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[The difference of surface electromyography data processing method based on simulated manal-lifting-task].

Q Xu1, S W Zhong1, X Y Zhang1

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Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi = Zhonghua Laodong Weisheng Zhiyebing Zazhi = Chinese Journal of Industrial Hygiene and Occupational Diseases
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Summary
This summary is machine-generated.

Analyzing surface electromyography (sEMG) signal processing methods for muscle fatigue detection reveals significant differences. The "all signal" method, capturing data from start to end of motion, offers superior sensitivity and lower volatility for dynamic operations.

Keywords:
ElectromyographyManual lifting operationsMuscle fatigueSignal processing

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Area of Science:

  • Biomedical Engineering
  • Kinesiology
  • Signal Processing

Background:

  • Muscle fatigue assessment is crucial in various physical and occupational settings.
  • Surface electromyography (sEMG) is a common non-invasive technique for monitoring muscle activity.
  • Standardizing sEMG signal processing is essential for reliable muscle fatigue detection.

Purpose of the Study:

  • To compare the efficacy of different sEMG signal processing methods in identifying muscle fatigue.
  • To evaluate time-domain (RMS) and frequency-domain (MDF) indices across various processing techniques.
  • To determine the optimal sEMG processing method for dynamic and complex manual operations.

Main Methods:

  • Collected sEMG data from 13 volunteers during simulated manual lifting tasks.
  • Analyzed signals using three methods: 'all signal', 'peak signal', and 'specified motion signal'.
  • Applied time-domain (RMS) and frequency-domain (MDF) analyses, with statistical comparison using Wilcoxon rank and sum test and nonlinear curve fitting.

Main Results:

  • Statistically significant differences (P<0.016) were observed in RMS and MDF signals across processing methods for most muscles.
  • The 'all signal' processing method demonstrated better data distribution dispersion and a higher RMS signal slope rate of change.
  • Non-linear regression indicated lower data volatility and a high degree of fit for the 'all signal' method.

Conclusions:

  • Different sEMG signal processing methods yield distinct results in muscle fatigue assessment.
  • The 'all signal' processing method, encompassing the entire motion cycle, exhibits minimal data volatility.
  • This method provides heightened sensitivity for time-domain and frequency-domain indices, making it suitable for dynamic, complex operations.