Islam, Md Sariful and Zainuddin, Ahmad Anwar and Amir Hussin, Amir 'Aatieff and Hassan, Mohd Khairul Azmi and Ahmad Puzi, Asmarani and Mohd Tamrin, Mohd Izzuddin and Handayani, Dini Oktarina Dwi and Subramaniam, Krishnan and Kamarudin, Saidatul Izyanie (2026) AI and RIoT for rehabilitation: advancing hand gesture recognition and voice assistance. In: First International Conference on Generative AI: Current Research, Industry Trends, and Innovations (FICAI2025), LIBYA.
|
PDF
Download (1MB) | Preview |
Abstract
After a heart attack or a stroke, the patient needs rehabilitation; nevertheless, obviously, conventional approaches are costly, time-consuming, and need a highly qualified staff, which excludes the majority of patients. As part of the proposed solution, this research incorporates Rehabilitation Internet-of-Things (RIoT) that uses Mediapipe for hand gesture detection and voice to guide the exercises. The culmination of the system is to offer availability of computer vision coupled with speech recognition to evaluate the performance during the exercise and to report the extent of rehabilitation within the shortest time. In particular, these movements include flexion, extension of fingers, pinch using the thumb index finger, and opening/closing of the hand and full hand movement that helps in determining the degree of motion for performing movements during the rehabilitation exercises. The RIoT system acts as a voice-activated, on-the-body graphical display that helps the partly mobile users as they obtain real-time feedback from their hand gestures. The sensitivity of the deep learning-based gesture recognition and the speech synthesized is then tested and practiced on recovering patients before testing on the system platform. Thus, the system, in the framework of utilizing assistive automation for rehabilitation, releases the necessity to use human observers while still keeping the overall control by doctors or other healthcare managers. and enables the access to the high-quality rehabilitation therapy for patients, contributes to the decreased healthcare expenditures, and improve the outcomes of the overall patient rehabilitation.
| Item Type: | Proceeding Paper (Other) |
|---|---|
| Uncontrolled Keywords: | Stroke Rehabilitation, Rehabilitation Internet-of-Things (RIoT), Voice AI, 4D Skeletal-Based Gesture Recognition, Machine Learning |
| Subjects: | T Technology > T Technology (General) > T10.5 Communication of technical information |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science |
| Depositing User: | Ts.Dr. Ahmad Anwar Zainuddin |
| Date Deposited: | 05 Feb 2026 10:01 |
| Last Modified: | 05 Feb 2026 13:00 |
| Queue Number: | 2026-01-Q1942 |
| URI: | http://irep.iium.edu.my/id/eprint/126945 |
Actions (login required)
 |
View Item |
Download Statistics
Download Statistics