A learning from demonstration framework to promote home-based neuromotor rehabilitation

Abstract

The paper proposes a learning from demonstration (LfD) framework which will enable children with motor disabilities to perform neuromotor rehabilitation exercises at home- and community- settings. LfD, a popular robot learning paradigm, has traditionally been used to teach embodied robots different skills through demonstrations by lay users. In this paper, we propose a novel application of LfD in the health-care domain. The goal of the proposed LfD framework is to learn standard rehabilitation exercises from a therapist's demonstration during a patient's clinic visit and assist the patient to perform the exercises at home through demonstrating (using a 3D avatar) different steps of the exercise. Motion information and EMG signals of a patient are used to train a Markov Decision Process (MDP) model with different steps of the exercise from real-time demonstrations. The MDP model then tracks the progress of a patient as (s)he performs the exercise at home and provides prompts if there is any error or missed steps. The MDP model also allows quantitative evaluation of a patient's performance and improvements over time, a highly desirable property of any home-based rehabilitation system.