A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare
The increasing demand for remote healthcare solutions has driven the need for effective telerehabilitation systems to support elderly individuals recovering from chronic conditions or post-operative impairments. Existing rehabilitation methods face limitations such as restricted access to specialize...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10829947/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592882314248192 |
|---|---|
| author | Muhammad Abrar Ashraf Shaheryar Najam Touseef Sadiq Shabbab Algamdi Hanan Aljuaid Hameedur Rahman Ahmad Jalal |
| author_facet | Muhammad Abrar Ashraf Shaheryar Najam Touseef Sadiq Shabbab Algamdi Hanan Aljuaid Hameedur Rahman Ahmad Jalal |
| author_sort | Muhammad Abrar Ashraf |
| collection | DOAJ |
| description | The increasing demand for remote healthcare solutions has driven the need for effective telerehabilitation systems to support elderly individuals recovering from chronic conditions or post-operative impairments. Existing rehabilitation methods face limitations such as restricted access to specialized care, overburdened healthcare providers, and the need for consistent, real-time monitoring. To address these challenges, we propose a novel telerehabilitation system that processes depth video frames using a multi-stage methodology. The pipeline begins with noise and floor removal, followed by 3D connected component labeling (CCL) to identify the human subject and extract the human silhouette. Next, skeleton joint points are estimated, and features are extracted from both the joints and silhouette. These multimodal features are fused and input into a deep learning model for classification and correctness assessment. Advanced feature extraction techniques, including Synchrosqueezing Transform (SST) and Hilbert-Huang Transform (HHT), are employed to capture dynamic time-frequency characteristics of human actions. The proposed system classifies nine distinct exercises and assesses the correctness of movements. Experimental evaluation on the IRDS dataset demonstrates a classification accuracy of 91% for exercise recognition and 82% for movement correctness assessment. These results highlight the system’s potential to deliver scalable, cost-effective, real-time rehabilitation, reducing the need for in-person clinical visits and supporting healthcare services for elderly populations. |
| format | Article |
| id | doaj-art-b1f71cb41eed4ec6b32ea901f87ba591 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b1f71cb41eed4ec6b32ea901f87ba5912025-01-21T00:02:02ZengIEEEIEEE Access2169-35362025-01-01139120913310.1109/ACCESS.2025.352671010829947A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly HealthcareMuhammad Abrar Ashraf0Shaheryar Najam1https://orcid.org/0000-0002-2186-2342Touseef Sadiq2https://orcid.org/0000-0001-6603-3639Shabbab Algamdi3https://orcid.org/0000-0003-3435-6681Hanan Aljuaid4Hameedur Rahman5https://orcid.org/0000-0001-8892-9911Ahmad Jalal6https://orcid.org/0009-0000-8421-8477Department of Electrical and Computer Engineering, Riphah International University, Islamabad, PakistanDepartment of Electrical and Computer Engineering, Riphah International University, Islamabad, PakistanDepartment of Information and Communication Technology, Centre for Artificial Intelligence Research, University of Agder, Grimstad, NorwayDepartment of Software Engineering, College of Computer Science and Engineering, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Riyadh, Saudi ArabiaDepartment of Computer Science, Air University, Islamabad, PakistanDepartment of Computer Science, Air University, Islamabad, PakistanThe increasing demand for remote healthcare solutions has driven the need for effective telerehabilitation systems to support elderly individuals recovering from chronic conditions or post-operative impairments. Existing rehabilitation methods face limitations such as restricted access to specialized care, overburdened healthcare providers, and the need for consistent, real-time monitoring. To address these challenges, we propose a novel telerehabilitation system that processes depth video frames using a multi-stage methodology. The pipeline begins with noise and floor removal, followed by 3D connected component labeling (CCL) to identify the human subject and extract the human silhouette. Next, skeleton joint points are estimated, and features are extracted from both the joints and silhouette. These multimodal features are fused and input into a deep learning model for classification and correctness assessment. Advanced feature extraction techniques, including Synchrosqueezing Transform (SST) and Hilbert-Huang Transform (HHT), are employed to capture dynamic time-frequency characteristics of human actions. The proposed system classifies nine distinct exercises and assesses the correctness of movements. Experimental evaluation on the IRDS dataset demonstrates a classification accuracy of 91% for exercise recognition and 82% for movement correctness assessment. These results highlight the system’s potential to deliver scalable, cost-effective, real-time rehabilitation, reducing the need for in-person clinical visits and supporting healthcare services for elderly populations.https://ieeexplore.ieee.org/document/10829947/Depth imaginghuman computer interactioninteraction designexercise recognitionuser experienceuser-centered design |
| spellingShingle | Muhammad Abrar Ashraf Shaheryar Najam Touseef Sadiq Shabbab Algamdi Hanan Aljuaid Hameedur Rahman Ahmad Jalal A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare IEEE Access Depth imaging human computer interaction interaction design exercise recognition user experience user-centered design |
| title | A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare |
| title_full | A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare |
| title_fullStr | A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare |
| title_full_unstemmed | A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare |
| title_short | A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare |
| title_sort | novel telerehabilitation system for physical exercise monitoring in elderly healthcare |
| topic | Depth imaging human computer interaction interaction design exercise recognition user experience user-centered design |
| url | https://ieeexplore.ieee.org/document/10829947/ |
| work_keys_str_mv | AT muhammadabrarashraf anoveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT shaheryarnajam anoveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT touseefsadiq anoveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT shabbabalgamdi anoveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT hananaljuaid anoveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT hameedurrahman anoveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT ahmadjalal anoveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT muhammadabrarashraf noveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT shaheryarnajam noveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT touseefsadiq noveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT shabbabalgamdi noveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT hananaljuaid noveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT hameedurrahman noveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare AT ahmadjalal noveltelerehabilitationsystemforphysicalexercisemonitoringinelderlyhealthcare |