Development of Rehabilitation System (ReHabgame) through Monte-Carlo Tree Search Algorithm

TL;DR

This paper introduces ReHabgame, a rehabilitation system using Monte-Carlo Tree Search to adaptively personalize therapy for post-stroke patients by analyzing movement data from Kinect and Myo Armband.

Contribution

It presents a novel rehabilitation game that employs MCTS to dynamically adjust difficulty and personalize therapy based on real-time movement data.

Findings

MCTS effectively personalizes rehabilitation paths.

The system adapts difficulty based on player performance.

Data collection from Kinect and Myo enhances decision-making.

Abstract

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.