Improving Myoelectric Prosthetic and Orthotic Limb Control Using Predictive Regression Algorithms and High-count Surface Electrodes
University of Utah
Summary
The purpose of this study is to improve control of myoelectrically-controlled advanced orthotic devices (an exoskeleton device that use the body's muscle signals to drive movements of a robotic brace) by using advanced predictive decode algorithms, and the use of high count (\> 8) surface electromyographic (sEMG) electrodes.
Description
This study looks to improve control of myoelectrically-controlled advanced powered orthoses (orthoses that use the body's muscle signals to drive movements of a robotic exoskeleton) by using advanced predictive decode algorithms, and the use of high count (\> 8) surface electromyographic (sEMG) electrodes.
Eligibility
- Age range
- 18–70 years
- Sex
- All
- Healthy volunteers
- Yes
Inclusion Criteria: * First-ever ischemic or hemorrhagic stroke * Chronic Stroke (at least 6 months since onset) * Chronic hemiparesis * Functional range of motion for contralateral arm Exclusion Criteria: * Individuals who are currently Incarcerated
Interventions
- Othercommercially available control algorithm
Control of the prosthesis/orthosis is based on clinical standard of care using commercially available control algorithms.
- Otherexperimental control algorithm
Control of the orthosis is based on residual muscle activity mapped to intended movement using high density electromyography and artificial intelligence control algorithms.
Location
- University of UtahSalt Lake City, Utah