Evaluation of functional tests performance using a camera-based and machine learning approach.

Autor: Adolf J; Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.; Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic., Segal Y; BGU Ben-Gurion University of the Negev, Beer Sheva, Israel., Turna M; Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic., Nováková T; Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic., Doležal J; Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic., Kutílek P; Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic., Hejda J; Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic., Hadar O; BGU Ben-Gurion University of the Negev, Beer Sheva, Israel., Lhotská L; Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.; Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Nov 03; Vol. 18 (11), pp. e0288279. Date of Electronic Publication: 2023 Nov 03 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0288279
Abstrakt: The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists' assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Adolf et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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