Detecting Compensatory Motions and Providing Informative Feedback During a Tangible Robot Assisted Game for Post-Stroke Rehabilitation

Abstract

Gamified rehabilitation tackles the problem of keeping patients engaged in, and motivated to do physical rehabilitation to improve its efficacy. However, with respect to standard rehabilitation, patients are freer to move about and may compensate their motion difficulties with parasite movements, which would greatly reduce the efficacy of the rehabilitation. To identify and characterize compensatory motions, we collected and analyzed video data of people playing the tangible Pacman game (an upper-limb rehabilitation game in which a patient moves a semi-passive robot, the Pacman , on a map to collect 6 apples, while being chased by one or two autonomous robots, the ghosts ). Participants include 10 healthy elderly adults and 10 chronic stroke patients, who played multiple runs of the game, with different sized maps and various game configurations. By analyzing the video recordings we successfully identified higher shoulder and torso lateral tilt compensation in stroke patients and developed a proof-of-concept compensatory motion detection system which relies on a wearable Inertial Measurement Unit and ROS to provide in-game, real-time visual feedback on compensation.