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EMG Processing Based Measures of Fatigue Assessment during Manual Lifting
E F Shair1, S A Ahmad2, M H Marhaban2
1Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Selangor, Malaysia; Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia.
Biomed Research International
|March 18, 2017
Summary
Electromyography (EMG) signals help assess worker fatigue during manual lifting tasks. This review explores EMG processing techniques to prevent musculoskeletal disorders caused by overexertion in industrial settings.
Area of Science:
- Occupational Safety and Health
- Biomedical Engineering
- Ergonomics
Background:
- Manual lifting remains a common industrial practice despite available mechanization.
- Improper manual lifting techniques are a leading cause of musculoskeletal disorders (MSDs) due to overexertion.
- Monitoring worker muscle condition is crucial for preventing fatigue and injury.
Purpose of the Study:
- To review electromyography (EMG) signal processing techniques for fatigue assessment in manual lifting.
- To provide an overview of biosignal processing methods applicable to lifting tasks.
- To guide researchers in selecting optimal EMG processing techniques for industrial applications.
Main Methods:
- Review of existing literature on EMG signal processing for fatigue detection.
- Analysis of various EMG features in time, frequency, and time-frequency domains.
- Evaluation of EMG-based measures for assessing fatigue during manual lifting.
Main Results:
- Various EMG processing techniques and fatigue indices have been developed.
- EMG signal analysis can determine safe lifting loads, heights, and repetition limits.
- These methods help identify overexertion before fatigue onset.
Conclusions:
- EMG signal processing offers effective methods for fatigue assessment in manual lifting.
- This review provides a comprehensive overview of available biosignal processing techniques.
- Selecting appropriate EMG measures can significantly enhance worker safety and prevent MSDs.
