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

• Published in: PloS one Vol. 18; no. 11; p. e0288279
• Main Authors: Adolf, Jindřich, Segal, Yoram, Turna, Matyáš, Nováková, Tereza, Doležal, Jaromír, Kutílek, Patrik, Hejda, Jan, Hadar, Ofer, Lhotská, Lenka
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 03.11.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Assessments; Biology and Life Sciences; Cameras; Complications and side effects; Computer and Information Sciences; Data mining; Evaluation; Functional testing; Hypotheses; Learning algorithms; Machine learning; Medicine and Health Sciences; Motion capture; Patient outcomes; Performance assessment; Performance evaluation; Physical Sciences; Physical therapy; Rehabilitation; Research and Analysis Methods; Therapeutics, Physiological; Czech Republic
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0288279

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.