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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.
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10 participants in 1 patient group
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Sharmini Atputharaj
Data sourced from clinicaltrials.gov
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