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

Jindřich Adolf, Yoram Segal, Matyáš Turna, Tereza Nováková, Jaromír Doležal, Patrik Kutílek, Jan Hejda, Ofer Hadar, Lenka Lhotská

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere0288279
JournalPLoS ONE
Issue number11 NOVEMBER
StatePublished - 1 Nov 2023

ASJC Scopus subject areas

  • General


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