1. Assessing the Effect of Cervical Transcutaneous Spinal Stimulation With an Upper Limb Robotic Exoskeleton and Surface Electromyography
- Author
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Erin E. Mahan, Jeonghoon Oh, Elyse D. Z. Chase, Nathan B. Dunkelberger, Shane T. King, Dimitry Sayenko, and Marcia K. O'Malley
- Subjects
Robotic rehabilitation ,hybrid FES-robotic systems ,neuromodulation ,spinal cord injury ,exoskeleton ,Medical technology ,R855-855.5 ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Transcutaneous spinal stimulation (TSS) is a promising rehabilitative intervention to restore motor function and coordination for individuals with spinal cord injury (SCI). The effects of TSS are most commonly assessed by evaluating muscle response to stimulation using surface electromyography (sEMG). Given the increasing use of robotic devices to deliver therapy and the emerging potential of hybrid rehabilitation interventions that combine neuromodulation with robotic devices, there is an opportunity to leverage the on-board sensors of the robots to measure kinematic and torque changes of joints in the presence of stimulation. This paper explores the potential for robotic assessment of the effects of TSS delivered to the cervical spinal cord. We used a four degree-of-freedom exoskeleton to measure the torque response of upper limb (UL) joints during stimulation, while simultaneously recording sEMG. We analyzed joint torque and electromyography data generated during TSS delivered over individual sites of the cervical spinal cord in neurologically intact participants. We show that site-specific effects of TSS are manifested not only by modulation of the amplitude of spinally evoked motor potentials in UL muscles, but also by changes in torque generated by individual UL joints. We observed preferential resultant action of proximal muscles and joints with stimulation at the rostral site, and of proximal joints with rostral-lateral stimulation. Robotic assessment can be used to measure the effects of TSS, and could be integrated into complex control algorithms that govern the behavior of hybrid neuromodulation-robotic systems.
- Published
- 2024
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