The ExoNET, a passive robotic solution that provides a soft, biomimetic, and elastic alternative to robotics that embodies intelligence within the mechanical design. Several groups have been exploring performance enhancement using springs with custom-tuned parameters via optimization. Here, it is possible to have a simple reconfigurable system that can not only assist performance, but can also make training easier, faster, and more complete. This contribution has the potential to be clinically significant for rehabilitating neurologically impaired individuals because this proposal will investigate how motor learning can be facilitated through novel assistive technology.
The primary objective of this study is to evaluate the safety, feasibility, and efficacy of using the forearm ExoNET. Specifically, investigators would like to see if the forearm ExoNET tuned to assistance will lead to a reduction in forearm muscle activity and an increase in active supination range of motion. To accomplish this, we plan to have participants perform upper extremity activities of daily living requiring active forearm supination wearing the ExoNET. To achieve these goals, we will use a wearable surface electromyography (EMG) and inertial measurement unit (IMU) using Delsys wearable sensors on the forearm muscles.
Investigators hypothesize that individuals with post-stroke arm movement deficits will experience gains in Action Research Arm Test (ARAT) measures that are significantly above their baseline levels while using the forearm ExoNET tuned to supination assistive support. Secondarily, investigators hypothesize that a forearm ExoNET tuned to supination assistive support will lead to a significant reduction in arm muscle activity and no significant difference in range of motion across a series of upper-extremity tasks in adults without a history of stroke. Lastly, it is hypothesized that usage of a forearm ExoNET tuned to supination anti-assistance can be safe, feasible and tolerated by patients in a given treatment session.