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Title:
a fall detection algorithm based on pattern recognition and human posture analysis
Author: Cheng Huang ; Luo Haiyong ; Zhao Fang
Source: IET Conference Publications
Conference Name: IET International Conference on Communication Technology and Application, ICCTA 2011
Conference Date: October 14, 2011 - October 16, 2011
Issued Date: 2012
Conference Place: Beijing, China
Keyword: Algorithms ; Communication ; Data mining ; Health care ; Pattern recognition ; Sensor networks ; Signal detection ; Support vector machines
Indexed Type: EI
ISBN: 9781849194709
Department: (1) Software School Beijing University of Posts and Telecommunications Beijing 100876 China; (2) Institute of Computing Technology Chinese Academy of Sciences Beijing 100876 China
Abstract: Detecting fall is a particular important task in security monitoring and healthcare applications of sensor networks. However traditional approaches suffer from either a high false positive rate or high false negative rate, especially when the collected sensor data are unbalanced. Therefore, there is a lack of tradeoff between false alarms and misses for many traditional data mining methods to be applied. To solve this problem a novel fall detection algorithm based on pattern recognition and human posture analysis is presented in this paper. It firstly extracts thirty temporal features from the original data traces for different length adaptation of samples, and then exploits Hidden Markov Model (HMM) to filter the noisy character data and reduce the dimension of feature vectors. After that, it performs a closer classification with one-class Support Vector Machine (OCSVM) to filter the high false positive samples, and finally applies posture analysis to counteract the effects of high false negative samples until a satisfying accuracy is achieved. Simulation with real data demonstrates that the proposed algorithm outperforms other existing approaches.
English Abstract: Detecting fall is a particular important task in security monitoring and healthcare applications of sensor networks. However traditional approaches suffer from either a high false positive rate or high false negative rate, especially when the collected sensor data are unbalanced. Therefore, there is a lack of tradeoff between false alarms and misses for many traditional data mining methods to be applied. To solve this problem a novel fall detection algorithm based on pattern recognition and human posture analysis is presented in this paper. It firstly extracts thirty temporal features from the original data traces for different length adaptation of samples, and then exploits Hidden Markov Model (HMM) to filter the noisy character data and reduce the dimension of feature vectors. After that, it performs a closer classification with one-class Support Vector Machine (OCSVM) to filter the high false positive samples, and finally applies posture analysis to counteract the effects of high false negative samples until a satisfying accuracy is achieved. Simulation with real data demonstrates that the proposed algorithm outperforms other existing approaches.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15965
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Cheng Huang,Luo Haiyong,Zhao Fang. a fall detection algorithm based on pattern recognition and human posture analysis[C]. 见:IET International Conference on Communication Technology and Application, ICCTA 2011. Beijing, China. October 14, 2011 - October 16, 2011.
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