基于足底压力信息的跌倒姿态聚类识别方法
为了进一步提高基于足底压力传感器的老年跌倒检测系统的识别率,以及准确地判断人体跌倒方向,提出了利用自组织映射神经网络（SOM）和足底压力传感信息对人体动作进行聚类分析的方法。为了验证SOM方法的识别效果,采取包含跌倒在内的13类常见动作的130个样本对训练好的SOM网络进行测试。测试结果表明,系统灵敏度、特异度及准确度分别为92.5%、93.3%、93.1%,其结果均优于常用的阈值法。综上,SOM方法对人体跌倒姿态识别具有较高的可靠性和准确度。...
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| Published in | 电子技术应用 Vol. 42; no. 5; pp. 113 - 115 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
桂林电子科技大学生命与环境科学学院,广西桂林,541004
2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0258-7998 |
| DOI | 10.16157/j.issn.0258-7998.2016.05.031 |
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| Abstract | 为了进一步提高基于足底压力传感器的老年跌倒检测系统的识别率,以及准确地判断人体跌倒方向,提出了利用自组织映射神经网络（SOM）和足底压力传感信息对人体动作进行聚类分析的方法。为了验证SOM方法的识别效果,采取包含跌倒在内的13类常见动作的130个样本对训练好的SOM网络进行测试。测试结果表明,系统灵敏度、特异度及准确度分别为92.5%、93.3%、93.1%,其结果均优于常用的阈值法。综上,SOM方法对人体跌倒姿态识别具有较高的可靠性和准确度。 |
|---|---|
| AbstractList | 为了进一步提高基于足底压力传感器的老年跌倒检测系统的识别率,以及准确地判断人体跌倒方向,提出了利用自组织映射神经网络（SOM）和足底压力传感信息对人体动作进行聚类分析的方法。为了验证SOM方法的识别效果,采取包含跌倒在内的13类常见动作的130个样本对训练好的SOM网络进行测试。测试结果表明,系统灵敏度、特异度及准确度分别为92.5%、93.3%、93.1%,其结果均优于常用的阈值法。综上,SOM方法对人体跌倒姿态识别具有较高的可靠性和准确度。 TM501; 为了进一步提高基于足底压力传感器的老年跌倒检测系统的识别率,以及准确地判断人体跌倒方向,提出了利用自组织映射神经网络(SOM)和足底压力传感信息对人体动作进行聚类分析的方法.为了验证SOM方法的识别效果,采取包含跌倒在内的13类常见动作的130个样本对训练好的SOM网络进行测试.测试结果表明,系统灵敏度、特异度及准确度分别为92.5％、93.3％、93.1％,其结果均优于常用的阈值法.综上,SOM方法对人体跌倒姿态识别具有较高的可靠性和准确度. |
| Author | 陈洪波 高青 冯涛 朱振朋 刘喻 |
| AuthorAffiliation | 桂林电子科技大学生命与环境科学学院,广西桂林541004 |
| AuthorAffiliation_xml | – name: 桂林电子科技大学生命与环境科学学院,广西桂林,541004 |
| Author_FL | Liu Yu Feng Tao Chen Hongbo Zhu Zhenpeng Gao Qing |
| Author_FL_xml | – sequence: 1 fullname: Chen Hongbo – sequence: 2 fullname: Gao Qing – sequence: 3 fullname: Feng Tao – sequence: 4 fullname: Zhu Zhenpeng – sequence: 5 fullname: Liu Yu |
| Author_xml | – sequence: 1 fullname: 陈洪波 高青 冯涛 朱振朋 刘喻 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | Clustering method for body falling gesture recognition based on sole pressure information |
| DocumentTitle_FL | Clustering method for body falling gesture recognition based on sole pressure information |
| EndPage | 115 |
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| Keywords | body gesture recognition Self-Organizing Map(SOM) neural network 足底压力传感信息 自组织映射神经网络 cluster analysis 聚类分析 人体跌倒姿态识别 sole pressure sensor |
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| Notes | In order to improve the performance of fall detection system for the elderly based on sole pressure sensor, and accurately to judge the fall direction of human body, a method was put forward based on self-organizing map neural network（ SOM） and the information of sole pressure sensor to cluster and analyze the human motion. To verify the recognition results of the SOM method, 130 samples of 13 common action including fall were participated in the SOM network testing. The results show that the sensitivity, specificity and accuracy of the new system were 92.5 %, 93.3 % and 93.1 % respectively. These results were better than those of the method of threshold value. 11-2305/TN Self-Organizing Map（SOM） neural network;cluster analysis;sole pressure sensor;body gesture recognition Chen Hongbo, Gao Qing, Feng Tao, Zhu Zhenpeng, Liu Yu （School of Life ＆ Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China） |
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| Snippet | 为了进一步提高基于足底压力传感器的老年跌倒检测系统的识别率,以及准确地判断人体跌倒方向,提出了利用自组织映射神经网络（SOM）和足底压力传感信息对人体动作进行聚类分... TM501; 为了进一步提高基于足底压力传感器的老年跌倒检测系统的识别率,以及准确地判断人体跌倒方向,提出了利用自组织映射神经网络(SOM)和足底压力传感信息对人体动作进行... |
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| SubjectTerms | 人体跌倒姿态识别 聚类分析 自组织映射神经网络 足底压力传感信息 |
| Title | 基于足底压力信息的跌倒姿态聚类识别方法 |
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