基于多路sEMG时序分析的人体运动模式识别方法

佟丽娜; 侯增广; 彭亮; 王卫群; 陈翼雄; 谭民

doi:10.3724/SP.J.1004.2014.00810

基于多路sEMG时序分析的人体运动模式识别方法

doi: 10.3724/SP.J.1004.2014.00810

1.
中国科学院自动化研究所复杂系统与管理国家重点实验室, 北京 100190;
2.
中国矿业大学 (北京)机电与信息工程学院, 北京 100083

基金项目:

国家自然科学基金（61305140，61225017，61175076，61203342），国家国际科技合作专项（2011DFG13390）资助

详细信息

作者简介:
佟丽娜中国科学院自动化研究所复杂系统管理与控制国家重点实验室控制科学与工程专业博士后，中国矿业大学(北京) 机电与信息工程学院讲师. 2011 年取得中国科学技术大学控制科学与工程博士学位. 主要研究方向为机器人与人机交互. E-mail：lina.tong@ia.ac.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2013-03-26
- 修回日期: 2013-11-05
- 刊出日期: 2014-05-20

Multi-channel sEMG Time Series Analysis Based Human Motion Recognition Method

1.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190;
2.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083

Funds:

Supported by National Natural Science Foundation of China (61305140, 61225017, 61175076, 61203342) and the International S & T Cooperation Project of China (2011DFG13390)

摘要

摘要: 针对主动康复训练中人体运动识别问题，提出了一种基于多路表面肌电（Surface electro myo gram，sEMG）时序特征的人体运动模式识别方法.设计评估类周期sEMG信号波形相似度的方法来对多路sEMG信号进行特征选择；以二维科荷伦自组织竞争网络（Self-organizati on mappingnet，SOM）对多路信息进行编码；最后，建立描述各运动过程多路sEMG时序特征的隐马尔科夫模型（Hidden Markov model，HMM），基于最大似然估计法对多模型匹配进行综合判决获取识别结果.并在对下肢踏车、椭圆、步行运动模式的识别实验中，相对于经典线性及非线性算法，识别率由72.5%和88.33%提高到91.67%，验证了本文方法的有效性.
- 康复训练 /
- 表面肌电 /
- 人体运动意图识别 /
- 自组织神经网络 /
- 隐马尔科夫模型
Abstract: Towards human motion intention recognition during active rehabilitation, a multi-channel suface electromyogram (sEMG) time series based human motion pattern recognition method is proposed. An evaluation method for sEMG signal waveform similarity is designed to select the features, which are coded by a 2D Kohonen self-organization mapping net (SOM) net to get feature series. At last, hidden Markov models (HMM) are built to describe the multi-channel sEMG time series features during each motion process, and then get recognition results based on maximum likelihood estimation method for multi-model synthesis decision. This method showed a good performance on real time and accuracy in the experiment: the treadmill, elliptical and walk training modes are identified by an accuracy of 91.67%, while the classical linear and nonlinear methods showed accuracies of 72.5% and 88.33%.
- Rehabilitation /
- surface electromyogram (sEMG) /
- human motion intention recognition /
- self-organization mapping net (SOM) /
- hidden Markov model (HMM)

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