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International Conference on Wireless Mobile Communication and Healthcare

MobiHealth 2017: Wireless Mobile Communication and Healthcare pp 207–214Cite as

Discriminant Analysis Based EMG Pattern Recognition for Hand Function Rehabilitation

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Abstract

Electromyographic (EMG) signal is playing an important role on hand function training as a neuromuscular rehabilitation tool. Various pattern recognition algorithms (PRAs) have been compared and evaluated in previous research, and Linear Discriminant Analysis (LDA) showed the higher offline accuracy for motion classification. However, it is rarely of comparison for different types of Discriminant Analysis (DA), and the surface electrodes are common methods for signal acquisition. This paper proposes to evaluate the offline performance of LDA and other types of DA, and using Myo armband for recording signals. The offline data was acquired by Myo armband, processing recognizing the data in BioPatRec, an open source platform for motion classification and hand prosthetics control. From the results of average offline accuracy, training time, and testing time of the five types, LDA and Quadratic Discriminant Analysis (QDA) have the better performance than others, and LDA is the fastest algorithm with simple computing.

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Acknowledgments

The authors would like to thank all of the participants in this study and the open source research platform BioPatRec. The research work was supported by Fundamental Research Funds for the Central Universities, the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 51521064), and National Natural Science Foundation of China (Grant No. 51505190).

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Authors and Affiliations

  1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China

    Jia Deng & Geng Yang

  2. School of Information Science and Technology, Fudan University, Shanghai, China

    Jian Niu

  3. School of Mechanical Engineering, Jiangnan University, Wuxi, China

    Kun Wang

  4. Thin Film Electronics ASA, Linköping, Sweden

    Li Xie

Authors
  1. Jia Deng
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  2. Jian Niu
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  3. Kun Wang
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  4. Li Xie
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  5. Geng Yang
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Corresponding author

Correspondence to Geng Yang .

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Editors and Affiliations

  1. Polytechnic University of Milan, Milan, Italy

    Paolo Perego

  2. University of California, Irvine, California, USA

    Amir M. Rahmani

  3. ICT, Institute of Computer Technology, Vienna University of Technology, Vienna, Austria

    Nima TaheriNejad

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Deng, J., Niu, J., Wang, K., Xie, L., Yang, G. (2018). Discriminant Analysis Based EMG Pattern Recognition for Hand Function Rehabilitation. In: Perego, P., Rahmani, A., TaheriNejad, N. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-98551-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-98551-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98550-3

  • Online ISBN: 978-3-319-98551-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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