Classification of the action surface EMG signals based on the dirichlet process mixtures method

Authors:
Min Lei;Guang Meng
Affiliations:
Institute of Vibration, Shock and Noise, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, P.R. China;Institute of Vibration, Shock and Noise, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Year:
2011

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Abstract

This paper proposes a new classification method based on Dirichlet process mixtures(DPM) to investigate the classification of the four actions from the action surface EMG(ASEMG) signals. This method first builds a classification model of the data by using the multinomial logit model (MNL). Then a classifier is given by using the classification information of training data. For the features of ASEMG, we use a combined method of the empirical mode decomposition(EMD), Largest Lyapunov exponent and Linear discriminant analysis(LDA) dimension reduction. The highest average classification accuracy rates are over 90%. The results indicate that this classification method could be applied the classification of the ASEMG signals.