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.
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Lei, M., Meng, G. (2011). Classification of the Action Surface EMG Signals Based on the Dirichlet Process Mixtures Method. In: Jeschke, S., Liu, H., Schilberg, D. (eds) Intelligent Robotics and Applications. ICIRA 2011. Lecture Notes in Computer Science(), vol 7101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25486-4_22
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DOI: https://doi.org/10.1007/978-3-642-25486-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25485-7
Online ISBN: 978-3-642-25486-4
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