Algorithms Based on Computational Intelligence for Autonomous Physical Rehabilitation at Home

Abstract

Abstract. Exergames provide efficient and motivating training mechanisms to support physical rehabilitation at home. Nonetheless, current exergame examples lack some important aspects which cannot be disregarded in rehabilitation. Exergames should: (i) modify the game difficulty adapting to patient's gameplay performance, (ii) monitor if the exercise is correctly executed, and (iii) provide continuous motivation. In this study, we present a game engine which implements computer intelligence-based solutions to provide real-time adaptation, on-line monitoring and an engaging gameplay experience. The game engine applies real-time adaptation using the Quest Bayesian approach to modify the game difficulty according to the patient's performance. Besides, it employs a fuzzy system to monitor the execution of the exercises according to the parameters set by the therapists and provides on-line feedback to guide the patient during the execution of the exercise. Finally, a motivating game experience is provided using rewards and adding random enrichments during the game.