中文摘要：

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 electromyogram，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-organization 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．

英文摘要：

Towards human motion intention recognition during active rehabilitation, a multi-channel suface electromyo- gram （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 a~curacy 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 %.