Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement...

Area of Science:

• Neuroscience
• Rehabilitation Engineering
• Biomedical Engineering

Background:

• Multiple sclerosis (MS) frequently causes upper limb dysfunction, limiting daily activities.
• Existing interventions for MS arm dysfunction are limited.
• Robotics and functional electrical stimulation (FES) show promise for rehabilitation.

Purpose of the Study:

• To investigate the feasibility of combining FES with passive robotic support during virtual reality (VR) training for people with MS (pwMS).
• To enhance upper limb function in pwMS through a novel VR-based intervention.
• To assess the effectiveness of iterative learning control (ILC) in optimizing FES delivery.

Main Methods:

• A system combining FES, passive robotic support, and VR was developed.
• Iterative learning control (ILC) adjusted FES to improve accuracy and maximize voluntary effort.
• Five pwMS underwent 18 one-hour training sessions over 10 weeks.
• Tracking performance and FES usage were analyzed; clinical arm function was assessed pre- and post-intervention.

Main Results:

• Significant improvements in tracking accuracy were observed, both with and without FES assistance.
• The maximum amount of FES required to assist tracking decreased post-intervention.
• Reduced impairment in the trained proximal arm was noted.
• The system was well-tolerated, with no reported increase in muscle fatigue.

Conclusions:

• Combining FES with passive robot assistance in VR training is feasible and potentially effective for improving arm movement and control in pwMS.
• This approach offers a promising avenue for MS rehabilitation, warranting further investigation in larger studies.