Ultra-Low-Power Digital Filtering for Insulated EMG Sensing

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Signal Processing

Background:

• Traditional electromyography (EMG) sensors for myoelectric prostheses require skin contact, leading to issues with sweat, pressure, and suitability for patients with circulatory disorders.
• Existing EMG systems are sensitive to sweat and require skin preparation, limiting their practicality and comfort for amputees.

Purpose of the Study:

• To develop ultra-low-power digital signal processing algorithms for insulated, capacitive EMG sensors for advanced myoelectric prostheses control.
• To overcome limitations of traditional EMG sensors by enabling sweat-free, comfortable, and easy-to-apply prosthetic control systems.

Main Methods:

• Development of ultra-low-power digital signal processing algorithms for insulated EMG sensors utilizing capacitive coupling.
• Design and implementation of digital filters for noise attenuation and an algorithm for movement artifact suppression.
• Investigation of optimal system setup and filter parameters for balancing noise reduction and EMG signal quality.

Main Results:

• A low-cost, insulated EMG sensor system was developed for real-time myoelectric prostheses control.
• Digital filters effectively attenuated external noise and movement artifacts, enabling robust performance in real-world conditions.
• The developed algorithms require minimal computational resources and memory, suitable for ultra-low-power microcontrollers.

Conclusions:

• Insulated, capacitive EMG sensors offer a significant advancement over traditional sensors for myoelectric prostheses.
• The developed ultra-low-power algorithms provide effective noise and artifact suppression for reliable prosthetic control.
• This technology enhances the quality of life for amputees by enabling more comfortable, robust, and independent prosthetic use.