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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time.

Zengyu Qing1, Zongxing Lu1, Yingjie Cai1

  • 1School of Mechanical Engineering and Automation, Fuzhou University, No.2 Xueyuan Road, Fuzhou 350116, China.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

Gesture decoding using surface Electromyography (sEMG) is impacted by muscle fatigue, forearm angle, and acquisition time. Acquisition time significantly reduces decoding accuracy, highlighting its importance for reliable control systems.

Keywords:
acquisition timeforearm anglegesture decodingmachine learningmuscle fatiguesurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Surface Electromyography (sEMG) signals are crucial for decoding human movement intentions.
  • sEMG-based control is widely used in robotics, prosthetics, and rehabilitation.
  • Factors affecting sEMG decoding accuracy require thorough investigation for improved system performance.

Purpose of the Study:

  • To investigate the impact of muscle fatigue, forearm angle, and acquisition time on sEMG-based gesture decoding accuracy.
  • To evaluate the performance of Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) models under varying conditions.
  • To identify the most influential factors affecting the reliability of sEMG gesture decoding.

Main Methods:

  • Selected 11 static gestures and sampled sEMG signals from four forearm muscles: SFD, FCU, ECRL, and FE.
  • Extracted signal eigenvalues including Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC), and Slope Sign Change (SSC).
  • Employed Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) for gesture classification.

Main Results:

  • Muscle fatigue, forearm angle, and acquisition time reduced decoding accuracy by an average of 7%, 10%, and 13%, respectively.
  • Acquisition time demonstrated the most significant impact, causing a maximum accuracy reduction of nearly 20%.
  • Both LDA and PNN models showed decreased performance with increased muscle fatigue, altered forearm angles, and longer acquisition times.

Conclusions:

  • Muscle fatigue, forearm angle, and acquisition time are critical factors influencing sEMG gesture decoding accuracy.
  • Acquisition time is the most detrimental factor, necessitating careful consideration in real-world applications.
  • Optimizing data acquisition protocols and developing robust algorithms are essential for enhancing the reliability of sEMG-controlled devices.