Calibrating Hand Gesture Recognition for Stroke Rehabilitation Internet-of-Things (RIOT) Using MediaPipe in Smart Healthcare Systems

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.