a fall detection algorithm based on pattern recognition and human posture analysis

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	a fall detection algorithm based on pattern recognition and human posture analysis
	Cheng Huang ; Luo Haiyong ; Zhao Fang
	2012
会议名称	IET International Conference on Communication Technology and Application, ICCTA 2011
会议日期	October 14, 2011 - October 16, 2011
会议地点	Beijing, China
关键词	Algorithms Communication Data mining Health care Pattern recognition Sensor networks Signal detection Support vector machines
页码	853-857
中文摘要	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.
英文摘要	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.
收录类别	EI
会议录	IET Conference Publications
语种	英语
ISBN号	9781849194709
内容类型	会议论文
源URL	[http://ir.iscas.ac.cn/handle/311060/15965]
专题	软件研究所_软件所图书馆_会议论文
推荐引用方式 GB/T 7714	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.