A decision model of stroke patient rehabilitation with augmented reality-based games

Abstract

Computer-based systems for stroke rehabilitation can potentially reduce complexity in rehabilitation processes. One of important issues among the rehabilitation systems is how to continuously evaluate patient's performances from such systems. Without a proper measurement for patient's performance, therapists suffer from accurate decision making in patient treatments. Therefore, the main focus of this paper is to develop a rehabilitation system that can minimize therapist supervision. To this end, we develop augmented reality-based rehabilitation system that can automatically capture patients' performance as well as visually monitor patients' progress. We also propose performance measurements of patients to improve decision making abilities of therapists. By analyzing performance data, we discover useful rules for further enhancement of the patients' treatment plan.