An user-independent gesture recognition method based on sEMG decomposition
Anbin Xiong, Xingang Zhao, Jianda Han, Guangjun Liu, Qichuan Ding
Abstract
sEMG recognition has been used extensively in prosthetic device control, human-assisting manipulators and sign language recognition, etc. However, the sEMG recognition model, trained with one subject's sEMG data, is not applicable to the other subjects, which hinders the practical application of myoelectric interfaces immensely. In this paper, a sEMG recognition method which is applicable to multi-users is proposed. Firstly, single channel sEMG is decomposed into 30 MUAPTs, which includes four steps: two-order differential filter, threshold calculation, spike detection and hierarchical clustering. Secondly, the MUAPTs are updated with the templates orthogonalization; and Deep Boltzman Machine is employed to classify the MUAPTs into five classes corresponding to the predefined five gestures. Six participants participated in this experiment to validate the effectiveness of the proposed method. Results indicated that this method can achieve a mean accuracy of 81.5%.
BibTeX
@inproceedings{iros2015_anuserindependen,
title = {An user-independent gesture recognition method based on sEMG decomposition},
author = {Anbin Xiong and Xingang Zhao and Jianda Han and Guangjun Liu and Qichuan Ding},
booktitle = {IROS 2015},
year = {2015}
}