Active proportional electromyogram controlled functional electrical stimulation system

Area of Science:

• Biomedical Engineering
• Neurorehabilitation
• Assistive Technology

Background:

• Intention-driven functional electrical stimulation (FES) shows promise for motor neurorehabilitation.
• Proportional control of FES using voluntary electromyogram (EMG) is a key goal.
• Electrical artefact contamination during FES hinders EMG signal extraction and real-time application.

Purpose of the Study:

• To develop a software-based solution for real-time extraction of voluntary EMG signals during FES.
• To overcome limitations of previous methods, including poor signal extraction, hardware dependency, and unsuitability for online use.
• To validate an adaptive filtering technique for EMG-FES systems in neurorehabilitation.

Main Methods:

• An entirely software-based adaptive filtering technique with an optional comb filter was implemented.
• The system extracts voluntary EMG signals from muscles under FES in real-time.
• The developed Active FES system was validated in a cohort of fifteen patients with tetraplegia.

Main Results:

• The implemented technique successfully extracted voluntary EMG signals, demonstrating coherence with noise-free versions.
• Unlike classic comb filters, the adaptive approach provided superior signal quality.
• The Active FES system effectively modulated FES intensity proportionally to intentional movement in patients.

Conclusions:

• The developed software-based Active FES system provides a viable solution for real-time EMG signal extraction during FES.
• This advancement has significant implications for improving motor neurorehabilitation and developing advanced assistive technologies.
• The system's success in patients with tetraplegia highlights its potential for restoring motor function.