Digital Sensing Systems for Electromyography

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Human-Computer Interaction

Background:

• Surface electromyogram (EMG) signals are crucial for movement rehabilitation and human-computer interfaces.
• Future AI-powered prostheses require multi-site forearm EMG, posing challenges in wiring and data management.

Purpose of the Study:

• To design and evaluate a bespoke EMG sensing system addressing wiring complexity and data handling issues.
• To enable distributed signal processing, optimize power consumption, and simultaneously measure skin-sensor impedance for signal fidelity.

Main Methods:

• Development of a custom EMG sensing system with distributed processing capabilities.
• Evaluation through experiments monitoring muscle fatigue, real-time skin-sensor impedance, and myoelectric computer interface control.

Main Results:

• The proposed EMG system demonstrated comparable signal acquisition to a clinically-approved product.
• Simultaneous recording and analysis of skin-sensor impedance were successfully achieved.
• The system facilitated effective control of a myoelectric computer interface.

Conclusions:

• The novel EMG sensing system effectively addresses challenges in advanced myoelectric applications.
• It enables distributed machine learning and improves signal transmission efficiency for future prosthetic integration.
• The system enhances signal fidelity through real-time impedance monitoring.