EMG Signals Based Gait Phases Recognition Using Hidden Markov Models

摘要：

The application of hidden Markov model (HMM) to recognize gait phase using electromyographic (EMG) signals is described. Four time-domain features are extracted within a time segment of each channel of EMG signals to preserve pattern structure. According to the division of the gait cycle, the structure of HMM is determined, in which each state is associated with a gait phase. A modified Baum-Welch algorithm is used to estimate the parameter of HMM. And Viterbi algorithm achieves the phase recognition by finding the best state sequence to assign corresponding phases to the given segments. The feature set and data segmentation manner yielded high rate of accuracy are ascertained through evaluation experiments.

关键词： EMG signals HMM gait phase recognition data segmentation

作者: Ming Meng Qingshan She Yunyuan Gao Zhizeng Luo

作者单位: Institute of Intelligent Control and Robotics Hangzhou Dianzi UniversityHangzhou,Zhejiang Province,C Institute of Intelligent Control and Robotics Hangzhou Dianzi University Hangzhou,Zhejiang Province,

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-5

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）