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 using the ExoNET. Specifically, investigators want to see if the ExoNET tuned to gravity support will lead to a reduction in bicep muscle activity and an increase in range of motion. To accomplish this aim, we plan to have participants perform reaching, arm elevation and flexion task exercises wearing the ExoNET. To achieve these goals, we will use a wearable activity tracker (MiGo), to detect the number of activities performed, a wearable surface EMG system (Delsys) on the bicep muscles and a markerless system called the Kinect (version 2) to collect distribution of motion.~Investigators hypothesize that individuals with post-stroke arm movement deficits treated with ExoNET gravity compensation will improve their ARAT measures more than controls receiving a sham treatment. Secondarily, treated subjects will improve in other clinical metrics and will make more movements than controls.
The purpose of this study is to evaluate the safety, feasibility, and preliminary efficacy of the ExoNET passive robotic device. It will provide upper-extremity gravity compensation for therapeutic movement retraining in the chronic post stroke patient population.