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International Conference on Intelligent Robotics and Applications

ICIRA 2011: Intelligent Robotics and Applications pp 212–220Cite as

Classification of the Action Surface EMG Signals Based on the Dirichlet Process Mixtures Method

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7101))

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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Author information

Authors and Affiliations

  1. Institute of Vibration, Shock and Noise, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 2000240, P.R. China

    Min Lei & Guang Meng

Authors
  1. Min Lei
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  2. Guang Meng
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Editor information

Editors and Affiliations

  1. Zentrum für Lern- und Wissensmanagement, Lehrstuhl für Informationsmanagement im Maschinenbau, RWTH Aachen, Dennewartstraße 27, 52068, Aachen, Germany

    Sabina Jeschke

  2. School of Creative Technologies, Intelligents Systems and Biomedical Robotics Group, University of Portsmouth, Eldon Building, Winston Churchill Avenue, PO1 3QL, Portsmouth, UK

    Honghai Liu

  3. ZLW/IMA, RWTH Aachen, Dennewartstraße 27, 52068, Aachen, Germany

    Daniel Schilberg

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© 2011 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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