Cognitive Agency in Movement Control
Neuromuscular disorders such as stroke, spinal cord injury, or amputation can severely impair one's ability to perform grasp activities of daily living. Individuals may undergo rehabilitate movement coordination with physical therapy to relearn abilities such as skillful hand grasp or newly utilize a hand prosthesis. Our objective is to investigate the potential role of cognitive agency to optimize hand grasp rehabilitation. We hypothesize that when people have a greater sense of agency, the perception they truly initiate and control their hand movements, they will significantly improve grasp function rehabilitation. We are developing a methodology to systematically vary a subject’s perception of agency over a computerized virtual hand performing grasp. This work will serve as a more efficient platform to rehabilitate grasp function following stroke or developing better control systems for either electromechanical hands or neuroprostheses.
Visual Feedback in Movement Performance
We are investigating visual-based learning aimed at accelerating rehabilitation and better user-device integration. Providing the user real-time feedback on spatial position, force generation, or muscle activation patterns can improve performance and retention rate of a rehabilitation exercise. An optimal technique to minimize unnecessary body stresses is determined by a musculoskeletal model developed from subject-specific external measurable performance metrics, including motion tracking of retroreflective markers, muscle activation patterns from surface electromyography sensors, and force sensitive resistors to approximate center of pressure for inverse analysis. Our objective is to train the user on machine-predictable movements for a dynamic rehabilitation task aimed at more effective device control. Assistive device focus is on exoskeletons or functional knee braces that accelerate rehabilitation of athletic injuries, neuromuscular disorders, and spinal cord injuries affecting the lower extremities.
Simulating Control Systems of Movement Devices
Powered lower-limb exoskeletons to restore gait function in the presence of neuromuscular pathology (e.g., spinal cord injury) that are compact and light-weight are now commercially available. Currently, users of these devices still employ crutches or canes to balance and brace against the impact of each step due to limitations in torque actuation and simplified control schemes. The resultant gait is typically staggered and inefficient. Our objective is to utilize simulation-based platforms to design optimal controllers that integrate capabilities of the user and the device to maximize gait performance.