基于加速度时域特征的实时人体行为模式识别

上海交通大学学报（自然版） ›› 2015, Vol. 49 ›› Issue (02): 169-172.

• 自动化技术、计算机技术 • 上一篇下一篇

基于加速度时域特征的实时人体行为模式识别

刘宇，江宏毅，王仕亮，王伊冰，陈燕苹

(重庆邮电大学光电信息感测与传输技术重庆市重点实验室，重庆 400065)

收稿日期:2014-05-12 出版日期:2015-02-28 发布日期:2015-02-28
基金资助:
国家自然科学基金(51175535)，重庆市科委自然科学基金(CSTC2012jjB40009)，2013重庆高校创新团队建设计划(智慧医疗系统与核心技术创新团队)资助项目

Real-time Human Activity Pattern Recognition Based on Time Domain Features of Acceleration

LIU Yu,JIANG Hongyi,WANG Shiliang,WANG Yibing,CHEN Yanping

(Chongqing Municipal Level Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

Received:2014-05-12 Online:2015-02-28 Published:2015-02-28

摘要/Abstract

摘要：

摘要：针对高精度的实时人体行为模式识别，提出了一种基于加速度时域特征的行为模式识别算法.本算法选取时域特征作为唯一特征量，通过简化特征提取运算实现行为的实时识别，获得了高精度结果.通过在Android智能手机平台进行测试，每项动作识别正确率均可达80%以上.该算法相对于现有算法实时精度有明显提高，在手持终端领域具有较好的应用前景.

关键词: , 行为模式, 实时识别, 加速度, 时域特征

Abstract:

Abstract： An algorithm of activity pattern recognition based on the time domain feature of acceleration was proposed for realtime human activity pattern recognition with high accuracy. Time-domain analysis was used as the only method for extracting features. Computation of feature extraction was simplified to achieve realtime recognition of activity, and the ideal result with high accuracy was acquired at the same time. The data tests on Android smart phone indicate that the average accuracy of realtime activity recognition is above 80%. The result proves that this algorithm has a much more high accuracy of realtime recognition compared with the existing algorithms, which has great application prospect in hand-held terminal areas.

Key words: activity pattern, real-time recognition, acceleration, time domain features

中图分类号:

TP 391.4

刘宇，江宏毅，王仕亮，王伊冰，陈燕苹. 基于加速度时域特征的实时人体行为模式识别[J]. 上海交通大学学报（自然版）, 2015, 49(02): 169-172.

LIU Yu,JIANG Hongyi,WANG Shiliang,WANG Yibing,CHEN Yanping. Real-time Human Activity Pattern Recognition Based on Time Domain Features of Acceleration[J]. Journal of Shanghai Jiaotong University, 2015, 49(02): 169-172.

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基于加速度时域特征的实时人体行为模式识别

Real-time Human Activity Pattern Recognition Based on Time Domain Features of Acceleration

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