scholarly journals Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-18
Author(s):  
Jian Luo ◽  
Jin Tang ◽  
Xiaoming Xiao
Keyword(s):  
Large Scale ◽  
The Elderly ◽  
Aged Care ◽  
Abnormal Behavior ◽  
Svm Classifier ◽  
Learning Method ◽  
Time Frequency ◽  
Behavior Detection ◽  
Abnormal Gait ◽  
Incremental Svm

A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM) learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.

Telepresence Robots for Healthcare Management: COVID-19 Experience (Preprint)

2020 ◽  
Author(s):  
Pradeep MHealth For Belt And Road Region (mHBR) ◽  
Mingzhong Wang ◽  
Chongdan Pan
Keyword(s):  
Large Scale ◽  
Emotional Health ◽  
Low Cost ◽  
The Elderly ◽  
Aged Care ◽  
Healthcare Management ◽  
Healthcare Operations ◽  
Telepresence Robot ◽  
The World ◽  
Telepresence Robots

BACKGROUND Researchers have been investigating the use of robots in the world for elderly in various types of applications, such as communication with relatives and friends at a distance, transportation of medical supplies and equipment across healthcare/aged care facilities, surgical procedures etc. In China, ground zero of the COVID-19 outbreak, robots are being used in hospitals to deliver food and medication and take patients' temperatures. Drones are deployed to transport supplies, spray disinfectants and do thermal imaging. This paper will focus on telepresence robots that have become critically important to perform remote healthcare operations, complying with social distancing measures.UNSW and University of Sunshine Coast have been partners in the European Union VictoryaHome (VH) project (2014-2016) that involved Australia and EU countries Norway, Sweden, Netherlands and Portugal. The project was aimed at better emotional health of the elderly and the project identified some major problems, such as the high cost of robot and its high complexity, making their adoption difficult. This led to the project “Robots for Elderly” as part of the new “Robots for Elderly” project (involving Australia, China, Bangladesh and EU) in mHealth for Belt and Road (mHBR) Initiative led by the UM-SJTU Joint Institute in China from 2018. OBJECTIVE The aim of this study is to design, implement and test a low-cost telepresence robot for healthcare. The focus has been on implementing a low-cost telepresence robot for healthcare management for the elderly during pandemics like COVID-19. METHODS This project uses an innovative, multi-disciplinary collaboration across disciplines (software, electronics engineering, mechatronics and public health) involving young university talents from these fields. RESULTS According to preliminary customer feedback, the main functions have already been realized by our robot. The cost is approx. $500, about 20 times less expensive than the Giraff robot used in the VH project. CONCLUSIONS Many groups all over the world have been trying to develop low-cost robots for various applications. We addressed the needs for the healthcare of elderly, most affected by the Coronavirus and came up with a simple low-cost design of telepresence robot that can be deployed widely in hospitals and aged care establishments. The system is currently in a prototype level and will require an entrepreneur to commercialize it in large scale.

scholarly journals Deep learning assisted data inspection for radio astronomy

2020 ◽  
Vol 496 (2) ◽  
pp. 1517-1529
Author(s):  
Michael Mesarcik ◽  
Albert-Jan Boonstra ◽  
Christiaan Meijer ◽  
Walter Jansen ◽  
Elena Ranguelova ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Simulated Data ◽  
Qualitative Assessment ◽  
Svm Classifier ◽  
Prototype System ◽  
Radio Telescopes ◽  
Long Distance ◽  
Time Frequency ◽  
Unsupervised Deep Learning

ABSTRACT Modern radio telescopes combine thousands of receivers, long-distance networks, large-scale compute hardware, and intricate software. Due to this complexity, failures occur relatively frequently. In this work, we propose novel use of unsupervised deep learning to diagnose system health for modern radio telescopes. The model is a convolutional variational autoencoder (VAE) that enables the projection of the high-dimensional time–frequency data to a low-dimensional prescriptive space. Using this projection, telescope operators are able to visually inspect failures thereby maintaining system health. We have trained and evaluated the performance of the VAE quantitatively in controlled experiments on simulated data from HERA. Moreover, we present a qualitative assessment of the model trained and tested on real LOFAR data. Through the use of a naïve SVM classifier on the projected synthesized data, we show that there is a trade-off between the dimensionality of the projection and the number of compounded features in a given spectrogram. The VAE and SVM combination scores between 65 per cent and 90 per cent accuracy depending on the number of features in a given input. Finally, we show the prototype system-health-diagnostic web framework that integrates the evaluated model. The system is currently undergoing testing at the ASTRON observatory.

scholarly journals Multiobjects Association and Abnormal Behavior Detection for Massive Data Analysis in Multisensor Monitoring Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ke Bao ◽  
Yourong Ding
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Monitoring Network ◽  
Automatic Classification ◽  
Abnormal Behavior ◽  
Statistical Dependence ◽  
Massive Data ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

With the rapid increase in the number of large-scale distributed cameras and the rapid increase in the monitoring range of the camera network, how to accurately recognize and analyze abnormal behavior is still a challenging problem. In addition, the appearance of moving objects between different cameras without overlapping fields of view undergoes significant changes, making it difficult to obtain accurate association Therefore, multiobjects association and abnormal behavior detection for massive data analysis in multisensor monitoring network are proposed in this paper, which firstly uses belief propagation to associate multiple objects, extracts the object’s behavior trajectory characteristics, and then builds a long short-term memory classification network to realize automatic classification of abnormal behaviors. Multiobject association fully considers the timing correlation and object detection probability, as well as the statistical dependence of the measurement on the association matrix. The experimental results show that our proposed method can achieve a high classification accuracy and sensitivity, which meets the requirements of automatic classification of abnormal behavior in complex monitoring network. This further shows that this research has practical application value.

scholarly journals Cross-Sectional Association between Hypercholesterolemia and Knee Pain in the Elderly with Radiographic Knee Osteoarthritis: Data from the Korean National Health and Nutritional Examination Survey

2021 ◽  
Vol 10 (5) ◽  
pp. 933
Author(s):  
Byung Woo Cho ◽  
Du Seong Kim ◽  
Hyuck Min Kwon ◽  
Ick Hwan Yang ◽  
Woo-Suk Lee ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Pain ◽  
Logistic Regression Analysis ◽  
National Health ◽  
Large Scale ◽  
Metabolic Diseases ◽  
The Elderly ◽  
Cross Sectional ◽  
Ordinal Logistic Regression Analysis

Few studies have reported the relationship between knee pain and hypercholesterolemia in the elderly population with osteoarthritis (OA), independent of other variables. The aim of this study was to reveal the association between knee pain and metabolic diseases including hypercholesterolemia using a large-scale cohort. A cross-sectional study was conducted using data from the Korea National Health and the Nutrition Examination Survey (KNHANES-V, VI-1; 2010–2013). Among the subjects aged ≥60 years, 7438 subjects (weighted number estimate = 35,524,307) who replied knee pain item and performed the simple radiographs of knee were enrolled. Using multivariable ordinal logistic regression analysis, variables affecting knee pain were identified, and the odds ratio (OR) was calculated. Of the 35,524,307 subjects, 10,630,836 (29.9%) subjects experienced knee pain. Overall, 20,290,421 subjects (56.3%) had radiographic OA, and 8,119,372 (40.0%) of them complained of knee pain. Multivariable ordinal logistic regression analysis showed that among the metabolic diseases, only hypercholesterolemia was positively correlated with knee pain in the OA group (OR 1.24; 95% Confidence Interval 1.02–1.52, p = 0.033). There were no metabolic diseases correlated with knee pain in the non-OA group. This large-scale study revealed that in the elderly, hypercholesterolemia was positively associated with knee pain independent of body mass index and other metabolic diseases in the OA group, but not in the non-OA group. These results will help in understanding the nature of arthritic pain, and may support the need for exploring the longitudinal associations.

scholarly journals A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1635
Author(s):  
Neeraj Chugh ◽  
Geetam Singh Tomar ◽  
Robin Singh Bhadoria ◽  
Neetesh Saxena
Keyword(s):  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Mobile Ad Hoc Networks ◽  
Network Performance ◽  
Ad Hoc ◽  
Abnormal Behavior ◽  
Energy Awareness ◽  
Behavior Detection ◽  
Mobile Ad Hoc ◽  
Hoc Networks

To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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