Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets
Rehabilitation is crucial for individuals recovering from injuries or illnesses. It combines medical knowledge, therapy, and technology to improve health and independence. However, a global shortage of physiotherapists makes it challenging to provide adequate rehabilitation services. Current rehabil...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
|
| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2025.2454053 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540902128615424 |
|---|---|
| author | Moamen Zaher Amr S. Ghoneim Laila Abdelhamid Ayman Atia |
| author_facet | Moamen Zaher Amr S. Ghoneim Laila Abdelhamid Ayman Atia |
| author_sort | Moamen Zaher |
| collection | DOAJ |
| description | Rehabilitation is crucial for individuals recovering from injuries or illnesses. It combines medical knowledge, therapy, and technology to improve health and independence. However, a global shortage of physiotherapists makes it challenging to provide adequate rehabilitation services. Current rehabilitation research often lacks advanced computational techniques to automate exercise assessment, relying heavily on time-consuming and costly in-person sessions. This study uses computer vision and classical machine learning (ML) to monitor and evaluate physical rehabilitation exercises using skeletal data. It compares five feature extraction approaches, six feature ranking techniques, and thirteen ML algorithms to identify the most influential features for accurate exercise classification using benchmark datasets (UI-PRMD and KIMORE). The performances of feature ranking algorithms–X2, ReliefF, Gini Decrease, FCBF, Information Gain, and Information Gain Ratio–were examined alongside ML algorithms such as SVMs, RFs, KNN, LDA, and lightGBM, amongst others. ReliefF with an Extra-Tree demonstrated superior performance (classification accuracy of 99.94%) compared to state-of-the-art studies on the UI-PRMD (a 4.4% improvement). However, FCBF, alongside an Extra-Tree, demonstrated robust performance across diverse datasets, achieving 99.64% on UIPRMD (the second-best result) and 81.85% on KIMORE (the highest accuracy reported compared to state-of-the-art studies). FCBF attained robust results together with the various classifiers, averaging 92.65%. |
| format | Article |
| id | doaj-art-7e216dd0376a42d88e2614af283d2766 |
| institution | Kabale University |
| issn | 2475-1839 2475-1847 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj-art-7e216dd0376a42d88e2614af283d27662025-02-04T12:48:38ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472025-01-0112110.1080/24751839.2025.2454053Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasetsMoamen Zaher0Amr S. Ghoneim1Laila Abdelhamid2Ayman Atia3Software Engineering Prog. Faculty of Computing and Artificial Intelligence, Helwan University, Cairo, EgyptComputer Science Dept. Faculty of Computing and Artificial Intelligence, Helwan University, Cairo, EgyptInformation System Dept. Faculty of Computing and Artificial Intelligence, Helwan University, Cairo, EgyptFaculty of Computer Science, October University for Modern Sciences and Arts (MSA), Giza, EgyptRehabilitation is crucial for individuals recovering from injuries or illnesses. It combines medical knowledge, therapy, and technology to improve health and independence. However, a global shortage of physiotherapists makes it challenging to provide adequate rehabilitation services. Current rehabilitation research often lacks advanced computational techniques to automate exercise assessment, relying heavily on time-consuming and costly in-person sessions. This study uses computer vision and classical machine learning (ML) to monitor and evaluate physical rehabilitation exercises using skeletal data. It compares five feature extraction approaches, six feature ranking techniques, and thirteen ML algorithms to identify the most influential features for accurate exercise classification using benchmark datasets (UI-PRMD and KIMORE). The performances of feature ranking algorithms–X2, ReliefF, Gini Decrease, FCBF, Information Gain, and Information Gain Ratio–were examined alongside ML algorithms such as SVMs, RFs, KNN, LDA, and lightGBM, amongst others. ReliefF with an Extra-Tree demonstrated superior performance (classification accuracy of 99.94%) compared to state-of-the-art studies on the UI-PRMD (a 4.4% improvement). However, FCBF, alongside an Extra-Tree, demonstrated robust performance across diverse datasets, achieving 99.64% on UIPRMD (the second-best result) and 81.85% on KIMORE (the highest accuracy reported compared to state-of-the-art studies). FCBF attained robust results together with the various classifiers, averaging 92.65%.https://www.tandfonline.com/doi/10.1080/24751839.2025.2454053Exercise classificationFeature RankingKinectPhysical RehabilitationMachine Learning |
| spellingShingle | Moamen Zaher Amr S. Ghoneim Laila Abdelhamid Ayman Atia Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets Journal of Information and Telecommunication Exercise classification Feature Ranking Kinect Physical Rehabilitation Machine Learning |
| title | Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets |
| title_full | Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets |
| title_fullStr | Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets |
| title_full_unstemmed | Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets |
| title_short | Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets |
| title_sort | rehabilitation monitoring and assessment a comparative analysis of feature engineering and machine learning algorithms on the ui prmd and kimore benchmark datasets |
| topic | Exercise classification Feature Ranking Kinect Physical Rehabilitation Machine Learning |
| url | https://www.tandfonline.com/doi/10.1080/24751839.2025.2454053 |
| work_keys_str_mv | AT moamenzaher rehabilitationmonitoringandassessmentacomparativeanalysisoffeatureengineeringandmachinelearningalgorithmsontheuiprmdandkimorebenchmarkdatasets AT amrsghoneim rehabilitationmonitoringandassessmentacomparativeanalysisoffeatureengineeringandmachinelearningalgorithmsontheuiprmdandkimorebenchmarkdatasets AT lailaabdelhamid rehabilitationmonitoringandassessmentacomparativeanalysisoffeatureengineeringandmachinelearningalgorithmsontheuiprmdandkimorebenchmarkdatasets AT aymanatia rehabilitationmonitoringandassessmentacomparativeanalysisoffeatureengineeringandmachinelearningalgorithmsontheuiprmdandkimorebenchmarkdatasets |