VAK Russia 1.1.10 UDC 616.858 CSCSTI 77.00 Russian Classification of Professions by Education 02.00.00 Russian Library and Bibliographic Classification 58 Russian Trade and Bibliographic Classification 5 BISAC SEL VAK Russia 1.2.1 VAK Russia 1.2.2 VAK Russia 3.3.9 VAK Russia 3.1.33 VAK Russia 5.8.6 VAK Russia 2.2.12 UDC 004.932.2 UDC 004.891.2 UDC 616.858-008.6 CSCSTI 20.00 Russian Classification of Professions by Education 06.00.00 Russian Classification of Professions by Education 09.00.00 Russian Classification of Professions by Education 32.00.00 Russian Library and Bibliographic Classification 75 BISAC COM014000 BISAC COM018000 BISAC COM089000 BISAC COM021030 BISAC COM023000 BISAC COM025000 BISAC COM074000 BISAC COM079010 BISAC COM032000

THE USE OF MACHINE VISION AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN OBJECTIFYING THE MOTOR SYMPTOMS OF PATIENTS SUFFERING FROM PARKINSON’S DISEASE USING THE EXAMPLE OF THE CYPD MOBILE APPLICATION The use of machine vision and artificial intelligence technologies in objectifying the motor symptoms of patients suffering from Parkinson’s disease using the example of the CYPD mobile application

Published in Russian Journal of Information Technology in Sports · Volume 2, Issue 3 · Pages 17–25 · Rubric: DIGITAL TECHNOLOGIES IN SPORTS AND REHABILITATION MEDICINE
DOI: https://doi.org/10.62105/2949-6349-2025-2-3-17-25 · EDN: ZSGURK
Received: 16.09.2025 Accepted: 25.09.2025 Published: 12.10.2025
Authors
The urgency of developing a mobile application to automate the collection, initial assessment, and transmission of data on kinematic parameters of patient body movements with Parkinson’s disease to doctors is due to the need to improve the quality of diagnosis and treatment for this condition. Goal. To develop a mobile application that records and objectifies symptoms of Parkinson’s disease by analyzing kinematic parameters of a patient’s movement using machine learning and computer vision techniques. Methods. When developing the application, we used the computer vision frameworks MediaPipe Object Detector and TensorFlow, which are pre-trained neural networks based on a large amount of data. These frameworks can accurately cope with the task in various situations, including different backgrounds, clothing, and shooting conditions. We also used a proprietary algorithm based on the use of a low-pass filter, window control, and Berg autoregression to recognize and evaluate tremor. Results. The CYPD mobile application (https://cypd.mobi/index_en.html) has been developed. It contains computer learning methods and neural networks that are trained to analyze large amounts of data. The application helps to collect information about a patient’s tremors and the results of motor and kinematic tests. Conclusion. The proposed technology for recognizing and analyzing human movements has the potential to be applied in other areas of medicine, such as neurology, as well as to solve other practical problems where human movement is studied.
artificial intelligence, neural networks, machine vision, motor tests, neurological tests, Parkinson’s disease
Funding
The company's own funds
Text References
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