Calibrating hand gesture recognition for stroke Rehabilitation Internet-of-Things (RIOT) using MediaPipe in smart healthcare systems

Zainuddin, Ahmad Anwar and Mohd Dhuzuki, Nurul Hanis and Ahmad Puzi, Asmarani and Johar, Mohd Naqiuddin and Yazid, Maslina (2024) Calibrating hand gesture recognition for stroke Rehabilitation Internet-of-Things (RIOT) using MediaPipe in smart healthcare systems. International Journal of Advanced Computer Science and Applications, 15 (7). pp. 568-583. ISSN 2158-107X E-ISSN 2156-5570

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Abstract

Stroke rehabilitation is fraught with challenges, particularly regarding patient mobility, imprecise assessment scoring during the therapy session, and the security of healthcare data shared online. This work aims to address these issues by calibrating hand gesture recognition systems using the Rehabilitation Internet-of-Things (RIOT) framework and examining the effectiveness of machine learning algorithms in conjunction with the MediaPipe framework for gesture recognition calibration. RIOT represents an IoT system developed for the purpose of facilitating remote rehabilitation, with a particular focus on individuals recovering from strokes and residing in geographically distant regions, in addition to healthcare professionals specialising in physical therapy. The Design of Experiment (DoE) methodology allows physiotherapists and researchers to systematically explore the relationship between RIOT and accurate hand gesture recognition using Python's MediaPipe library, by addressing possible factors that may affect the reliability of patients’ scoring results while emphasising data security consideration. To ensure precise rehabilitation assessments, this initiative seeks to enhance accessible home-based stroke rehabilitation by producing optimal and secure calibrated hand gesture recognition with practical recognition techniques. These solutions will be able to benefit both physiotherapists and patients, especially stroke patients who require themselves to be monitored remotely while prioritising security measures within the smart healthcare context.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Internet-of-Things (IoT); RIOT; stroke rehabilitation; calibration; machine learning; MediaPipe; data security; smart healthcare
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 Kulliyyah of Information and Communication Technology 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:	01 Aug 2024 12:58
Last Modified:	20 Nov 2024 17:20
URI:	http://irep.iium.edu.my/id/eprint/113578

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