Reduced arm and hand function has a significant impact on independence and quality of life after spinal cord injury. Functional electrical stimulation therapy (FES-T) is a treatment that can produce improvements in reaching and grasping function after neurological injuries. However, not all paralyzed muscles respond equally well to the therapy. Currently, therapists cannot predict which muscles will respond, limiting their ability to create a personalized therapy plan that can maximize outcomes while making the best use of the limited treatment time available. The objective of this study is to develop a diagnostic method that will allow therapists to quickly and easily screen muscles in the clinic, in order to predict how they will respond to FES-T. Participants with cervical spinal cord injury will receive FES-T through the Rocket Family Upper Extremity Clinic at the Toronto Rehabilitation Institute - University Health Network. Muscles receiving training will undergo a electrophysiological examination before the start of therapy, and will then be tracked for strength recovery over the course of 30 sessions. Lastly, signal processing and machine learning techniques will be applied to the electrophysiological data to predict the recovery profile of each muscle. The significance of this work will be to provide personalized therapy planning in FES-T, leading to more effective use of healthcare resource as well as improved outcomes.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
10
Functional electrical stimulation therapy will be delivered using the MyndMove system (MyndTec, Inc., Mississauga, Canada).
Toronto Rehabilitation Institute - University Health Network
Toronto, Ontario, Canada
Muscle strength recovery profile
Change over time in manual muscle testing conducted on each treated muscle.
Time frame: Muscle strength assessed at each of 30 therapy sessions (3-5 sessions per week).
Surface electromyography signal features
Surface electromyography of treated muscles will be conducted at baseline. A set of signal features will be extracted for use in predictive modeling (examples include Hudgins' time-domain feature set, autoregressive coefficients, and frequency domain features). An optional second assessment will occur at the end of the 30 therapy sessions.
Time frame: Baseline (required). End of therapy (6-10 weeks; optional).
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.