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.
PLoS One. 2023 Nov 3;18(11):e0288279. doi: 10.1371/journal.pone.0288279. eCollection 2023.
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.
本研究旨在评估基于摄像头系统和机器学习技术的功能测试的性能。具体来说，我们研究了 OpenPose 和任何标准相机是否可用于评估单腿深蹲测试和下台阶测试的功能测试质量。我们记录了 46 名健康受试者进行的这些练习，提取运动数据，并使用 15 个二进制参数，由三位独立的物理治疗师将其分类为专家评估。我们计算了关键点对方向、关节角度和监测段之间相对距离的运动范围，并使用机器学习算法预测物理治疗师的评估。我们的结果表明，AdaBoost 分类器的特异性为 0.8，敏感性为 0.68，准确性为 0.7。我们的研究结果表明，基于摄像头的系统结合机器学习算法可以成为一种简单且经济实惠的工具，用于评估功能测试的性能质量。
