DocumentCode :
734182
Title :
A robust gesture recognition algorithm based on surface EMG
Author :
Ke Lin ; Chaohua Wu ; Xiaoshan Huang ; Qiang Ding ; Xiaorong Gao
Author_Institution :
Tsinghua Univ., Beijing, China
fYear :
2015
fDate :
27-29 March 2015
Firstpage :
131
Lastpage :
136
Abstract :
This study researched a robust gesture recognition algorithm based on EMG. The proposed algorithm only needs very limited training data (1 or 2 training trials for each gesture). The contribution of the proposed algorithm was mainly three-fold. First, a shrinkage approach was applied to estimate the samples´ covariance matrix, which helped to improve the robustness of the algorithm. Second, to evaluate the system performance, classification accuracy and gesture number to be recognized was compromised using information transfer rate (ITR). We found a system which can recognize 10 gestures could achieve similar ITR as the system which can recognize 20 gestures. However, the 10-gesture system was more robust. Third, K-L divergence was used to evaluate the separability of the EMG signals from different gestures. The result of a 5 subject experiment showed that the classification accuracy of 10 gestures using 2 trials as training set can reach 85%.
Keywords :
covariance matrices; electromyography; gesture recognition; human computer interaction; interactive systems; signal classification; 10-gesture system; ITR; K-L divergence; classification accuracy; covariance matrix; information transfer rate; robust gesture recognition algorithm; surface EMG; Accuracy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2015 Seventh International Conference on
Conference_Location :
Wuyi
Print_ISBN :
978-1-4799-7257-9
Type :
conf
DOI :
10.1109/ICACI.2015.7184763
Filename :
7184763
Link To Document :
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