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

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.