a fall detection algorithm based on pattern recognition and human posture analysis
文献类型：会议论文
| 作者 | Cheng Huang ; Luo Haiyong ; Zhao Fang |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：软件研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。