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Review on electromyography signal acquisition and processing
Vidhi Gohel1, Ninad Mehendale2
1K. J. Somaiya College of Engineering, Mumbai, India.
Biophysical Reviews
|November 10, 2020
Summary
Electromyography (EMG) signal capture efficiency averages 70%, but signal processing achieves over 99% accuracy for applications like gait analysis. Deep learning advancements promise improved EMG hardware.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Signal Processing
Background:
- Electromyography (EMG) records neuromuscular electrical signals for medical and biomechanical analysis.
- Current EMG signal acquisition efficiency averages approximately 70%.
Purpose of the Study:
- To review EMG signal acquisition and processing techniques.
- To highlight the potential for deep learning in enhancing EMG technology.
Main Methods:
- Review of existing EMG signal acquisition technologies.
- Analysis of signal processing algorithms for EMG data interpretation.
- Examination of recognition accuracy metrics for decoded EMG signals.
Main Results:
- EMG signal acquisition efficiency is currently around 70%.
- Signal processing algorithms achieve high recognition accuracy, exceeding 99% in many applications.
- Deep learning shows significant potential for improving EMG hardware design and signal capture.
Conclusions:
- While EMG signal acquisition has limitations, advanced processing techniques yield high accuracy.
- Future advancements in deep learning are expected to enhance both EMG signal capture efficiency and hardware design.
