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sEMG Feature Optimization Strategy for Finger Grip Force Estimation

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Intelligent Robotics and Applications (ICIRA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12595))

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Abstract

Finger Grip force estimation based on sEMG plays an important role in dexterous control of a prosthetic hand. In order to obtain higher estimation accuracy, one of the commonly used methods is to extract more features from sEMG and input them into the regression model. This practice results in a large amount of computation and is not suitable for practical use in low cost commercial prosthetic hand. In this paper, a sEMG feature optimization strategy for thumb-index finger grip force estimation is proposed with the purpose that using less features to achieve higher estimation accuracy. Four time-domain features are extracted from raw sEMG signals which captured from four muscle surfaces of the subject’s forearm. GRNN is employed to realize the estimation of the finger grip force. RMS and MAE are adopted to validate the performance of estimation. The effects of different feature sets on the estimation performances are evaluated by ANOVA. The results show that sEMG features have a significant influence on the grip force estimation results. The optimal feature combination is VZ, which provides an accuracy of 1.13N RMS and 0.85N MAE.

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Acknowledgement

This paper is supported by the National Natural Science Foundation of China under Grant No. 61803201, 91648206. Jiangsu Natural Science Foundation under Grant No.BK20170803. The China Postdoctoral Science Foundation under Grant No.2019M661686.

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Changcheng Wu, Fei Fei & Dehua Yang

  2. School of Aviation Engineering, Nanjing Vocational University of Industry Technology, Nanjing, China

    Qingqing Cao

  3. School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Changcheng Wu, Baoguo Xu, Hong Zeng & Aiguo Song

Authors
  1. Changcheng Wu
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  2. Qingqing Cao
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  3. Fei Fei
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  4. Dehua Yang
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  5. Baoguo Xu
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  6. Hong Zeng
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  7. Aiguo Song
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Corresponding author

Correspondence to Changcheng Wu .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Chee Seng Chan

  2. Harbin Institute of Technology, Harbin, China

    Hong Liu

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Monash University, Selangor, Malaysia

    Chern Hong Lim

  5. Tsinghua University, Beijing, China

    Xinjun Liu

  6. Shenyang Institute of Automation, Shenyang, China

    Lianqing Liu

  7. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Kam Meng Goh

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Wu, C. et al. (2020). sEMG Feature Optimization Strategy for Finger Grip Force Estimation. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_16

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  • DOI: https://doi.org/10.1007/978-3-030-66645-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66644-6

  • Online ISBN: 978-3-030-66645-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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