Automating Stroke Rehabilitation for Home-Based Therapy

In this work we present a conceptual design to automatically evaluate a subject's performance for a homebased stroke rehabilitation system. We propose to model a reaching task as a trajectory in the state space of hand part features and then use reward learning to automatically generate new ratings for subjects to track performance over time. Neuromuscular rehabilitation of the upper-extremity after a stroke requires dedicated hours of arm and hand exercises with a therapist. Often to improve the flexibility of the hands, therapists ask patients to manipulate differently shaped objects and move them around. We are working towards developing a home-based neuromuscular rehabilitation system Figure 1: An ideal home based therapy system using a single by doing away with markers and using 2D computer vision camera where a subject can do upper-extremity exercises by instead. As a first step, we are able to identify different grasp manipulating objects in the home on a simple kitchen table.