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Advances in Body Area Networks I pp 423–434Cite as

Voluntary EMG-to-Force Estimation in Shoulder and Elbow During the Movement of Feeding Oneself

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Abstract

Muscle force estimation opens up the possibility of objective evaluating human motion in both mechanical and physiological ways. This paper proposes an EMG-adjusted method to predict individual muscle force in the shoulder and elbow during a purposeful daily activity: feeding oneself. Two male subjects were asked to flex and extend their shoulders and elbows to simulate the movement of getting food from the pocket to the mouth. Three inertial sensors and six surface electromyography (sEMG) sensors were used to synchronously collect motion and sEMG data during the movement. A Hill-type musculotendon model was then employed to predict individual muscle force by the fusion of motion and adjusted sEMG data. The result shows that our method can predict individual muscle force accurately with the ability to cover subject-specific joint dynamics and neural control strategies in multi-joints movement.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61431017).

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Authors and Affiliations

  1. School of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 101408, China

    Jiateng Hou, Yingfei Sun, Lixin Sun, Bingyu Pan, Zhipei Huang & Jiankang Wu

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  1. Jiateng Hou
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  2. Yingfei Sun
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  3. Lixin Sun
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  4. Bingyu Pan
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  5. Zhipei Huang
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  6. Jiankang Wu
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Corresponding author

Correspondence to Zhipei Huang .

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Editors and Affiliations

  1. DIMES, Cubo 41C, Universita della Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, Liaoning, China

    Zhelong Wang

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Hou, J., Sun, Y., Sun, L., Pan, B., Huang, Z., Wu, J. (2019). Voluntary EMG-to-Force Estimation in Shoulder and Elbow During the Movement of Feeding Oneself. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_32

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  • DOI: https://doi.org/10.1007/978-3-030-02819-0_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02818-3

  • Online ISBN: 978-3-030-02819-0

  • eBook Packages: EngineeringEngineering (R0)

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