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Robust EMG Pattern Recognition with Electrode Donning/Doffing and Multiple Confounding Factors

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Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10464))

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Abstract

Traditional electromyography (EMG) pattern recognition did not take into account confounding factors such as electrode shifting, force variation, limb posture, etc., which lead to a great gap between academic research and clinical practice. In this paper, we investigated the robustness of EMG pattern recognition under conditions of electrode shifting, force varying, limb posture changing, and dominant/non-dominant hand switching. In feature extraction, we proposed a method for threshold optimization based on Particle Swarm Optimization (PSO). Compared with the traditional trail & error method, it can largely increase the classification accuracy (CA) by 10.2%. In addition, the hybrid features integrated with discrete Fourier transform (DFT), wavelet transform (WT), and wavelet packet transform (WPT) were proposed, which increased the CA by 30.5%, 25.4%, 22.9%, respectively. We introduced probabilistic neural network (PNN) as a new classifier for EMG pattern recognition, and reported the CA’s obtained by a large variety of features and classifiers. The results showed that the combination of DFT_MAV2 (a novel feature based on DFT) and PNN reached the best CA (45.5%, 14 motions, validated on different hands without re-training).

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Acknowledgments

The authors would like to thank all the subjects participated in the experiments for their generous cooperation. The authors also appreciate the help of Qi Huang, Wei Yang, and Yuan Liu for their help in the experiments and paper drafting. This work is partially supported by the National Natural Science Foundation of China (No. 51675123, No. 61603112) and the Self-Planned Task of State Key Laboratory of Robotics and System (No. SKLRS201603B).

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Authors and Affiliations

  1. State Key of Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, People’s Republic of China

    Huajie Zhang, Dapeng Yang, Chunyuan Shi, Li Jiang & Hong Liu

Authors
  1. Huajie Zhang
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  2. Dapeng Yang
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  3. Chunyuan Shi
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  4. Li Jiang
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  5. Hong Liu
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Corresponding author

Correspondence to Dapeng Yang .

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Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

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Zhang, H., Yang, D., Shi, C., Jiang, L., Liu, H. (2017). Robust EMG Pattern Recognition with Electrode Donning/Doffing and Multiple Confounding Factors. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_38

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  • DOI: https://doi.org/10.1007/978-3-319-65298-6_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65297-9

  • Online ISBN: 978-3-319-65298-6

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