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Improving Patient Rehabilitation Performance In Exercise Games Using Collaborative Filtering Approach

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dc.contributor.authorWaidah Ismail​en_US
dc.contributor.authorIsmail Ahmed Al-Qasem Al-Hadien_US
dc.contributor.authorCrina Grosanen_US
dc.contributor.authorRimuljo Hendradien_US
dc.date.accessioned2024-05-29T01:55:39Z
dc.date.available2024-05-29T01:55:39Z
dc.date.issued2021
dc.date.submitted2022-1-28
dc.descriptionPeerJ Computer ScienceOpen AccessVolume 7, Pages 1 - 292021en_US
dc.description.abstractBackground 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.en_US
dc.identifier.citationIsmail W, Al-Hadi IAA, Grosan C, Hendradi R. 2021. Improving patient rehabilitation performance in exercise games using collaborative filtering approach. PeerJ Computer Science 7:e599 https://doi.org/10.7717/peerj-cs.599en_US
dc.identifier.doi10.7717/peerj-cs.599
dc.identifier.epage29
dc.identifier.issn2376-5992
dc.identifier.issue1
dc.identifier.other481-34
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85112666313&origin=resultslist&sort=plf-f&src=s&st1=Improving+Patient+Rehabilitation+Performance+In+Exercise+Games+Using+Collaborative+Filtering+Approach&sid=2baa564214358486b2ba166d19bd87c5&sot=b&sdt=b&sl=116&s=TITLE-ABS-KEY%28Improving+Patient+Rehabilitation+Performance+In+Exercise+Games+Using+Collaborative+Filtering+Approach%29&relpos=0&citeCnt=2&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1
dc.identifier.urihttps://peerj.com/articles/cs-599/
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9733
dc.identifier.volume1
dc.language.isoenen_US
dc.publisherPeer J Computer Scienceen_US
dc.relation.ispartofPeerJ Computer Scienceen_US
dc.subjectArtificial Intelligence; Collaborative filtering; Data Mining and Machine Learning; Exercise games; Optimization Theory and Computation; Rehabilitationen_US
dc.titleImproving Patient Rehabilitation Performance In Exercise Games Using Collaborative Filtering Approachen_US
dc.typeArticleen_US
dspace.entity.typePublication

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