HAND MOVEMENT DISORDERS TRACKING BY SMARTPHONE BASED ON COMPUTER VISION METHODS
Marko Andrushchenko, Karina Selivanova, Олег Аврунін, Dmytro Palii, Sergii Tymchyk, Dana Turlykozhayeva
Abstract
This article describes the development of a cost-effective, efficient, and accessible solution for diagnosing hand movement disorders using smartphone-based computer vision technologies. It highlights the idea of using ToF camera data combined with RG data and machine learning algorithms to accurately recognize limbs and movements, which overcomes the limitations of traditional motion recognition methods, improving rehabilitation and reducing the high cost of professional medical equipment. Using the ubiquity of smartphones and advanced computational methods, the study offers a new approach to improving the quality and accessibility of diagnosis of movement disorders, offering a promising direction for future research and application in clinical practice.