摘要
针对表面肌电(SEMG)信号分解的获取波形模板困难问题,本文提出一种基于收缩力水平分级处理的分解方法,首先按不同肌肉收缩力水平采集SEMG信号,然后利用常规方法对最低收缩力下的SEMG信号进行分解,再利用其分解结果获得的波形模板信息和募集发放(IPI)信息对更高收缩力下的SEMG进行分解,以此逐级向上,从而实现对较高收缩力水平肌电(EMG)信号的分解。实验结果表明,上述方法可以在一定程度上解决较高收缩力条件下模板获取困难的问题,降低较高收缩力水平下EMG信号分解的复杂度,是一种行之有效的方法。
Aiming at the difficulty of surface eleetromyography （SEMG） signal decomposition, we in this paper pro posed a method of gradual processing based on contraction force level of muscle. At first, SEMG signals were recor ded at different levels of muscle contraction force. Then, the SEMG data recorded at minimum level of contraction force were decomposed adopting the conventional methods. Further, the data at higher level of contraction force was decomposed using the templates and interpulse interval （IPI） information resulted from the previous composition performed at lower level of contraction force. Such procedure was iteratively performed level by level until the SEMG data at the maximal level of contraction force were successfully decomposed. The experimental results showed that the proposed method was effective in decomposing SEMG data, offering a valuable solution to the difficulty in obtai ning templates at relatively high level of muscle contraction force. The complexity of SEMG decomposition in the case of high level of contraction force could also be reduced to a certain extent by using the proposed method.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2012年第6期1046-1051,1077,共7页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(30870656)
关键词
表面肌电
线性变化力
多通道
肌电分解
Surface electromyography （SEMG） Linear change force Multichannel EMG decomposition
