  Computational Intelligence (CI) in computer games plays an important role
that could simulate various aspects of real-life problems. CI in real-time
decision-making games can provide a platform for the examination of tree search
algorithms. In this paper, we present a rehabilitation serious game (ReHabgame)
in which the Monte-Carlo Tree Search (MCTS) algorithm is utilized. The game is
designed to combat the physical impairment of post-stroke/brain injury
casualties in order to improve upper limb movement. Through the process of
ReHabgame the player chooses paths via upper limb according to his/her movement
ability to reach virtual goal objects. The system adjusts the difficulty level
of the game based on the player's quality of activity through MCTS. It learns
from the movements made by a player and generates further subsequent objects
for collection. The system collects orientation, muscle and joint activity data
and utilizes them to make decisions. Players data are collected through Kinect
Xbox One and Myo Armband. The results show the effectiveness of the MCTS in the
ReHabgame that progresses from highly achievable paths to the less achievable
ones, thus configuring and personalizing the rehabilitation process.
