Improving patient rehabilitation performance in exercise games using collaborative filtering approach

Abstract

Background Virtual reality is utilised in exergames to help patients with disabilities improve on the movement of their limbs. Exergame settings, such as the game difficulty, play important roles in the rehabilitation outcome. Similarly, suboptimal exergames’ settings may adversely affect the accuracy of the results obtained. As such, the improvement in patients’ movement performances falls below the desired expectations. In this paper, a recommender system is incorporated to suggest the most preferred movement setting for each patient, based on the movement history of the patient. Method The proposed recommender system (ResComS) suggests the most suitable setting necessary to optimally improve patients’ rehabilitation performances. In the course of developing the recommender system, three methods are proposed and compared: ReComS (K-nearest neighbours and collaborative filtering algorithms), ReComS+ (k-means, K-nearest neighbours, and collaborative filtering algorithms) and ReComS++ (bacterial foraging optimisation, k-means, K-nearest neighbours, and collaborative filtering algorithms). The experimental datasets are collected using the Medical Interactive Recovery Assistant (MIRA) software platform. Result Experimental results, validated by the patients’ exergame performances, reveal that the ReComS++ approach predicts the best exergame settings for patients with 85.76% accuracy.

Evaluation of Upper Body and Lower Limbs Kinematics through an IMU-Based Medical System: A Comparative Study with the Optoelectronic System

“…Wearable technologies can enable the automated, unsupervised, and objective evaluation of home-based exercise programs, together with patients' compliance with the prescribed treatment plan [36]. Additionally, the biofeedback encourages the patient to pay more attention to the execution of exercises [42], also through a gamification mechanism based on scores, badges, and prizes that may increase their motivation and the engagement in the program [43][44][45].…”

Sports Recommender Systems: Overview and Research Issues

“…During rehabilitation, exercise games (so-called "exergames") help to encourage patients without feeling bored. For such scenarios, Ismail et al [60] propose a collaborative filtering based recommendation system that proposes exercise settings which are most suitable to optimally improve a patients rehabilitation success. With this approach, for the current patient nearest neighbors with similar injuries and rehabilitation phases are identified and the most promising exercises performed by those patients are recommended to the current patient.…”