Development of Rehabilitation System (RehabGame) through Monte-Carlo Tree Search Algorithm using Kinect and Myo Sensor Interface

Artificial and Computational Intelligence in computer games play an important role that could simulate various aspects of real life problems. Development of artificial intelligence techniques in real time decision-making games can provide a platform for the examination of tree search algorithms. In this paper, we present a rehabilitation system known as RehabGame in which the Monte-Carlo Tree Search algorithm is used. The objective of the game is to combat the physical impairment of stroke/ brain injury casualties in order to improve upper limb movement. Through the process of a real-time rehabilitation game, the player decides on paths that could be taken by her/his upper limb in order to reach virtual goal objects. The system has the capability of adjusting the difficulty level to the player0 s ability by learning from the movements made and generating further subsequent objects. The game collects orientation, muscle and joint activity data and utilizes them to make decisions on game progression. Limb movements are stored in the search tree which is used to determine the location of new target virtual fruit objects by accessing the data saved in the background from different game plays. It monitors the enactment of the muscles strain and stress through the Myo armband sensor and provides the next step required for the rehabilitation purpose. The results from two samples show the effectiveness of the MonteCarlo Tree Search in the RehabGame by being able to build a coherent hand motion. It progresses from highly achievable paths to the less achievable ones, thus configuring and personalizing the rehabilitation process.