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Electromyography Signal Acquisition, Filtering, and Data Analysis for Exoskeleton Development
Jung-Hoon Sul1, Lasitha Piyathilaka1, Diluka Moratuwage1
1School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia.
Sensors (Basel, Switzerland)
|July 12, 2025
Summary
Electromyography (EMG) processing is key for intuitive robotic exoskeleton control. This review details EMG signal analysis, machine learning, and sensor fusion for advanced human-machine interaction in exoskeletons.
Area of Science:
- Biomedical Engineering
- Robotics
- Neuroscience
Background:
- Wearable robotic exoskeletons require intuitive control interfaces.
- Electromyography (EMG) signals offer a direct measure of neuromuscular activity for control.
- Existing EMG-based control systems face challenges in signal quality and adaptability.
Purpose of the Study:
- To provide a comprehensive review of the EMG signal processing pipeline for robotic exoskeleton applications.
- To explore advanced signal processing, feature extraction, and machine learning techniques for EMG-based control.
- To highlight innovations in multimodal sensing and edge computing for enhanced exoskeleton performance.
Main Methods:
- Review of EMG acquisition techniques (surface, intramuscular, high-density).
- Analysis of noise mitigation strategies (filtering, wavelet transforms, empirical mode decomposition).
- Examination of feature extraction, machine learning (pattern recognition, hybrid control), muscle synergy analysis, and adaptive algorithms.
Main Results:
- Various EMG acquisition methods are suitable for real-time control, each with specific applicability.
- Advanced signal processing and machine learning significantly improve motion classification and control accuracy.
- Multimodal sensing and edge computing offer solutions to EMG-only system limitations.
Conclusions:
- Optimized EMG signal processing, combined with machine learning and sensor fusion, is crucial for developing precise, adaptable, and robust exoskeletons.
- Innovations in this field enhance human-machine interaction for next-generation assistive and rehabilitative devices.
- Future research should focus on personalized control and fatigue compensation strategies.
