioral auditory thresholds with cortical auditory thresholds obtained from an automated response analysis technique. The study included 52 adults, divided into two groups: 21 adults with moderate to severe hearing loss (study group); and 31 adults with normal hearing (control group). An automated system of detection, analysis, and recording of cortical responses (HEARLab ® ) was used to record the behavioral and cortical thresholds. The subjects remained awake in an acoustically treated environment. Altogether, 150 tone bursts at 500, 1000, 2000, and 4000Hz were presented through insert earphones in descending-ascending intensity. The lowest level at which the subject detected the sound stimulus was defined as the behavioral (hearing) threshold (BT). The lowest level at which a cortical response was observed was defined as the cortical electrophysiological threshold. These two responses were correlated using linear regression. The cortical electrophysiological threshold was, on average, 7.8dB higher than the

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.

Science.gov (United States)

Tu, Yiheng; Hung, Yeung Sam; Hu, Li; Huang, Gan; Hu, Yong; Zhang, Zhiguo

2014-12-01

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

Science.gov (United States)

Niemeijer, Meindert; van Ginneken, Bram; Russell, Stephen R; Suttorp-Schulten, Maria S A; Abràmoff, Michael D

2007-05-01

To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagnosis database (nonmydriatic camera, two-field photography): 100 with previously diagnosed bright lesions and 200 without. A machine learning computer program was developed that can identify and differentiate among drusen, (hard) exudates, and cotton-wool spots. A human expert standard for the 300 images was obtained by consensus annotation by two retinal specialists. Sensitivities and specificities of the annotations on the 300 images by the automated system and a third retinal specialist were determined. The system achieved an area under the receiver operating characteristic (ROC) curve of 0.95 and sensitivity/specificity pairs of 0.95/0.88 for the detection of bright lesions of any type, and 0.95/0.86, 0.70/0.93, and 0.77/0.88 for the detection of exudates, cotton-wool spots, and drusen, respectively. The third retinal specialist achieved pairs of 0.95/0.74 for bright lesions and 0.90/0.98, 0.87/0.98, and 0.92/0.79 per lesion type. A machine learning-based, automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed and approaches the performance level of retinal experts. If the machine learning can be improved with additional training data sets, it may be useful for detecting clinically important bright lesions, enhancing early diagnosis, and reducing visual loss in patients with diabetes.

Automated Vehicle Monitoring System

OpenAIRE

Wibowo, Agustinus Deddy Arief; Heriansyah, Rudi

2014-01-01

An automated vehicle monitoring system is proposed in this paper. The surveillance system is based on image processing techniques such as background subtraction, colour balancing, chain code based shape detection, and blob. The proposed system will detect any human's head as appeared at the side mirrors. The detected head will be tracked and recorded for further action.

Using microwave Doppler radar in automated manufacturing applications

Science.gov (United States)

Smith, Gregory C.

Since the beginning of the Industrial Revolution, manufacturers worldwide have used automation to improve productivity, gain market share, and meet growing or changing consumer demand for manufactured products. To stimulate further industrial productivity, manufacturers need more advanced automation technologies: "smart" part handling systems, automated assembly machines, CNC machine tools, and industrial robots that use new sensor technologies, advanced control systems, and intelligent decision-making algorithms to "see," "hear," "feel," and "think" at the levels needed to handle complex manufacturing tasks without human intervention. The investigator's dissertation offers three methods that could help make "smart" CNC machine tools and industrial robots possible: (1) A method for detecting acoustic emission using a microwave Doppler radar detector, (2) A method for detecting tool wear on a CNC lathe using a Doppler radar detector, and (3) An online non-contact method for detecting industrial robot position errors using a microwave Doppler radar motion detector. The dissertation studies indicate that microwave Doppler radar could be quite useful in automated manufacturing applications. In particular, the methods developed may help solve two difficult problems that hinder further progress in automating manufacturing processes: (1) Automating metal-cutting operations on CNC machine tools by providing a reliable non-contact method for detecting tool wear, and (2) Fully automating robotic manufacturing tasks by providing a reliable low-cost non-contact method for detecting on-line position errors. In addition, the studies offer a general non-contact method for detecting acoustic emission that may be useful in many other manufacturing and non-manufacturing areas, as well (e.g., monitoring and nondestructively testing structures, materials, manufacturing processes, and devices). By advancing the state of the art in manufacturing automation, the studies may help

Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd.

Science.gov (United States)

Irshad, H; Montaser-Kouhsari, L; Waltz, G; Bucur, O; Nowak, J A; Dong, F; Knoblauch, N W; Beck, A H

2015-01-01

The development of tools in computational pathology to assist physicians and biomedical scientists in the diagnosis of disease requires access to high-quality annotated images for algorithm learning and evaluation. Generating high-quality expert-derived annotations is time-consuming and expensive. We explore the use of crowdsourcing for rapidly obtaining annotations for two core tasks in com- putational pathology: nucleus detection and nucleus segmentation. We designed and implemented crowdsourcing experiments using the CrowdFlower platform, which provides access to a large set of labor channel partners that accesses and manages millions of contributors worldwide. We obtained annotations from four types of annotators and compared concordance across these groups. We obtained: crowdsourced annotations for nucleus detection and segmentation on a total of 810 images; annotations using automated methods on 810 images; annotations from research fellows for detection and segmentation on 477 and 455 images, respectively; and expert pathologist-derived annotations for detection and segmentation on 80 and 63 images, respectively. For the crowdsourced annotations, we evaluated performance across a range of contributor skill levels (1, 2, or 3). The crowdsourced annotations (4,860 images in total) were completed in only a fraction of the time and cost required for obtaining annotations using traditional methods. For the nucleus detection task, the research fellow-derived annotations showed the strongest concordance with the expert pathologist- derived annotations (F-M =93.68%), followed by the crowd-sourced contributor levels 1,2, and 3 and the automated method, which showed relatively similar performance (F-M = 87.84%, 88.49%, 87.26%, and 86.99%, respectively). For the nucleus segmentation task, the crowdsourced contributor level 3-derived annotations, research fellow-derived annotations, and automated method showed the strongest concordance with the expert pathologist

Automated method for measuring the extent of selective logging damage with airborne LiDAR data

Science.gov (United States)

Melendy, L.; Hagen, S. C.; Sullivan, F. B.; Pearson, T. R. H.; Walker, S. M.; Ellis, P.; Kustiyo; Sambodo, Ari Katmoko; Roswintiarti, O.; Hanson, M. A.; Klassen, A. W.; Palace, M. W.; Braswell, B. H.; Delgado, G. M.

2018-05-01

Selective logging has an impact on the global carbon cycle, as well as on the forest micro-climate, and longer-term changes in erosion, soil and nutrient cycling, and fire susceptibility. Our ability to quantify these impacts is dependent on methods and tools that accurately identify the extent and features of logging activity. LiDAR-based measurements of these features offers significant promise. Here, we present a set of algorithms for automated detection and mapping of critical features associated with logging - roads/decks, skid trails, and gaps - using commercial airborne LiDAR data as input. The automated algorithm was applied to commercial LiDAR data collected over two logging concessions in Kalimantan, Indonesia in 2014. The algorithm results were compared to measurements of the logging features collected in the field soon after logging was complete. The automated algorithm-mapped road/deck and skid trail features match closely with features measured in the field, with agreement levels ranging from 69% to 99% when adjusting for GPS location error. The algorithm performed most poorly with gaps, which, by their nature, are variable due to the unpredictable impact of tree fall versus the linear and regular features directly created by mechanical means. Overall, the automated algorithm performs well and offers significant promise as a generalizable tool useful to efficiently and accurately capture the effects of selective logging, including the potential to distinguish reduced impact logging from conventional logging.

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

Science.gov (United States)

Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

2014-04-01

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Approach to Fall in Elderly Population

Directory of Open Access Journals (Sweden)

Mehmet Ilkin Naharci

2009-10-01

Full Text Available Falls are one of the geriatric syndromes which occur commonly and significantly increase morbidity and mortality rates in elderly. The incidence of falls increases with age. Falls usually occur when impairments in cognitive, behavioral, and executive function begin. The incidence of fall is between 30 and 40 percent of community-dwelling people and approximately 50 percent of individuals in the long-term care setting over the age of 65 years. Fracture (hip, arm, wrist, pelvis, head trauma or major lacerations, as defined serious wounding, occur 10-25% of elderly cases. Fall is overlooked in clinical examination due to various reasons; the patient never mentions the event to a doctor; there is no injury at the time of the fall; the doctor fails to ask the patient about a history of falls; or either doctor or patient erroneously believes that falls are an inevitable part of the aging process. Elderly give not usually any self-information about fall, for this reason, all older patients should be asked at least once per year about falls and should be assessed in terms of balance and gait disorders. There are many distinct causes for falls in old people. Falls in older individuals occur when a threat to the normal homeostatic mechanisms that maintain postural stability is superimposed on underlying age-related declines in balance, ambulation, and cardiovascular function. This factor may be an acute illness (eg, fever, water loss, arrhythmia, a new medication, an environmental stress (eg, unfamiliar surrounding, or an unsafe walking surface. The elderly person can not cope with happened additional stress. To prevent and decrease the frequency of falls, effective approaches are medical interventions, environmental modifications, education-exercise programs, and assisted device. Detection and amelioration of risk factors can significantly reduce the rate of future falls. The assessment of fall, causing mobility restriction, use of nursing home, and

Automated detection of exudates and macula for grading of diabetic macular edema.

Science.gov (United States)

Akram, M Usman; Tariq, Anam; Khan, Shoab A; Javed, M Younus

2014-04-01

Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Three essays on major trends in a slow clockspeed industry : the case of industrial automation

OpenAIRE

Tunkelo, T.

2014-01-01

The motivation for this research initiated from the abrupt rise and fall of minicomputers which were initially used both for industrial automation and business applications due to their significantly lower cost than their predecessors, the mainframes. Later industrial automation developed its own vertically integrated hardware and software to address the application needs of uninterrupted operations, real-time control and resilience to harsh environmental conditions. This has led to the creat...

A thesis on the Development of an Automated SWIFT Edge Detection Algorithm

Energy Technology Data Exchange (ETDEWEB)

Trujillo, Christopher J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-07-28

Throughout the world, scientists and engineers such as those at Los Alamos National Laboratory, perform research and testing unique only to applications aimed towards advancing technology, and understanding the nature of materials. With this testing, comes a need for advanced methods of data acquisition and most importantly, a means of analyzing and extracting the necessary information from such acquired data. In this thesis, I aim to produce an automated method implementing advanced image processing techniques and tools to analyze SWIFT image datasets for Detonator Technology at Los Alamos National Laboratory. Such an effective method for edge detection and point extraction can prove to be advantageous in analyzing such unique datasets and provide for consistency in producing results.

Detection of Operator Performance Breakdown as an Automation Triggering Mechanism

Science.gov (United States)

Yoo, Hyo-Sang; Lee, Paul U.; Landry, Steven J.

2015-01-01

Performance breakdown (PB) has been anecdotally described as a state where the human operator "loses control of context" and "cannot maintain required task performance." Preventing such a decline in performance is critical to assure the safety and reliability of human-integrated systems, and therefore PB could be useful as a point at which automation can be applied to support human performance. However, PB has never been scientifically defined or empirically demonstrated. Moreover, there is no validated objective way of detecting such a state or the transition to that state. The purpose of this work is: 1) to empirically demonstrate a PB state, and 2) to develop an objective way of detecting such a state. This paper defines PB and proposes an objective method for its detection. A human-in-the-loop study was conducted: 1) to demonstrate PB by increasing workload until the subject reported being in a state of PB, and 2) to identify possible parameters of a detection method for objectively identifying the subjectively-reported PB point, and 3) to determine if the parameters are idiosyncratic to an individual/context or are more generally applicable. In the experiment, fifteen participants were asked to manage three concurrent tasks (one primary and two secondary) for 18 minutes. The difficulty of the primary task was manipulated over time to induce PB while the difficulty of the secondary tasks remained static. The participants' task performance data was collected. Three hypotheses were constructed: 1) increasing workload will induce subjectively-identified PB, 2) there exists criteria that identifies the threshold parameters that best matches the subjectively-identified PB point, and 3) the criteria for choosing the threshold parameters is consistent across individuals. The results show that increasing workload can induce subjectively-identified PB, although it might not be generalizable-only 12 out of 15 participants declared PB. The PB detection method based on

A multiplex reverse transcription PCR and automated electronic microarray assay for detection and differentiation of seven viruses affecting swine.

Science.gov (United States)

Erickson, A; Fisher, M; Furukawa-Stoffer, T; Ambagala, A; Hodko, D; Pasick, J; King, D P; Nfon, C; Ortega Polo, R; Lung, O

2018-04-01

Microarray technology can be useful for pathogen detection as it allows simultaneous interrogation of the presence or absence of a large number of genetic signatures. However, most microarray assays are labour-intensive and time-consuming to perform. This study describes the development and initial evaluation of a multiplex reverse transcription (RT)-PCR and novel accompanying automated electronic microarray assay for simultaneous detection and differentiation of seven important viruses that affect swine (foot-and-mouth disease virus [FMDV], swine vesicular disease virus [SVDV], vesicular exanthema of swine virus [VESV], African swine fever virus [ASFV], classical swine fever virus [CSFV], porcine respiratory and reproductive syndrome virus [PRRSV] and porcine circovirus type 2 [PCV2]). The novel electronic microarray assay utilizes a single, user-friendly instrument that integrates and automates capture probe printing, hybridization, washing and reporting on a disposable electronic microarray cartridge with 400 features. This assay accurately detected and identified a total of 68 isolates of the seven targeted virus species including 23 samples of FMDV, representing all seven serotypes, and 10 CSFV strains, representing all three genotypes. The assay successfully detected viruses in clinical samples from the field, experimentally infected animals (as early as 1 day post-infection (dpi) for FMDV and SVDV, 4 dpi for ASFV, 5 dpi for CSFV), as well as in biological material that were spiked with target viruses. The limit of detection was 10 copies/μl for ASFV, PCV2 and PRRSV, 100 copies/μl for SVDV, CSFV, VESV and 1,000 copies/μl for FMDV. The electronic microarray component had reduced analytical sensitivity for several of the target viruses when compared with the multiplex RT-PCR. The integration of capture probe printing allows custom onsite array printing as needed, while electrophoretically driven hybridization generates results faster than conventional

Automated detection of neovascularization for proliferative diabetic retinopathy screening.

Science.gov (United States)

Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

2016-08-01

Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.

Detection of compensatory balance responses using wearable electromyography sensors for fall-risk assessment.

Science.gov (United States)

Nouredanesh, Mina; Kukreja, Sunil L; Tung, James

2016-08-01

Loss of balance is prevalent in older adults and populations with gait and balance impairments. The present paper aims to develop a method to automatically distinguish compensatory balance responses (CBRs) from normal gait, based on activity patterns of muscles involved in maintaining balance. In this study, subjects were perturbed by lateral pushes while walking and surface electromyography (sEMG) signals were recorded from four muscles in their right leg. To extract sEMG time domain features, several filtering characteristics and segmentation approaches are examined. The performance of three classification methods, i.e., k-nearest neighbor, support vector machines, and random forests, were investigated for accurate detection of CBRs. Our results show that features extracted in the 50-200Hz band, segmented using peak sEMG amplitudes, and a random forest classifier detected CBRs with an accuracy of 92.35%. Moreover, our results support the important role of biceps femoris and rectus femoris muscles in stabilization and consequently discerning CBRs. This study contributes towards the development of wearable sensor systems to accurately and reliably monitor gait and balance control behavior in at-home settings (unsupervised conditions), over long periods of time, towards personalized fall risk assessment tools.

Flexible Method for the Automated Offline-Detection of Artifacts in Multi-Channel Electroencephalogram Recordings

DEFF Research Database (Denmark)

Waser, Markus; Garn, Heinrich; Benke, Thomas

2017-01-01

. However, these preprocessing steps do not allow for complete artifact correction. We propose a method for the automated offline-detection of remaining artifacts after preprocessing in multi-channel EEG recordings. In contrast to existing methods it requires neither adaptive parameters varying between...... recordings nor a topography template. It is suited for short EEG segments and is flexible with regard to target applications. The algorithm was developed and tested on 60 clinical EEG samples of 20 seconds each that were recorded both in resting state and during cognitive activation to gain a realistic...

Automated baseline change detection - Phases 1 and 2. Final report

International Nuclear Information System (INIS)

Byler, E.

1997-01-01

The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. The ABCD image processing software was installed on a robotic vehicle developed under a related DOE/FETC contract DE-AC21-92MC29112 Intelligent Mobile Sensor System (IMSS) and integrated with the electronics and software. This vehicle was designed especially to navigate in DOE Waste Storage Facilities. Initial system testing was performed at Fernald in June 1996. After some further development and more extensive integration the prototype integrated system was installed and tested at the Radioactive Waste Management Facility (RWMC) at INEEL beginning in April 1997 through the present (November 1997). The integrated system, composed of ABCD imaging software and IMSS mobility base, is called MISS EVE (Mobile Intelligent Sensor System--Environmental Validation Expert). Evaluation of the integrated system in RWMC Building 628, containing approximately 10,000 drums, demonstrated an easy to use system with the ability to properly navigate through the facility, image all the defined drums, and process the results into a report delivered to the operator on a GUI interface and on hard copy. Further work is needed to make the brassboard system more operationally robust

Understanding human management of automation errors

Science.gov (United States)

McBride, Sara E.; Rogers, Wendy A.; Fisk, Arthur D.

2013-01-01

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance. PMID:25383042

Fast-FISH Detection and Semi-Automated Image Analysis of Numerical Chromosome Aberrations in Hematological Malignancies

Directory of Open Access Journals (Sweden)

Arif Esa

1998-01-01

Full Text Available A new fluorescence in situ hybridization (FISH technique called Fast-FISH in combination with semi-automated image analysis was applied to detect numerical aberrations of chromosomes 8 and 12 in interphase nuclei of peripheral blood lymphocytes and bone marrow cells from patients with acute myelogenous leukemia (AML and chronic lymphocytic leukemia (CLL. Commercially available α-satellite DNA probes specific for the centromere regions of chromosome 8 and chromosome 12, respectively, were used. After application of the Fast-FISH protocol, the microscopic images of the fluorescence-labelled cell nuclei were recorded by the true color CCD camera Kappa CF 15 MC and evaluated quantitatively by computer analysis on a PC. These results were compared to results obtained from the same type of specimens using the same analysis system but with a standard FISH protocol. In addition, automated spot counting after both FISH techniques was compared to visual spot counting after standard FISH. A total number of about 3,000 cell nuclei was evaluated. For quantitative brightness parameters, a good correlation between standard FISH labelling and Fast-FISH was found. Automated spot counting after Fast-FISH coincided within a few percent to automated and visual spot counting after standard FISH. The examples shown indicate the reliability and reproducibility of Fast-FISH and its potential for automatized interphase cell diagnostics of numerical chromosome aberrations. Since the Fast-FISH technique requires a hybridization time as low as 1/20 of established standard FISH techniques, omitting most of the time consuming working steps in the protocol, it may contribute considerably to clinical diagnostics. This may especially be interesting in cases where an accurate result is required within a few hours.

Determination of Proton dose distal fall-off location by detecting right-angled prompt gamma rays

International Nuclear Information System (INIS)

Seo, Kyu Seok

2006-02-01

The proton beam has a unique advantage over the electron and photon beams in that it can give very high radiation dose to the tumor volume while effectively sparing the neighboring healthy tissue and organs. The number of proton therapy facility is very rapidly increasing in the world. And now the 230 MeV cyclotron facility for proton therapy is constructing at National Cancer Center, this facility until 2006. The distal fall-off location of proton beam is simply calculated by analytical method, but this method has many uncertain when anatomical structure is very complicated. It is very important to know the exact position of the proton beam distal fall-off, or beam range, in the patient's body for both the safety of the patient and the effectiveness of the treatment itself. In 2003, Stichelbaut and Jongen reported the possibility of using the right-angled prompt gamma rays, which are emitted at 90 .deg. from the incident proton beam direction, to determine the position of the proton beam distal fall-off. They studied the interactions of the protons and other secondary particles in a water phantom and concluded that there is a correlation between the position of the distal fall-off and the distribution of the right-angled prompt gamma rays. We have recently designed a prompt gamma scanning system to measure the proton range in situ by using Monte Carlo technique employing MCNPX, FLUKA, and Sabrina TM . The prompt gamma scanning system was designed to measure only the right-angled prompt gamma rays passing through a narrow collimation hole in order to correlate the position with the dose distribution. The collimation part of the scanning system, which has been constructed to measure the gamma rays at 70 MeV of proton energy, is made of a set of paraffin, boron carbide, and lead layers to shield the high-energy neutrons and secondary photons. After the different proton energies and SOBP beam widths are irradiated at the water phantom. we detected prompt gamma at 5 cm

21 CFR 864.9300 - Automated Coombs test systems.

Science.gov (United States)

2010-04-01

... Blood and Blood Products § 864.9300 Automated Coombs test systems. (a) Identification. An automated Coombs test system is a device used to detect and identify antibodies in patient sera or antibodies bound... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated Coombs test systems. 864.9300 Section...

Fall Risk Index predicts functional decline regardless of fall experiences among community-dwelling elderly.

Science.gov (United States)

Ishimoto, Yasuko; Wada, Taizo; Kasahara, Yoriko; Kimura, Yumi; Fukutomi, Eriko; Chen, Wenling; Hirosaki, Mayumi; Nakatsuka, Masahiro; Fujisawa, Michiko; Sakamoto, Ryota; Ishine, Masayuki; Okumiya, Kiyohito; Otsuka, Kuniaki; Matsubayashi, Kozo

2012-10-01

The 21-item Fall Risk Index (FRI-21) has been used to detect elderly persons at risk for falls. The aim of this longitudinal study was to evaluate the FRI-21 as a predictor of decline in basic activities of daily living (BADL) among Japanese community-dwelling elderly persons independent of fall risk. The study population consisted of 518 elderly participants aged 65 years and older who were BADL independent at baseline in Tosa, Japan. We examined risk factors for BADL decline from 2008 to 2009 by multiple logistic regression analysis on the FRI-21 and other functional status measures in all participants. We carried out the same analysis in selected participants who had no experience of falls to remove the effect of falls. A total of 45 of 518 participants showed decline in BADL within 1 year. Multivariate logistic regression analysis showed that age (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.05-1.20), FRI-21 ≥ 10 (OR 3.81, 95% CI 1.49-9.27), intellectual activity dependence (OR 3.25, 95% CI 1.42-7.44) and history of osteoarthropathy (OR 3.17, 95% CI 1.40-7.21) were significant independent risk factors for BADL decline within 1 year. FRI-21 ≥ 10 and intellectual activity dependence (≤ 3) remained significant predictors, even in selected non-fallers. FRI-21 ≥ 10 and intellectual activity dependence were significant predictive factors of BADL decline, regardless of fall experience, after adjustment for confounding variables. The FRI-21 is a brief, useful tool not only for predicting falls, but also future decline in functional ability in community-dwelling elderly persons. © 2012 Japan Geriatrics Society.

High throughput detection of Coxiella burnetii by real-time PCR with internal control system and automated DNA preparation

Directory of Open Access Journals (Sweden)

Kramme Stefanie

2008-05-01

Full Text Available Abstract Background Coxiella burnetii is the causative agent of Q-fever, a widespread zoonosis. Due to its high environmental stability and infectivity it is regarded as a category B biological weapon agent. In domestic animals infection remains either asymptomatic or presents as infertility or abortion. Clinical presentation in humans can range from mild flu-like illness to acute pneumonia and hepatitis. Endocarditis represents the most common form of chronic Q-fever. In humans serology is the gold standard for diagnosis but is inadequate for early case detection. In order to serve as a diagnostic tool in an eventual biological weapon attack or in local epidemics we developed a real-time 5'nuclease based PCR assay with an internal control system. To facilitate high-throughput an automated extraction procedure was evaluated. Results To determine the minimum number of copies that are detectable at 95% chance probit analysis was used. Limit of detection in blood was 2,881 copies/ml [95%CI, 2,188–4,745 copies/ml] with a manual extraction procedure and 4,235 copies/ml [95%CI, 3,143–7,428 copies/ml] with a fully automated extraction procedure, respectively. To demonstrate clinical application a total of 72 specimens of animal origin were compared with respect to manual and automated extraction. A strong correlation between both methods was observed rendering both methods suitable. Testing of 247 follow up specimens of animal origin from a local Q-fever epidemic rendered real-time PCR more sensitive than conventional PCR. Conclusion A sensitive and thoroughly evaluated real-time PCR was established. Its high-throughput mode may show a useful approach to rapidly screen samples in local outbreaks for other organisms relevant for humans or animals. Compared to a conventional PCR assay sensitivity of real-time PCR was higher after testing samples from a local Q-fever outbreak.

Automated analysis of retinal images for detection of referable diabetic retinopathy.

Science.gov (United States)

Abràmoff, Michael D; Folk, James C; Han, Dennis P; Walker, Jonathan D; Williams, David F; Russell, Stephen R; Massin, Pascale; Cochener, Beatrice; Gain, Philippe; Tang, Li; Lamard, Mathieu; Moga, Daniela C; Quellec, Gwénolé; Niemeijer, Meindert

2013-03-01

The diagnostic accuracy of computer detection programs has been reported to be comparable to that of specialists and expert readers, but no computer detection programs have been validated in an independent cohort using an internationally recognized diabetic retinopathy (DR) standard. To determine the sensitivity and specificity of the Iowa Detection Program (IDP) to detect referable diabetic retinopathy (RDR). In primary care DR clinics in France, from January 1, 2005, through December 31, 2010, patients were photographed consecutively, and retinal color images were graded for retinopathy severity according to the International Clinical Diabetic Retinopathy scale and macular edema by 3 masked independent retinal specialists and regraded with adjudication until consensus. The IDP analyzed the same images at a predetermined and fixed set point. We defined RDR as more than mild nonproliferative retinopathy and/or macular edema. A total of 874 people with diabetes at risk for DR. Sensitivity and specificity of the IDP to detect RDR, area under the receiver operating characteristic curve, sensitivity and specificity of the retinal specialists' readings, and mean interobserver difference (κ). The RDR prevalence was 21.7% (95% CI, 19.0%-24.5%). The IDP sensitivity was 96.8% (95% CI, 94.4%-99.3%) and specificity was 59.4% (95% CI, 55.7%-63.0%), corresponding to 6 of 874 false-negative results (none met treatment criteria). The area under the receiver operating characteristic curve was 0.937 (95% CI, 0.916-0.959). Before adjudication and consensus, the sensitivity/specificity of the retinal specialists were 0.80/0.98, 0.71/1.00, and 0.91/0.95, and the mean intergrader κ was 0.822. The IDP has high sensitivity and specificity to detect RDR. Computer analysis of retinal photographs for DR and automated detection of RDR can be implemented safely into the DR screening pipeline, potentially improving access to screening and health care productivity and reducing visual loss

Transitioning to future air traffic management: effects of imperfect automation on controller attention and performance.

Science.gov (United States)

Rovira, Ericka; Parasuraman, Raja

2010-06-01

This study examined whether benefits of conflict probe automation would occur in a future air traffic scenario in which air traffic service providers (ATSPs) are not directly responsible for freely maneuvering aircraft but are controlling other nonequipped aircraft (mixed-equipage environment). The objective was to examine how the type of automation imperfection (miss vs. false alarm) affects ATSP performance and attention allocation. Research has shown that the type of automation imperfection leads to differential human performance costs. Participating in four 30-min scenarios were 12 full-performance-level ATSPs. Dependent variables included conflict detection and resolution performance, eye movements, and subjective ratings of trust and self confidence. ATSPs detected conflicts faster and more accurately with reliable automation, as compared with manual performance. When the conflict probe automation was unreliable, conflict detection performance declined with both miss (25% conflicts detected) and false alarm automation (50% conflicts detected). When the primary task of conflict detection was automated, even highly reliable yet imperfect automation (miss or false alarm) resulted in serious negative effects on operator performance. The further in advance that conflict probe automation predicts a conflict, the greater the uncertainty of prediction; thus, designers should provide users with feedback on the state of the automation or other tools that allow for inspection and analysis of the data underlying the conflict probe algorithm.

Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging

Science.gov (United States)

Phipps, Jennifer E.; Gorpas, Dimitris; Unger, Jakob; Darrow, Morgan; Bold, Richard J.; Marcu, Laura

2018-01-01

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N  =  20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.

Determination of the Optimized Automation Rate considering Effects of Automation on Human Operators in Nuclear Power Plants

International Nuclear Information System (INIS)

Lee, Seung Min; Seong, Poong Hyun; Kim, Jong Hyun; Kim, Man Cheol

2015-01-01

Automation refers to the use of a device or a system to perform a function previously performed by a human operator. It is introduced to reduce the human errors and to enhance the performance in various industrial fields, including the nuclear industry. However, these positive effects are not always achieved in complex systems such as nuclear power plants (NPPs). An excessive introduction of automation can generate new roles for human operators and change activities in unexpected ways. As more automation systems are accepted, the ability of human operators to detect automation failures and resume manual control is diminished. This disadvantage of automation is called the Out-of-the- Loop (OOTL) problem. We should consider the positive and negative effects of automation at the same time to determine the appropriate level of the introduction of automation. Thus, in this paper, we suggest an estimation method to consider the positive and negative effects of automation at the same time to determine the appropriate introduction of automation. This concept is limited in that it does not consider the effects of automation on human operators. Thus, a new estimation method for automation rate was suggested to overcome this problem

Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.

Science.gov (United States)

Irshad, Humayun; Jalali, Sepehr; Roux, Ludovic; Racoceanu, Daniel; Hwee, Lim Joo; Naour, Gilles Le; Capron, Frédérique

2013-01-01

According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques. We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT) features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM), and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for an International Conference on Pattern Recognition (ICPR) 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and classification rate.

AUTOMATED PROCESS MONITORING: APPLYING PROVEN AUTOMATION TECHNIQUES TO INTERNATIONAL SAFEGUARDS NEEDS

International Nuclear Information System (INIS)

O'Hara, Matthew J.; Durst, Philip C.; Grate, Jay W.; Devol, Timothy A.; Egorov, Oleg; Clements, John P.

2008-01-01

Identification and quantification of specific alpha- and beta-emitting radionuclides in complex liquid matrices is highly challenging, and is typically accomplished through laborious wet chemical sample preparation and separations followed by analysis using a variety of detection methodologies (e.g., liquid scintillation, gas proportional counting, alpha energy analysis, mass spectrometry). Analytical results may take days or weeks to report. Chains of custody and sample security measures may also complicate or slow the analytical process. When an industrial process-scale plant requires the monitoring of specific radionuclides as an indication of the composition of its feed stream or of plant performance, radiochemical measurements must be fast, accurate, and reliable. Scientists at Pacific Northwest National Laboratory have assembled a fully automated prototype Process Monitor instrument capable of a variety of tasks: automated sampling directly from a feed stream, sample digestion/analyte redox adjustment, chemical separations, radiochemical detection and data analysis/reporting. The system is compact, its components are fluidically inter-linked, and analytical results could be immediately transmitted to on- or off-site locations. The development of a rapid radiochemical Process Monitor for 99Tc in Hanford tank waste processing streams, capable of performing several measurements per hour, will be discussed in detail. More recently, the automated platform was modified to perform measurements of 90Sr in Hanford tank waste stimulant. The system exemplifies how automation could be integrated into reprocessing facilities to support international nuclear safeguards needs

Speech activity detection for the automated speaker recognition system of critical use

Directory of Open Access Journals (Sweden)

M. M. Bykov

2017-06-01

Full Text Available In the article, the authors developed a method for detecting speech activity for an automated system for recognizing critical use of speeches with wavelet parameterization of speech signal and classification at intervals of “language”/“pause” using a curvilinear neural network. The method of wavelet-parametrization proposed by the authors allows choosing the optimal parameters of wavelet transformation in accordance with the user-specified error of presentation of speech signal. Also, the method allows estimating the loss of information depending on the selected parameters of continuous wavelet transformation (NPP, which allowed to reduce the number of scalable coefficients of the LVP of the speech signal in order of magnitude with the allowable degree of distortion of the local spectrum of the LVP. An algorithm for detecting speech activity with a curvilinear neural network classifier is also proposed, which shows the high quality of segmentation of speech signals at intervals "language" / "pause" and is resistant to the presence in the speech signal of narrowband noise and technogenic noise due to the inherent properties of the curvilinear neural network.

Falls following discharge after an in-hospital fall

Directory of Open Access Journals (Sweden)

Kessler Lori A

2009-12-01

Full Text Available Abstract Background Falls are among the most common adverse events reported in hospitalized patients. While there is a growing body of literature on fall prevention in the hospital, the data examining the fall rate and risk factors for falls in the immediate post-hospitalization period has not been well described. The objectives of the present study were to determine the fall rate of in-hospital fallers at home and to explore the risk factors for falls during the immediate post-hospitalization period. Methods We identified patients who sustained a fall on one of 16 medical/surgical nursing units during an inpatient admission to an urban community teaching hospital. After discharge, falls were ascertained using weekly telephone surveillance for 4 weeks post-discharge. Patients were followed until death, loss to follow up or end of study (four weeks. Time spent rehospitalized or institutionalized was censored in rate calculations. Results Of 95 hospitalized patients who fell during recruitment, 65 (68% met inclusion criteria and agreed to participate. These subjects contributed 1498 person-days to the study (mean duration of follow-up = 23 days. Seventy-five percent were African-American and 43% were women. Sixteen patients (25% had multiple falls during hospitalization and 23 patients (35% suffered a fall-related injury during hospitalization. Nineteen patients (29% experienced 38 falls at their homes, yielding a fall rate of 25.4/1,000 person-days (95% CI: 17.3-33.4. Twenty-three patients (35% were readmitted and 3(5% died. One patient experienced a hip fracture. In exploratory univariate analysis, persons who were likely to fall at home were those who sustained multiple falls in the hospital (p = 0.008. Conclusion Patients who fall during hospitalization, especially on more than one occasion, are at high risk for falling at home following hospital discharge. Interventions to reduce falls would be appropriate to test in this high-risk population.

Automated Detection and Differentiation of Drusen, Exudates, and Cotton-Wool Spots in Digital Color Fundus Photographs for Diabetic Retinopathy Diagnosis

NARCIS (Netherlands)

Niemeijer, M.; van Ginneken, B.; Russel, S.R.; Suttorp-Schulten, M.S.A.; Abràmoff, M.D.

2007-01-01

purpose. To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. methods. Three hundred retinal images from one eye of 300

Cell-Detection Technique for Automated Patch Clamping

Science.gov (United States)

McDowell, Mark; Gray, Elizabeth

2008-01-01

A unique and customizable machinevision and image-data-processing technique has been developed for use in automated identification of cells that are optimal for patch clamping. [Patch clamping (in which patch electrodes are pressed against cell membranes) is an electrophysiological technique widely applied for the study of ion channels, and of membrane proteins that regulate the flow of ions across the membranes. Patch clamping is used in many biological research fields such as neurobiology, pharmacology, and molecular biology.] While there exist several hardware techniques for automated patch clamping of cells, very few of those techniques incorporate machine vision for locating cells that are ideal subjects for patch clamping. In contrast, the present technique is embodied in a machine-vision algorithm that, in practical application, enables the user to identify good and bad cells for patch clamping in an image captured by a charge-coupled-device (CCD) camera attached to a microscope, within a processing time of one second. Hence, the present technique can save time, thereby increasing efficiency and reducing cost. The present technique involves the utilization of cell-feature metrics to accurately make decisions on the degree to which individual cells are "good" or "bad" candidates for patch clamping. These metrics include position coordinates (x,y) in the image plane, major-axis length, minor-axis length, area, elongation, roundness, smoothness, angle of orientation, and degree of inclusion in the field of view. The present technique does not require any special hardware beyond commercially available, off-the-shelf patch-clamping hardware: A standard patchclamping microscope system with an attached CCD camera, a personal computer with an imagedata- processing board, and some experience in utilizing imagedata- processing software are all that are needed. A cell image is first captured by the microscope CCD camera and image-data-processing board, then the image

Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya.

Science.gov (United States)

Hansen, Morten B; Abràmoff, Michael D; Folk, James C; Mathenge, Wanjiku; Bastawrous, Andrew; Peto, Tunde

2015-01-01

Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya. Retinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]). First, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR. The primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard. Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0-93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. In this epidemiological sample, the IDP's grading was comparable to that of human graders'. It therefore might be feasible to consider inclusion into usual epidemiological grading.

Women's perspectives on falls and fall prevention during pregnancy.

Science.gov (United States)

Brewin, Dorothy; Naninni, Angela

2014-01-01

Falls are the leading cause of unintentional injury in women. During pregnancy, even a minor fall can result in adverse consequences. Evidence to inform effective and developmentally appropriate pregnancy fall prevention programs is lacking. Early research on pregnancy fall prevention suggests that exercise may reduce falls. However, acceptability and effectiveness of pregnancy fall prevention programs are untested. To better understand postpartum women's perspective and preferences on fall prevention strategies during pregnancy to formulate an intervention. Focus groups and individual interviews were conducted with 31 postpartum women using descriptive qualitative methodology. Discussion of falls during pregnancy and fall prevention strategies was guided by a focus group protocol and enhanced by 1- to 3-minute videos on proposed interventions. Focus groups were audio recorded, transcribed, and analyzed using NVivo 10 software. Emerging themes were environmental circumstances and physical changes of pregnancy leading to a fall, prevention strategies, barriers, safety concerns, and marketing a fall prevention program. Wet surfaces and inappropriate footwear commonly contributed to falls. Women preferred direct provider counseling and programs including yoga and Pilates. Fall prevention strategies tailored to pregnant women are needed. Perspectives of postpartum women support fall prevention through provider counseling and individual or supervised exercise programs.

Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya

DEFF Research Database (Denmark)

Juul Bøgelund Hansen, Morten; Abramoff, M. D.; Folk, J. C.

2015-01-01

Objective Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased...... workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields...... predictive value of IDP versus the human grader as reference standard. Results Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3%(95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0%(95% CI, 88.0-93.4%). The IDP ability to detect DED...

A fully automated contour detection algorithm the preliminary step for scatter and attenuation compensation in SPECT

International Nuclear Information System (INIS)

Younes, R.B.; Mas, J.; Bidet, R.

1988-01-01

Contour detection is an important step in information extraction from nuclear medicine images. In order to perform accurate quantitative studies in single photon emission computed tomography (SPECT) a new procedure is described which can rapidly derive the best fit contour of an attenuated medium. Some authors evaluate the influence of the detected contour on the reconstructed images with various attenuation correction techniques. Most of the methods are strongly affected by inaccurately detected contours. This approach uses the Compton window to redetermine the convex contour: It seems to be simpler and more practical in clinical SPECT studies. The main advantages of this procedure are the high speed of computation, the accuracy of the contour found and the programme's automation. Results obtained using computer simulated and real phantoms or clinical studies demonstrate the reliability of the present algorithm. (orig.)

Anxiety disorders and falls among older adults.

Science.gov (United States)

Holloway, K L; Williams, L J; Brennan-Olsen, S L; Morse, A G; Kotowicz, M A; Nicholson, G C; Pasco, J A

2016-11-15

Falls are common among older adults and can lead to serious injuries, including fractures. We aimed to determine associations between anxiety disorders and falls in older adults. Participants were 487 men and 376 women aged ≥60 years enrolled in the Geelong Osteoporosis Study, Australia. Using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Non-patient edition (SCID-I/NP), lifetime history of anxiety disorders was determined. Falls were determined by self-report. In men, a falls-risk score (Elderly Falls Screening Test (EFST)) was also calculated. Among fallers, 24 of 299 (8.0%) had a lifetime history of anxiety disorder compared to 36 of 634 (5.7%) non-fallers (p=0.014). Examination of the association between anxiety and falls suggested differential relationships for men and women. In men, following adjustment for psychotropic medications, mobility and blood pressure, lifetime anxiety disorder was associated with falling (OR 2.96; 95%CI 1.07-8.21) and with EFST score (OR 3.46; 95%CI 1.13-10.6). In women, an association between lifetime anxiety disorder and falls was explained by psychotropic medication use, poor mobility and socioeconomic status. Sub-group analyses involving types of anxiety and anxiety disorders over the past 12-months were not performed due to power limitations. Although anxiety disorders were independently associated with a 3-fold increase in likelihood of reported falls and high falls risk among men, an independent association was not detected among women. These results may aid in prevention of falls through specific interventions aimed at reducing anxiety, particularly in men. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy.

Science.gov (United States)

Ernstsen, Christina L; Login, Frédéric H; Jensen, Helene H; Nørregaard, Rikke; Møller-Jensen, Jakob; Nejsum, Lene N

2017-08-01

To target bacterial pathogens that invade and proliferate inside host cells, it is necessary to design intervention strategies directed against bacterial attachment, cellular invasion and intracellular proliferation. We present an automated microscopy-based, fast, high-throughput method for analyzing size and number of intracellular bacterial colonies in infected tissue culture cells. Cells are seeded in 48-well plates and infected with a GFP-expressing bacterial pathogen. Following gentamicin treatment to remove extracellular pathogens, cells are fixed and cell nuclei stained. This is followed by automated microscopy and subsequent semi-automated spot detection to determine the number of intracellular bacterial colonies, their size distribution, and the average number per host cell. Multiple 48-well plates can be processed sequentially and the procedure can be completed in one working day. As a model we quantified intracellular bacterial colonies formed by uropathogenic Escherichia coli (UPEC) during infection of human kidney cells (HKC-8). Urinary tract infections caused by UPEC are among the most common bacterial infectious diseases in humans. UPEC can colonize tissues of the urinary tract and is responsible for acute, chronic, and recurrent infections. In the bladder, UPEC can form intracellular quiescent reservoirs, thought to be responsible for recurrent infections. In the kidney, UPEC can colonize renal epithelial cells and pass to the blood stream, either via epithelial cell disruption or transcellular passage, to cause sepsis. Intracellular colonies are known to be clonal, originating from single invading UPEC. In our experimental setup, we found UPEC CFT073 intracellular bacterial colonies to be heterogeneous in size and present in nearly one third of the HKC-8 cells. This high-throughput experimental format substantially reduces experimental time and enables fast screening of the intracellular bacterial load and cellular distribution of multiple

A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study.

Science.gov (United States)

Kabeshova, A; Annweiler, C; Fantino, B; Philip, T; Gromov, V A; Launay, C P; Beauchet, O

2014-06-01

Regression tree (RT) analyses are particularly adapted to explore the risk of recurrent falling according to various combinations of fall risk factors compared to logistic regression models. The aims of this study were (1) to determine which combinations of fall risk factors were associated with the occurrence of recurrent falls in older community-dwellers, and (2) to compare the efficacy of RT and multiple logistic regression model for the identification of recurrent falls. A total of 1,760 community-dwelling volunteers (mean age ± standard deviation, 71.0 ± 5.1 years; 49.4 % female) were recruited prospectively in this cross-sectional study. Age, gender, polypharmacy, use of psychoactive drugs, fear of falling (FOF), cognitive disorders and sad mood were recorded. In addition, the history of falls within the past year was recorded using a standardized questionnaire. Among 1,760 participants, 19.7 % (n = 346) were recurrent fallers. The RT identified 14 nodes groups and 8 end nodes with FOF as the first major split. Among participants with FOF, those who had sad mood and polypharmacy formed the end node with the greatest OR for recurrent falls (OR = 6.06 with p falls (OR = 0.25 with p factors for recurrent falls, the combination most associated with recurrent falls involving FOF, sad mood and polypharmacy. The FOF emerged as the risk factor strongly associated with recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.

Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach

Directory of Open Access Journals (Sweden)

Humayun Irshad

2013-01-01

Full Text Available Context: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX biologically inspired approach for mitosis detection using machine-learning techniques. Materials and Methods: We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM, and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. Results: The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS dataset provided for an International Conference on Pattern Recognition (ICPR 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. Conclusions: Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and

Sink detection on tilted terrain for automated identification of glacial cirques

Science.gov (United States)

Prasicek, Günther; Robl, Jörg; Lang, Andreas

2016-04-01

Glacial cirques are morphologically distinct but complex landforms and represent a vital part of high mountain topography. Their distribution, elevation and relief are expected to hold information on (1) the extent of glacial occupation, (2) the mechanism of glacial cirque erosion, and (3) how glacial in concert with periglacial processes can limit peak altitude and mountain range height. While easily detectably for the expert's eye both in nature and on various representations of topography, their complicated nature makes them a nemesis for computer algorithms. Consequently, manual mapping of glacial cirques is commonplace in many mountain landscapes worldwide, but consistent datasets of cirque distribution and objectively mapped cirques and their morphometrical attributes are lacking. Among the biggest problems for algorithm development are the complexity in shape and the great variability of cirque size. For example, glacial cirques can be rather circular or longitudinal in extent, exist as individual and composite landforms, show prominent topographic depressions or can entirely be filled with water or sediment. For these reasons, attributes like circularity, size, drainage area and topology of landform elements (e.g. a flat floor surrounded by steep walls) have only a limited potential for automated cirque detection. Here we present a novel, geomorphometric method for automated identification of glacial cirques on digital elevation models that exploits their genetic bowl-like shape. First, we differentiate between glacial and fluvial terrain employing an algorithm based on a moving window approach and multi-scale curvature, which is also capable of fitting the analysis window to valley width. We then fit a plane to the valley stretch clipped by the analysis window and rotate the terrain around the center cell until the plane is level. Doing so, we produce sinks of considerable size if the clipped terrain represents a cirque, while no or only very small sinks

Automated microfluidically controlled electrochemical biosensor for the rapid and highly sensitive detection of Francisella tularensis.

Science.gov (United States)

Dulay, Samuel B; Gransee, Rainer; Julich, Sandra; Tomaso, Herbert; O'Sullivan, Ciara K

2014-09-15

Tularemia is a highly infectious zoonotic disease caused by a Gram-negative coccoid rod bacterium, Francisella tularensis. Tularemia is considered as a life-threatening potential biological warfare agent due to its high virulence, transmission, mortality and simplicity of cultivation. In the work reported here, different electrochemical immunosensor formats for the detection of whole F. tularensis bacteria were developed and their performance compared. An anti-Francisella antibody (FB11) was used for the detection that recognises the lipopolysaccharide found in the outer membrane of the bacteria. In the first approach, gold-supported self-assembled monolayers of a carboxyl terminated bipodal alkanethiol were used to covalently cross-link with the FB11 antibody. In an alternative second approach F(ab) fragments of the FB11 antibody were generated and directly chemisorbed onto the gold electrode surface. The second approach resulted in an increased capture efficiency and higher sensitivity. Detection limits of 4.5 ng/mL for the lipopolysaccharide antigen and 31 bacteria/mL for the F. tularensis bacteria were achieved. Having demonstrated the functionality of the immunosensor, an electrode array was functionalised with the antibody fragment and integrated with microfluidics and housed in a tester set-up that facilitated complete automation of the assay. The only end-user intervention is sample addition, requiring less than one-minute hands-on time. The use of the automated microfluidic set-up not only required much lower reagent volumes but also the required incubation time was considerably reduced and a notable increase of 3-fold in assay sensitivity was achieved with a total assay time from sample addition to read-out of less than 20 min. Copyright © 2014 Elsevier B.V. All rights reserved.

An Automated Motion Detection and Reward System for Animal Training.

Science.gov (United States)

Miller, Brad; Lim, Audrey N; Heidbreder, Arnold F; Black, Kevin J

2015-12-04

A variety of approaches has been used to minimize head movement during functional brain imaging studies in awake laboratory animals. Many laboratories expend substantial effort and time training animals to remain essentially motionless during such studies. We could not locate an "off-the-shelf" automated training system that suited our needs.  We developed a time- and labor-saving automated system to train animals to hold still for extended periods of time. The system uses a personal computer and modest external hardware to provide stimulus cues, monitor movement using commercial video surveillance components, and dispense rewards. A custom computer program automatically increases the motionless duration required for rewards based on performance during the training session but allows changes during sessions. This system was used to train cynomolgus monkeys (Macaca fascicularis) for awake neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The automated system saved the trainer substantial time, presented stimuli and rewards in a highly consistent manner, and automatically documented training sessions. We have limited data to prove the training system's success, drawn from the automated records during training sessions, but we believe others may find it useful. The system can be adapted to a range of behavioral training/recording activities for research or commercial applications, and the software is freely available for non-commercial use.

Understanding reliance on automation: effects of error type, error distribution, age and experience

Science.gov (United States)

Sanchez, Julian; Rogers, Wendy A.; Fisk, Arthur D.; Rovira, Ericka

2015-01-01

An obstacle detection task supported by “imperfect” automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over relying on it during non-alarms states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behavior according to the characteristics of the automation similarly to younger adults, although it took them longer to do so. The results of this study suggest the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human-automation interaction can help designers of automated systems make predictions about human behavior and system performance as a function of the characteristics of the automation. PMID:25642142

Relationship between subjective fall risk assessment and falls and fall-related fractures in frail elderly people.

Science.gov (United States)

Shimada, Hiroyuki; Suzukawa, Megumi; Ishizaki, Tatsuro; Kobayashi, Kumiko; Kim, Hunkyung; Suzuki, Takao

2011-08-12

Objective measurements can be used to identify people with risks of falls, but many frail elderly adults cannot complete physical performance tests. The study examined the relationship between a subjective risk rating of specific tasks (SRRST) to screen for fall risks and falls and fall-related fractures in frail elderly people. The SRRST was investigated in 5,062 individuals aged 65 years or older who were utilized day-care services. The SRRST comprised 7 dichotomous questions to screen for fall risks during movements and behaviours such as walking, transferring, and wandering. The history of falls and fall-related fractures during the previous year was reported by participants or determined from an interview with the participant's family and care staff. All SRRST items showed significant differences between the participants with and without falls and fall-related fractures. In multiple logistic regression analysis adjusted for age, sex, diseases, and behavioural variables, the SRRST score was independently associated with history of falls and fractures. Odds ratios for those in the high-risk SRRST group (≥ 5 points) compared with the no risk SRRST group (0 point) were 6.15 (p fall, 15.04 (p falls, and 5.05 (p fall-related fractures. The results remained essentially unchanged in subgroup analysis accounting for locomotion status. These results suggest that subjective ratings by care staff can be utilized to determine the risks of falls and fall-related fractures in the frail elderly, however, these preliminary results require confirmation in further prospective research.

DEEP LEARNING AND IMAGE PROCESSING FOR AUTOMATED CRACK DETECTION AND DEFECT MEASUREMENT IN UNDERGROUND STRUCTURES

Directory of Open Access Journals (Sweden)

F. Panella

2018-05-01

Full Text Available This work presents the combination of Deep-Learning (DL and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information. As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.

Evaluation of automated nucleic acid extraction methods for virus detection in a multicenter comparative trial

DEFF Research Database (Denmark)

Rasmussen, Thomas Bruun; Uttenthal, Åse; Hakhverdyan, M.

2009-01-01

between the results obtained for the different automated extraction platforms. In particular, the limit of detection was identical for 9/12 and 8/12 best performing robots (using dilutions of BVDV infected-serum and cell culture material, respectively), which was similar to a manual extraction method used......Five European veterinary laboratories participated in an exercise to compare the performance of nucleic acid extraction robots. Identical sets of coded samples were prepared using serial dilutions of bovine viral diarrhoea virus (BVDV) from serum and cell culture propagated material. Each...

Fully automated left ventricular contour detection for gated radionuclide angiography, (1)

International Nuclear Information System (INIS)

Hosoba, Minoru; Wani, Hidenobu; Hiroe, Michiaki; Kusakabe, Kiyoko.

1984-01-01

A fully automated practical method has been developed to detect the left ventricular (LV) contour from gated pool images. Ejection fraction and volume curve can be computed accurately without operater variance. The characteristics of the method are summarized as follows: 1. Optimal design of the filter that works on Fourier domain, can be achieved to improve the signal to noise ratio. 2. New algorithm which use the cosine and sine transform images has been developed for the separating ventricle from atrium and defining center of LV. 3. Contrast enhancement by optimized square filter. 4. Radial profiles are generated from the center of LV and smoothed by fourth order Fourier series approximation. The crossing point with local threshold value searched from the center of the LV is defined as edge. 5. LV contour is obtained by conecting all the edge points defined on radial profiles by fitting them to Fourier function. (author)

Automated Detection of Thermo-Erosion in High Latitude Ecosystems

Science.gov (United States)

Lara, M. J.; Chipman, M. L.; Hu, F.

2017-12-01

Detecting permafrost disturbance is of critical importance as the severity of climate change and associated increase in wildfire frequency and magnitude impacts regional to global carbon dynamics. However, it has not been possible to evaluate spatiotemporal patterns of permafrost degradation over large regions of the Arctic, due to limited spatial and temporal coverage of high resolution optical, radar, lidar, or hyperspectral remote sensing products. Here we present the first automated multi-temporal analysis for detecting disturbance in response to permafrost thaw, using meso-scale high-frequency remote sensing products (i.e. entire Landsat image archive). This approach was developed, tested, and applied in the Noatak National Preserve (26,500km2) in northwestern Alaska. We identified thermo-erosion (TE), by capturing the indirect spectral signal associated with episodic sediment plumes in adjacent waterbodies following TE disturbance. We isolated this turbidity signal within lakes during summer (mid-summer & late-summer) and annual time-period image composites (1986-2016), using the cloud-based geospatial parallel processing platform, Google Earth Engine™API. We validated the TE detection algorithm using seven consecutive years of sub-meter high resolution imagery (2009-2015) covering 798 ( 33%) of the 2456 total lakes in the Noatak lowlands. Our approach had "good agreement" with sediment pulses and landscape deformation in response to permafrost thaw (overall accuracy and kappa coefficient of 85% and 0.61). We identify active TE to impact 10.4% of all lakes, but was inter-annually variable, with the highest and lowest TE years represented by 1986 ( 41.1%) and 2002 ( 0.7%), respectively. We estimate thaw slumps, lake erosion, lake drainage, and gully formation to account for 23.3, 61.8, 12.5, and 1.3%, of all active TE across the Noatak National Preserve. Preliminary analysis, suggests TE may be subject to a hysteresis effect following extreme climatic

Automated 3-D Radiation Mapping

International Nuclear Information System (INIS)

Tarpinian, J. E.

1991-01-01

This work describes an automated radiation detection and imaging system which combines several state-of-the-art technologies to produce a portable but very powerful visualization tool for planning work in radiation environments. The system combines a radiation detection system, a computerized radiation imaging program, and computerized 3-D modeling to automatically locate and measurements are automatically collected and imaging techniques are used to produce colored, 'isodose' images of the measured radiation fields. The isodose lines from the images are then superimposed over the 3-D model of the area. The final display shows the various components in a room and their associated radiation fields. The use of an automated radiation detection system increases the quality of radiation survey obtained measurements. The additional use of a three-dimensional display allows easier visualization of the area and associated radiological conditions than two-dimensional sketches

Automated Analysis of Accountability

DEFF Research Database (Denmark)

Bruni, Alessandro; Giustolisi, Rosario; Schürmann, Carsten

2017-01-01

that the system can detect the misbehaving parties who caused that failure. Accountability is an intuitively stronger property than verifiability as the latter only rests on the possibility of detecting the failure of a goal. A plethora of accountability and verifiability definitions have been proposed...... in the literature. Those definitions are either very specific to the protocols in question, hence not applicable in other scenarios, or too general and widely applicable but requiring complicated and hard to follow manual proofs. In this paper, we advance formal definitions of verifiability and accountability...... that are amenable to automated verification. Our definitions are general enough to be applied to different classes of protocols and different automated security verification tools. Furthermore, we point out formally the relation between verifiability and accountability. We validate our definitions...

The relationship between orthostatic hypotension and falling in older adults.

Science.gov (United States)

Shaw, Brett H; Claydon, Victoria E

2014-02-01

Falls are devastating events and are the largest contributor towards injury-related hospitalization of older adults. Orthostatic hypotension (OH) represents an intrinsic risk factor for falls in older adults. OH refers to a significant decrease in blood pressure upon assuming an upright posture. Declines in blood pressure can reduce cerebral perfusion; this can impair consciousness, lead to dizziness, and increase the likelihood of a fall. Although theoretical mechanisms linking OH and falls exist, the magnitude of the association remains poorly characterized, possibly because of methodological differences between previous studies. The use of non-invasive beat-to-beat blood pressure monitoring has altered the way in which OH is now defined, and represents a substantial improvement for detecting OH that was previously unavailable in many studies. Additionally, there is a lack of consistency and standardization of orthostatic assessments and analysis techniques for interpreting blood pressure data. This review explores the previous literature examining the relationship between OH and falls. We highlight the impact of broadening the timing, degree, and overall duration of blood pressure measurements on the detection of OH. We discuss the types of orthostatic stress assessments currently used to evaluate OH and the various techniques capable of measuring these often transient blood pressure changes. Overall, we identify future solutions that may better clarify the relationship between OH and falling risk in order to gain a more precise understanding of potential mechanisms for falls in older adults.

Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images

Directory of Open Access Journals (Sweden)

Samina Khalid

2017-01-01

Full Text Available Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE, central serous chorioretinopathy (CSCR, or age related macular degeneration (ARMD. Optical coherence tomography (OCT imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported. This paper presents the world’s first ever decision support system to automatically detect RE, CSCR, and ARMD retinal pathologies and healthy retina from OCT images. The automated disease diagnosis in our proposed system is based on multilayered support vector machines (SVM classifier trained on 40 labeled OCT scans (10 healthy, 10 RE, 10 CSCR, and 10 ARMD. After training, SVM forms an accurate decision about the type of retinal pathology using 9 extracted features. We have tested our proposed system on 2819 OCT scans (1437 healthy, 640 RE, and 742 CSCR of 502 patients from two different datasets and our proposed system correctly diagnosed 2817/2819 subjects with the accuracy, sensitivity, and specificity ratings of 99.92%, 100%, and 99.86%, respectively.

Automated detection of submerged navigational obstructions in freshwater impoundments with hull mounted sidescan sonar

Science.gov (United States)

Morris, Phillip A.

The prevalence of low-cost side scanning sonar systems mounted on small recreational vessels has created improved opportunities to identify and map submerged navigational hazards in freshwater impoundments. However, these economical sensors also present unique challenges for automated techniques. This research explores related literature in automated sonar imagery processing and mapping technology, proposes and implements a framework derived from these sources, and evaluates the approach with video collected from a recreational grade sonar system. Image analysis techniques including optical character recognition and an unsupervised computer automated detection (CAD) algorithm are employed to extract the transducer GPS coordinates and slant range distance of objects protruding from the lake bottom. The retrieved information is formatted for inclusion into a spatial mapping model. Specific attributes of the sonar sensors are modeled such that probability profiles may be projected onto a three dimensional gridded map. These profiles are computed from multiple points of view as sonar traces crisscross or come near each other. As lake levels fluctuate over time so do the elevation points of view. With each sonar record, the probability of a hazard existing at certain elevations at the respective grid points is updated with Bayesian mechanics. As reinforcing data is collected, the confidence of the map improves. Given a lake's current elevation and a vessel draft, a final generated map can identify areas of the lake that have a high probability of containing hazards that threaten navigation. The approach is implemented in C/C++ utilizing OpenCV, Tesseract OCR, and QGIS open source software and evaluated in a designated test area at Lake Lavon, Collin County, Texas.

Automated detection of test fixture strategies and smells

NARCIS (Netherlands)

Greiler, M.S.; Van Deursen, A.; Storey, M.A.

2013-01-01

Paper accepted for publication in the Proceedings of the Sixth International Conference on Software Testing, Verification and Validation, IEEE Computer Society, 18-22 March 2013, ISBN 978-1-4673-5961-0, doi: 10.1109/ICST.2013.45 Designing automated tests is a challenging task. One important concern

Towards a fully automated lab-on-a-disc system integrating sample enrichment and detection of analytes from complex matrices

DEFF Research Database (Denmark)

Andreasen, Sune Zoëga

the technology on a large scale from fulfilling its potential for maturing into applied technologies and products. In this work, we have taken the first steps towards realizing a capable and truly automated “sample-to-answer” analysis system, aimed at small molecule detection and quantification from a complex...... sample matrix. The main result is a working prototype of a microfluidic system, integrating both centrifugal microfluidics for sample handling, supported liquid membrane extraction (SLM) for selective and effective sample treatment, as well as in-situ electrochemical detection. As a case study...

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

Science.gov (United States)

Li, Qi; Melton, Kristin; Lingren, Todd; Kirkendall, Eric S; Hall, Eric; Zhai, Haijun; Ni, Yizhao; Kaiser, Megan; Stoutenborough, Laura; Solti, Imre

2014-01-01

Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Automated detection of heuristics and biases among pathologists in a computer-based system.

Science.gov (United States)

Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-08-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software

Directory of Open Access Journals (Sweden)

Manh Cong Tran

2016-01-01

Full Text Available HTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware thrives. Unfortunately, besides normal autoware, HTTP malware and greyware are also spreading rapidly in web environment. Consequently, network communication is not just rigorously controlled by users intention. This raises the demand for analyzing HTTP autoware communication behaviour to detect and classify malicious and normal activities via HTTP traffic. Hence, in this paper, based on many studies and analysis of the autoware communication behaviour through access graph, a new method to detect and classify HTTP autoware communication at network level is presented. The proposal system includes combination of MapReduce of Hadoop and MarkLogic NoSQL database along with xQuery to deal with huge HTTP traffic generated each day in a large network. The method is examined with real outbound HTTP traffic data collected through a proxy server of a private network. Experimental results obtained for proposed method showed that promised outcomes are achieved since 95.1% of suspicious autoware are classified and detected. This finding may assist network and system administrator in inspecting early the internal threats caused by HTTP autoware.

The Science of Home Automation

Science.gov (United States)

Thomas, Brian Louis

Smart home technologies and the concept of home automation have become more popular in recent years. This popularity has been accompanied by social acceptance of passive sensors installed throughout the home. The subsequent increase in smart homes facilitates the creation of home automation strategies. We believe that home automation strategies can be generated intelligently by utilizing smart home sensors and activity learning. In this dissertation, we hypothesize that home automation can benefit from activity awareness. To test this, we develop our activity-aware smart automation system, CARL (CASAS Activity-aware Resource Learning). CARL learns the associations between activities and device usage from historical data and utilizes the activity-aware capabilities to control the devices. To help validate CARL we deploy and test three different versions of the automation system in a real-world smart environment. To provide a foundation of activity learning, we integrate existing activity recognition and activity forecasting into CARL home automation. We also explore two alternatives to using human-labeled data to train the activity learning models. The first unsupervised method is Activity Detection, and the second is a modified DBSCAN algorithm that utilizes Dynamic Time Warping (DTW) as a distance metric. We compare the performance of activity learning with human-defined labels and with automatically-discovered activity categories. To provide evidence in support of our hypothesis, we evaluate CARL automation in a smart home testbed. Our results indicate that home automation can be boosted through activity awareness. We also find that the resulting automation has a high degree of usability and comfort for the smart home resident.

Relationship between subjective fall risk assessment and falls and fall-related fractures in frail elderly people

Directory of Open Access Journals (Sweden)

Shimada Hiroyuki

2011-08-01

Full Text Available Abstract Background Objective measurements can be used to identify people with risks of falls, but many frail elderly adults cannot complete physical performance tests. The study examined the relationship between a subjective risk rating of specific tasks (SRRST to screen for fall risks and falls and fall-related fractures in frail elderly people. Methods The SRRST was investigated in 5,062 individuals aged 65 years or older who were utilized day-care services. The SRRST comprised 7 dichotomous questions to screen for fall risks during movements and behaviours such as walking, transferring, and wandering. The history of falls and fall-related fractures during the previous year was reported by participants or determined from an interview with the participant's family and care staff. Results All SRRST items showed significant differences between the participants with and without falls and fall-related fractures. In multiple logistic regression analysis adjusted for age, sex, diseases, and behavioural variables, the SRRST score was independently associated with history of falls and fractures. Odds ratios for those in the high-risk SRRST group (≥ 5 points compared with the no risk SRRST group (0 point were 6.15 (p Conclusion These results suggest that subjective ratings by care staff can be utilized to determine the risks of falls and fall-related fractures in the frail elderly, however, these preliminary results require confirmation in further prospective research.

Support vector machine as a binary classifier for automated object detection in remotely sensed data

International Nuclear Information System (INIS)

Wardaya, P D

2014-01-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result

Support vector machine as a binary classifier for automated object detection in remotely sensed data

Science.gov (United States)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Automated Solar Flare Detection and Feature Extraction in High-Resolution and Full-Disk Hα Images

Science.gov (United States)

Yang, Meng; Tian, Yu; Liu, Yangyi; Rao, Changhui

2018-05-01

In this article, an automated solar flare detection method applied to both full-disk and local high-resolution Hα images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of Hα solar flares.

Radiologists' Performance for Detecting Lesions and the Interobserver Variability of Automated Whole Breast Ultrasound

International Nuclear Information System (INIS)

Kim, Sung Hun; Kang, Bong Joo; Choi, Byung Gil; Choi, Jae Jung; Lee, Ji Hye; Song, Byung Joo; Choe, Byung Joo; Park, Sarah; Kim, Hyunbin

2013-01-01

To compare the detection performance of the automated whole breast ultrasound (AWUS) with that of the hand-held breast ultrasound (HHUS) and to evaluate the interobserver variability in the interpretation of the AWUS. AWUS was performed in 38 breast cancer patients. A total of 66 lesions were included: 38 breast cancers, 12 additional malignancies and 16 benign lesions. Three breast radiologists independently reviewed the AWUS data and analyzed the breast lesions according to the BI-RADS classification. The detection rate of malignancies was 98.0% for HHUS and 90.0%, 88.0% and 96.0% for the three readers of the AWUS. The sensitivity and the specificity were 98.0% and 62.5% in HHUS, 90.0% and 87.5% for reader 1, 88.0% and 81.3% for reader 2, and 96.0% and 93.8% for reader 3, in AWUS. There was no significant difference in the radiologists' detection performance, sensitivity and specificity (p > 0.05) between the two modalities. The interobserver agreement was fair to good for the ultrasonographic features, categorization, size, and the location of breast masses. AWUS is thought to be useful for detecting breast lesions. In comparison with HHUS, AWUS shows no significant difference in the detection rate, sensitivity and the specificity, with high degrees of interobserver agreement

Radiologists' Performance for Detecting Lesions and the Interobserver Variability of Automated Whole Breast Ultrasound

Energy Technology Data Exchange (ETDEWEB)

Kim, Sung Hun; Kang, Bong Joo; Choi, Byung Gil; Choi, Jae Jung; Lee, Ji Hye [Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of); Song, Byung Joo; Choe, Byung Joo [Department of General Surgery, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of); Park, Sarah [Department of Internal Medicine, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of); Kim, Hyunbin [CMC Clinical Research Coordinating Center, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of)

2013-07-01

To compare the detection performance of the automated whole breast ultrasound (AWUS) with that of the hand-held breast ultrasound (HHUS) and to evaluate the interobserver variability in the interpretation of the AWUS. AWUS was performed in 38 breast cancer patients. A total of 66 lesions were included: 38 breast cancers, 12 additional malignancies and 16 benign lesions. Three breast radiologists independently reviewed the AWUS data and analyzed the breast lesions according to the BI-RADS classification. The detection rate of malignancies was 98.0% for HHUS and 90.0%, 88.0% and 96.0% for the three readers of the AWUS. The sensitivity and the specificity were 98.0% and 62.5% in HHUS, 90.0% and 87.5% for reader 1, 88.0% and 81.3% for reader 2, and 96.0% and 93.8% for reader 3, in AWUS. There was no significant difference in the radiologists' detection performance, sensitivity and specificity (p > 0.05) between the two modalities. The interobserver agreement was fair to good for the ultrasonographic features, categorization, size, and the location of breast masses. AWUS is thought to be useful for detecting breast lesions. In comparison with HHUS, AWUS shows no significant difference in the detection rate, sensitivity and the specificity, with high degrees of interobserver agreement.

Automation impact study of Army Training Management

Energy Technology Data Exchange (ETDEWEB)

Sanquist, T.F.; Schuller, C.R.; McCallum, M.C.; Underwood, J.A.; Bettin, P.J.; King, J.L.; Melber, B.D.; Hostick, C.J.; Seaver, D.A.

1988-01-01

The main objectives of this impact study were to identify the potential cost savings associated with automated Army Training Management (TM), and to perform a cost-benefit analysis for an Army-wide automated TM system. A subsidiary goal was to establish baseline data for an independent evaluation of a prototype Integrated Training Management System (ITMS), to be tested in the fall of 1988. A structured analysis of TM doctrine was performed for comparison with empirical data gathered in a job analysis survey of selected units of the 9ID (MTZ) at Ft. Lewis, Washington. These observations will be extended to other units in subsequent surveys. The survey data concerning staffing levels and amount of labor expended on eight distinct TM tasks were analyzed in a cost effectiveness model. The main results of the surveys and cost effectiveness modelling are summarized. 18 figs., 47 tabs.

Automation impact study of Army Training Management

International Nuclear Information System (INIS)

Sanquist, T.F.; Schuller, C.R.; McCallum, M.C.; Underwood, J.A.; Bettin, P.J.; King, J.L.; Melber, B.D.; Hostick, C.J.; Seaver, D.A.

1988-01-01

The main objectives of this impact study were to identify the potential cost savings associated with automated Army Training Management (TM), and to perform a cost-benefit analysis for an Army-wide automated TM system. A subsidiary goal was to establish baseline data for an independent evaluation of a prototype Integrated Training Management System (ITMS), to be tested in the fall of 1988. A structured analysis of TM doctrine was performed for comparison with empirical data gathered in a job analysis survey of selected units of the 9ID (MTZ) at Ft. Lewis, Washington. These observations will be extended to other units in subsequent surveys. The survey data concerning staffing levels and amount of labor expended on eight distinct TM tasks were analyzed in a cost effectiveness model. The main results of the surveys and cost effectiveness modelling are summarized. 18 figs., 47 tabs

Patient centered fall risk awareness perspectives: clinical correlates and fall risk

Science.gov (United States)

Verghese, Joe

2016-01-01

Background While objective measures to assess risk of falls in older adults have been established; the value of patient self-reports in the context of falls is not known. Objectives To identify clinical correlates of patient centered fall risk awareness, and their validity for predicting falls. Design Prospective cohort study. Setting and Participants 316 non-demented and ambulatory community-dwelling older adults (mean age 78 years, 55% women). Measurements Fall risk awareness was assessed with a two-item questionnaire, which asked participants about overall likelihood and personal risk of falling over the next 12 months. Incident falls were recorded over study follow-up. Results Fifty-three participants (16.8%) responded positively to the first fall risk awareness question about being likely to have a fall in the next 12 months, and 100 (31.6%) reported being at personal risk of falling over the next 12 months. There was only fair correlation (kappa 0.370) between responses on the two questions. Prior falls and depressive symptoms were associated with positive responses on both fall risk awareness questions. Age and other established fall risk factors were not associated with responses on both fall risk awareness questions. The fall risk awareness questionnaire did not predict incident falls or injurious falls. Conclusion Fall risk awareness is low in older adults. While patient centered fall risk awareness is not predictive of falls, subjective risk perceptions should be considered when designing fall preventive strategies as they may influence participation and behaviors. PMID:27801936

Effects of Automation Types on Air Traffic Controller Situation Awareness and Performance

Science.gov (United States)

Sethumadhavan, A.

2009-01-01

The Joint Planning and Development Office has proposed the introduction of automated systems to help air traffic controllers handle the increasing volume of air traffic in the next two decades (JPDO, 2007). Because fully automated systems leave operators out of the decision-making loop (e.g., Billings, 1991), it is important to determine the right level and type of automation that will keep air traffic controllers in the loop. This study examined the differences in the situation awareness (SA) and collision detection performance of individuals when they worked with information acquisition, information analysis, decision and action selection and action implementation automation to control air traffic (Parasuraman, Sheridan, & Wickens, 2000). When the automation was unreliable, the time taken to detect an upcoming collision was significantly longer for all the automation types compared with the information acquisition automation. This poor performance following automation failure was mediated by SA, with lower SA yielding poor performance. Thus, the costs associated with automation failure are greater when automation is applied to higher order stages of information processing. Results have practical implications for automation design and development of SA training programs.

Framework for Human-Automation Collaboration: Conclusions from Four Studies

Energy Technology Data Exchange (ETDEWEB)

Oxstrand, Johanna [Idaho National Lab. (INL), Idaho Falls, ID (United States); Le Blanc, Katya L. [Idaho National Lab. (INL), Idaho Falls, ID (United States); O' Hara, John [Brookhaven National Lab. (BNL), Upton, NY (United States); Joe, Jeffrey C. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Whaley, April M. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Medema, Heather [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2013-11-01

The Human Automation Collaboration (HAC) research project is investigating how advanced technologies that are planned for Advanced Small Modular Reactors (AdvSMR) will affect the performance and the reliability of the plant from a human factors and human performance perspective. The HAC research effort investigates the consequences of allocating functions between the operators and automated systems. More specifically, the research team is addressing how to best design the collaboration between the operators and the automated systems in a manner that has the greatest positive impact on overall plant performance and reliability. Oxstrand et al. (2013 - March) describes the efforts conducted by the researchers to identify the research needs for HAC. The research team reviewed the literature on HAC, developed a model of HAC, and identified gaps in the existing knowledge of human-automation collaboration. As described in Oxstrand et al. (2013 – June), the team then prioritized the research topics identified based on the specific needs in the context of AdvSMR. The prioritization was based on two sources of input: 1) The preliminary functions and tasks, and 2) The model of HAC. As a result, three analytical studies were planned and conduced; 1) Models of Teamwork, 2) Standardized HAC Performance Measurement Battery, and 3) Initiators and Triggering Conditions for Adaptive Automation. Additionally, one field study was also conducted at Idaho Falls Power.

Fall-related activity avoidance in relation to a history of falls or near falls, fear of falling and disease severity in people with Parkinson's disease.

Science.gov (United States)

Kader, Manzur; Iwarsson, Susanne; Odin, Per; Nilsson, Maria H

2016-06-02

There is limited knowledge concerning fall-related activity avoidance in people with Parkinson's disease (PD); such knowledge would be of importance for the development of more efficient PD-care and rehabilitation. This study aimed to examine how fall-related activity avoidance relates to a history of self-reported falls/near falls and fear of falling (FOF) as well as to disease severity in people with PD. Data were collected from 251 (61 % men) participants with PD; their median (min-max) age and PD duration were 70 (45-93) and 8 (1-43) years, respectively. A self-administered postal survey preceded a home visit which included observations, clinical tests and interview-administered questionnaires. Fall-related activity avoidance was assessed using the modified Survey of Activities and Fear of Falling in the Elderly (mSAFFE) as well as by using a dichotomous (Yes/No) question. Further dichotomous questions concerned: the presence of FOF and the history (past 6 months) of falls or near falls, followed by stating the number of incidents. Disease severity was assessed according to the Hoehn and Yahr (HY) stages. In the total sample (n = 251), 41 % of the participants reported fall-related activity avoidance; the median mSAFFE score was 22. In relation to a history of fall, the proportions of participants (p fall-related activity avoidance were: non-fallers (30 %), single fallers (50 %) and recurrent fallers, i.e. ≥ 2 falls (57 %). Among those that reported near falls (but no falls), 51 % (26 out of 51) reported fall-related activity avoidance. Of those that reported FOF, 70 % reported fall-related activity avoidance. Fall-related activity avoidance ranged from 24 % in the early PD-stage (HY I) to 74 % in the most severe stages (HY IV-V). Results indicate that fall-related activity avoidance may be related to a history of self-reported falls/near falls, FOF and disease severity in people with PD. Importantly, fall-related activity avoidance is

Automated radioanalytical system incorporating microwave-assisted sample preparation, chemical separation, and online radiometric detection for the monitoring of total 99Tc in nuclear waste processing streams.

Science.gov (United States)

Egorov, Oleg B; O'Hara, Matthew J; Grate, Jay W

2012-04-03

An automated fluidic instrument is described that rapidly determines the total (99)Tc content of aged nuclear waste samples, where the matrix is chemically and radiologically complex and the existing speciation of the (99)Tc is variable. The monitor links microwave-assisted sample preparation with an automated anion exchange column separation and detection using a flow-through solid scintillator detector. The sample preparation steps acidify the sample, decompose organics, and convert all Tc species to the pertechnetate anion. The column-based anion exchange procedure separates the pertechnetate from the complex sample matrix, so that radiometric detection can provide accurate measurement of (99)Tc. We developed a preprogrammed spike addition procedure to automatically determine matrix-matched calibration. The overall measurement efficiency that is determined simultaneously provides a self-diagnostic parameter for the radiochemical separation and overall instrument function. Continuous, automated operation was demonstrated over the course of 54 h, which resulted in the analysis of 215 samples plus 54 hly spike-addition samples, with consistent overall measurement efficiency for the operation of the monitor. A sample can be processed and measured automatically in just 12.5 min with a detection limit of 23.5 Bq/mL of (99)Tc in low activity waste (0.495 mL sample volume), with better than 10% RSD precision at concentrations above the quantification limit. This rapid automated analysis method was developed to support nuclear waste processing operations planned for the Hanford nuclear site.

Does smart home technology prevent falls in community-dwelling older adults: a literature review.

Science.gov (United States)

Pietrzak, Eva; Cotea, Cristina; Pullman, Stephen

2014-01-01

Falls in older Australians are an increasingly costly public health issue, driving the development of novel modes of intervention, especially those that rely on computer-driven technologies. The aim of this paper was to gain an understanding of the state of the art of research on smart homes and computer-based monitoring technologies to prevent and detect falls in the community-dwelling elderly. Cochrane, Medline, Embase and Google databases were searched for articles on fall prevention in the elderly using pre-specified search terms. Additional papers were searched for in the reference lists of relevant reviews and by the process of 'snowballing'. Only studies that investigated outcomes related to falling such as fall prevention and detection, change in participants' fear of falling and attitudes towards monitoring technology were included. Nine papers fulfilled the inclusion criteria. The following outcomes were observed: (1) older adults' attitudes towards fall detectors and smart home technology are generally positive; (2) privacy concerns and intrusiveness of technology were perceived as less important to participants than their perception of health needs and (3) unfriendly and age-inappropriate design of the interface may be one of the deciding factors in not using the technology. So far, there is little evidence that using smart home technology may assist in fall prevention or detection, but there are some indications that it may increase older adults' confidence and sense of security, thus possibly enabling aging in place.

Mitigating fall risk: A community fall reduction program.

Science.gov (United States)

Reinoso, Humberto; McCaffrey, Ruth G; Taylor, David W M

One fourth of all American's over 65 years of age fall each year. Falls are a common and often devastating event that can pose a serious health risk for older adults. Healthcare providers are often unable to spend the time required to assist older adults with fall risk issues. Without a team approach to fall prevention the system remains focused on fragmented levels of health promotion and risk prevention. The specific aim of this project was to engage older adults from the community in a fall risk assessment program, using the Stopping Elderly Accidents, Deaths & Injuries (STEADI) program, and provide feedback on individual participants' risks that participants could share with their primary care physician. Older adults who attended the risk screening were taking medications that are known to increase falls. They mentioned that their health care providers do not screen for falls and appreciated a community based screening. Copyright © 2017 Elsevier Inc. All rights reserved.

Person-Centered Fall Risk Awareness Perspectives: Clinical Correlates and Fall Risk.

Science.gov (United States)

Verghese, Joe

2016-12-01

To identify clinical correlates of person-centered fall risk awareness and their validity for predicting falls. Prospective cohort study. Community. Ambulatory community-dwelling older adults without dementia (N = 316; mean age 78, 55% female). Fall risk awareness was assessed using a two-item questionnaire that asked participants about overall likelihood of someone in their age group having a fall and their own personal risk of falling over the next 12 months. Incident falls were recorded over study follow-up. Fifty-three participants (16.8%) responded positively to the first fall risk awareness question about being likely to have a fall in the next 12 months, and 100 (31.6%) reported being at personal risk of falling over the next 12 months. There was only fair correlation (κ = 0.370) between responses on the two questions. Prior falls and depressive symptoms were associated with positive responses on both fall risk awareness questions. Age and other established fall risk factors were not associated with responses on either fall risk awareness question. The fall risk awareness questionnaire did not predict incident falls or injurious falls. Fall risk awareness is low in older adults. Although person-centered fall risk awareness is not predictive of falls, subjective risk perceptions should be considered when designing fall preventive strategies because they may influence participation and behaviors. © 2016, Copyright the Author Journal compilation © 2016, The American Geriatrics Society.

A graphical automated detection system to locate hardwood log surface defects using high-resolution three-dimensional laser scan data

Science.gov (United States)

Liya Thomas; R. Edward. Thomas

2011-01-01

We have developed an automated defect detection system and a state-of-the-art Graphic User Interface (GUI) for hardwood logs. The algorithm identifies defects at least 0.5 inch high and at least 3 inches in diameter on barked hardwood log and stem surfaces. To summarize defect features and to build a knowledge base, hundreds of defects were measured, photographed, and...

Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

Science.gov (United States)

Joshi, Vinayak; Agurto, Carla; Barriga, Simon; Nemeth, Sheila; Soliz, Peter; MacCormick, Ian J.; Lewallen, Susan; Taylor, Terrie E.; Harding, Simon P.

2017-02-01

Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis.

Automated detection of pain from facial expressions: a rule-based approach using AAM

Science.gov (United States)

Chen, Zhanli; Ansari, Rashid; Wilkie, Diana J.

2012-02-01

In this paper, we examine the problem of using video analysis to assess pain, an important problem especially for critically ill, non-communicative patients, and people with dementia. We propose and evaluate an automated method to detect the presence of pain manifested in patient videos using a unique and large collection of cancer patient videos captured in patient homes. The method is based on detecting pain-related facial action units defined in the Facial Action Coding System (FACS) that is widely used for objective assessment in pain analysis. In our research, a person-specific Active Appearance Model (AAM) based on Project-Out Inverse Compositional Method is trained for each patient individually for the modeling purpose. A flexible representation of the shape model is used in a rule-based method that is better suited than the more commonly used classifier-based methods for application to the cancer patient videos in which pain-related facial actions occur infrequently and more subtly. The rule-based method relies on the feature points that provide facial action cues and is extracted from the shape vertices of AAM, which have a natural correspondence to face muscular movement. In this paper, we investigate the detection of a commonly used set of pain-related action units in both the upper and lower face. Our detection results show good agreement with the results obtained by three trained FACS coders who independently reviewed and scored the action units in the cancer patient videos.

Utilizing Weather RADAR for Rapid Location of Meteorite Falls and Space Debris Re-Entry

Science.gov (United States)

Fries, Marc D.

2016-01-01

This activity utilizes existing NOAA weather RADAR imagery to locate meteorite falls and space debris falls. The near-real-time availability and spatial accuracy of these data allow rapid recovery of material from both meteorite falls and space debris re-entry events. To date, at least 22 meteorite fall recoveries have benefitted from RADAR detection and fall modeling, and multiple debris re-entry events over the United States have been observed in unprecedented detail.

Automated Ground Penetrating Radar hyperbola detection in complex environment

Science.gov (United States)

Mertens, Laurence; Lambot, Sébastien

2015-04-01

Ground Penetrating Radar (GPR) systems are commonly used in many applications to detect, amongst others, buried targets (various types of pipes, landmines, tree roots ...), which, in a cross-section, present theoretically a particular hyperbolic-shaped signature resulting from the antenna radiation pattern. Considering the large quantity of information we can acquire during a field campaign, a manual detection of these hyperbolas is barely possible, therefore we have a real need to have at our disposal a quick and automated detection of these hyperbolas. However, this task may reveal itself laborious in real field data because these hyperbolas are often ill-shaped due to the heterogeneity of the medium and to instrumentation clutter. We propose a new detection algorithm for well- and ill-shaped GPR reflection hyperbolas especially developed for complex field data. This algorithm is based on human recognition pattern to emulate human expertise to identify the hyperbolas apexes. The main principle relies in a fitting process of the GPR image edge dots detected with Canny filter to analytical hyperbolas, considering the object as a punctual disturbance with a physical constraint of the parameters. A long phase of observation of a large number of ill-shaped hyperbolas in various complex media led to the definition of smart criteria characterizing the hyperbolic shape and to the choice of accepted value ranges acceptable for an edge dot to correspond to the apex of a specific hyperbola. These values were defined to fit the ambiguity zone for the human brain and present the particularity of being functional in most heterogeneous media. Furthermore, the irregularity is particularly taken into account by defining a buffer zone around the theoretical hyperbola in which the edge dots need to be encountered to belong to this specific hyperbola. First, the method was tested in laboratory conditions over tree roots and over PVC pipes with both time- and frequency-domain radars

Falls and Fear of Falling After Stroke: A Case-Control Study.

Science.gov (United States)

Goh, Hui-Ting; Nadarajah, Mohanasuntharaam; Hamzah, Norhamizan Binti; Varadan, Parimalaganthi; Tan, Maw Pin

2016-12-01

Falls are common after stroke, with potentially serious consequences. Few investigations have included age-matched control participants to directly compare fall characteristics between older adults with and without stroke. Further, fear of falling, a significant psychological consequence of falls, has only been examined to a limited degree as a risk factor for future falls in a stroke population. To compare the fall history between older adults with and without a previous stroke and to identify the determinants of falls and fear of falling in older stroke survivors. Case-control observational study. Primary teaching hospital. Seventy-five patients with stroke (mean age ± standard deviation, 66 ± 7 years) and 50 age-matched control participants with no previous stroke were tested. Fall history, fear of falling, and physical, cognitive, and psychological function were assessed. A χ 2 test was performed to compare characteristics between groups, and logistic regression was performed to determine the risk factors for falls and fear of falling. Fall events in the past 12 months, Fall Efficacy Scale-International, Berg Balance Scale, Functional Ambulation Category, Fatigue Severity Scale, Montreal Cognitive Assessment, and Patient Healthy Questionnaire-9 were measured for all participants. Fugl-Meyer Motor Assessment was used to quantify severity of stroke motor impairments. Twenty-three patients and 13 control participants reported at least one fall in the past 12 months (P = .58). Nine participants with stroke had recurrent falls (≥2 falls) compared with none of the control participants (P falling than did nonstroke control participants (P falls in the nonstroke group, whereas falls in the stroke group were not significantly associated with any measured outcomes. Fear of falling in the stroke group was associated with functional ambulation level and balance. Functional ambulation level alone explained 22% of variance in fear of falling in the stroke group

Are triage questions sufficient to assign fall risk precautions in the ED?

Science.gov (United States)

Southerland, Lauren T; Slattery, Lauren; Rosenthal, Joseph A; Kegelmeyer, Deborah; Kloos, Anne

2017-02-01

The American College of Emergency Physicians Geriatric Emergency Department (ED) Guidelines and the Center for Disease Control recommend that older adults be assessed for risk of falls. The standard ED assessment is a verbal query of fall risk factors, which may be inadequate. We hypothesized that the addition of a functional balance test endorsed by the Center for Disease Control Stop Elderly Accidents, Deaths, and Injuries Falls Prevention Guidelines, the 4-Stage Balance Test (4SBT), would improve the detection of patients at risk for falls. Prospective pilot study of a convenience sample of ambulatory adults 65 years and older in the ED. All participants received the standard nursing triage fall risk assessment. After patients were stabilized in their ED room, the 4SBT was administered. The 58 participants had an average age of 74.1 years (range, 65-94), 40.0% were women, and 98% were community dwelling. Five (8.6%) presented to the ED for a fall-related chief complaint. The nursing triage screen identified 39.7% (n=23) as at risk for falls, whereas the 4SBT identified 43% (n=25). Combining triage questions with the 4SBT identified 60.3% (n=35) as at high risk for falls, as compared with 39.7% (n=23) with triage questions alone (Ppatients at high risk by 4SBT and missed by triage questions were inpatients unaware that they were at risk for falls (new diagnoses). Incorporating a quick functional test of balance into the ED assessment for fall risk is feasible and significantly increases the detection of older adults at risk for falls. Copyright © 2016 Elsevier Inc. All rights reserved.

Getting Students Familiar with the Use of Computers: Study of the Falling of a Body in a Fluid.

Science.gov (United States)

Guisasola, J.; Barragues, J. I.; Valdes, P.; Pedroso, F.

1999-01-01

Describes changes in scientific research methods that have been brought about by the use of computers. Presents an example of the falling of a body in a fluid to show students how computers can be used to experiment with mathematical models and to automate experiments. Contains 11 references. (Author/WRM)