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Volume 23
Issue 19
10.3390/s23198058
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Article

Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation

by
Artem Obukhov
1,*,
Andrey Volkov
1,
Alexander Pchelintsev
2,
Alexandra Nazarova
1,
Daniil Teselkin
1,
Ekaterina Surkova
1 and
Ivan Fedorchuk
1
1
Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia
2
Department of Higher Mathematics, Tambov State Technical University, 392000 Tambov, Russia
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(19), 8058; https://doi.org/10.3390/s23198058
Submission received: 19 August 2023 / Revised: 15 September 2023 / Accepted: 22 September 2023 / Published: 24 September 2023
(This article belongs to the Section Wearables)
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Abstract

When patients perform musculoskeletal rehabilitation exercises, it is of great importance to observe the correctness of their performance. The aim of this study is to increase the accuracy of recognizing human movements during exercise. The process of monitoring and evaluating musculoskeletal rehabilitation exercises was modeled using various tracking systems, and the necessary algorithms for processing information for each of the tracking systems were formalized. An approach to classifying exercises using machine learning methods is presented. Experimental studies were conducted to identify the most accurate tracking systems (virtual reality trackers, motion capture, and computer vision). A comparison of machine learning models is carried out to solve the problem of classifying musculoskeletal rehabilitation exercises, and 96% accuracy is obtained when using multilayer dense neural networks. With the use of computer vision technologies and the processing of a full set of body points, the accuracy of classification achieved is 100%. The hypotheses on the ranking of tracking systems based on the accuracy of positioning of human target points, the presence of restrictions on application in the field of musculoskeletal rehabilitation, and the potential to classify exercises are fully confirmed.
Keywords: musculoskeletal rehabilitation; motion tracking systems; machine learning; human positioning accuracy musculoskeletal rehabilitation; motion tracking systems; machine learning; human positioning accuracy

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MDPI and ACS Style

Obukhov, A.; Volkov, A.; Pchelintsev, A.; Nazarova, A.; Teselkin, D.; Surkova, E.; Fedorchuk, I. Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation. Sensors 2023, 23, 8058. https://doi.org/10.3390/s23198058

AMA Style

Obukhov A, Volkov A, Pchelintsev A, Nazarova A, Teselkin D, Surkova E, Fedorchuk I. Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation. Sensors. 2023; 23(19):8058. https://doi.org/10.3390/s23198058

Chicago/Turabian Style

Obukhov, Artem, Andrey Volkov, Alexander Pchelintsev, Alexandra Nazarova, Daniil Teselkin, Ekaterina Surkova, and Ivan Fedorchuk. 2023. "Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation" Sensors 23, no. 19: 8058. https://doi.org/10.3390/s23198058

APA Style

Obukhov, A., Volkov, A., Pchelintsev, A., Nazarova, A., Teselkin, D., Surkova, E., & Fedorchuk, I. (2023). Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation. Sensors, 23(19), 8058. https://doi.org/10.3390/s23198058

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