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

  • Jiateng Hou
  • Yingfei Sun
  • Lixin Sun
  • Bingyu Pan
  • Zhipei Huang
  • Jiankang Wu
Conference paper
Part of the Internet of Things book series (ITTCC)

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.

Keywords

Hill-type musculotendon model Surface electromyography (sEMG) Shoulder Elbow Muscle force 

Notes

Acknowledgements

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiateng Hou
    • 1
  • Yingfei Sun
    • 1
  • Lixin Sun
    • 1
  • Bingyu Pan
    • 1
  • Zhipei Huang
    • 1
  • Jiankang Wu
    • 1
  1. 1.School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina

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