Huang, HuaiqiLi, TaoBruschini, ClaudioEnz, ChristianKoch, Volker M.Justiz, JornAntfolk, Christian2017-02-172017-02-172017-02-17201610.1109/BIOROB.2016.7523810https://infoscience.epfl.ch/handle/20.500.14299/134374WOS:000392266900221To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. This paper evaluates two types of time-domain EMG features, one independent feature and one combined feature including four features. The selected features from eight subjects with 13 finger movements were tested with four decomposed multi-class support vector machines (SVM), four decomposed linear discriminant analyses (LDA) and a multi-class LDA. The classification accuracy, training, and classification time are compared. The results have shown that the combined features decrease error rate, and binary tree based decomposition multi-class classifiers yield the highest classification success rate (88.2%) with relatively low training and classification time.text::conference output::conference proceedings::conference paper