%0 Journal Article
%T SURFACE EMG SIGNAL CLASSIFICATON METHOD BASEDON COMPLEXITY MEASURE<br>基于复杂性度量的表面肌电信号分类方法
%A CAI Li-yu
%A WANG Zhi-zhong
%A ZHANG Hai-hong
%A <br>蔡立羽
%A 王志中
%A 张海虹
%J 生物物理学报
%D 2000
%I 
%X The feasibility of using complexity measure as surface EMG signal feature for motion classification was explored in this paper. By constructing feature vectors from complexity measures extracted from raw EMG data, four kinds of forearm motions were identified with a high accuracy. Experimental results proved that this measure, having a simple algorithm, is suitable for short data sets and capable of real time processing. It provides a new way for prothesis control and pathological diagnosis.
%K Complexity measure
%K EMG
%K Pattern recognition<br>复杂性测度
%K 肌电
%K 模式识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=E131DF24C5FCA6F4&yid=9806D0D4EAA9BED3&vid=7801E6FC5AE9020C&iid=CA4FD0336C81A37A&sid=EFD65B51496FB200&eid=2F56B21F91C9B05B&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=2&reference_num=6