HAND MOVEMENT DISORDERS TRACKING BY SMARTPHONE BASED ON COMPUTER VISION METHODS-Reference-Cited by-同舟云学术

HAND MOVEMENT DISORDERS TRACKING BY SMARTPHONE BASED ON COMPUTER VISION METHODS

Published:2024-06-30 Issue:2 Volume:14 Page:5-10
ISSN:2391-6761
Container-title:Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
language:
Short-container-title:IAPGOS

Author:

Andrushchenko Marko^ORCID,Selivanova Karina,Avrunin Oleg^ORCID,Palii Dmytro^ORCID,Tymchyk Sergii^ORCID,Turlykozhayeva Dana^ORCID

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.

Publisher

Politechnika Lubelska

Reference27 articles.

1. Apple Machine Learning Research (n.d.). Deploying Transformers on the Apple Neural Engine [https://machinelearning.apple.com/research/neural-engine-transformers].

2. Apple Inc. (n.d.). Streaming Depth Data from the TrueDepth Camera. Apple Developer Documentation [https://developer.apple.com/documentation/avfoundation/additional_data_capture/streaming_depth_data_from_the_truedepth_camera].

3. Apple Developer Documentation. Streaming Depth Data from the TrueDepth Camera | Apple Developer Documentation [https://developer.apple.com/documentation/avfoundation/additional_data_capture/streaming_depth_data_from_the_truedepth_camera,2023] (accessed 3 Dec. 2023).

4. Assimp.org. The Asset-Importer Library Home [https://www.assimp.org] (accessed 3 Dec. 2023).

5. Avrunin O. G. et al.: Research Active Posterior Rhinomanometry Tomography Method for Nasal Breathing Determining Violations. Sensors 21, 2021, 8508.