ISCAS OpenIR
a fall detection algorithm based on pattern recognition and human posture analysis
Cheng Huang; Luo Haiyong; Zhao Fang
2012
Conference NameIET International Conference on Communication Technology and Application, ICCTA 2011
SourceIET Conference Publications
Pages853-857
Conference DateOctober 14, 2011 - October 16, 2011
Conference PlaceBeijing, China
Indexed TypeEI
ISBN9781849194709
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
English AbstractDetecting 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.; 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.
KeywordAlgorithms Communication Data Mining Health Care Pattern Recognition Sensor Networks Signal Detection Support Vector Machines
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/15965
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Cheng Huang,Luo Haiyong,Zhao Fang. a fall detection algorithm based on pattern recognition and human posture analysis[C],2012:853-857.
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