Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises

Physical rehabilitation focuses on the improvement of body functions, usually after injury or surgery. Patients undergoing rehabilitation often need to perform exercises at home without the presence of a physiotherapist. Computer-Aided assessment of physical rehabilitation can improve patients' performance and help in completing prescribed rehabilitation exercises. In this work, we focus on human motion analysis in the context of physical rehabilitation for Low Back Pain (LBP). As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data acquired from RGB videos and human pose estimation from those. In this work, we provide an analysis of two types of algorithms on a Low Back Pain rehabilitation datasets. One is based on a Gaussian Mixture Model (GMM), with performance metrics based on the log-Likelihood values from GMM. Furthermore, with the recent development of Deep Learning and Graph Neural Networks, algorithms based on Spatio-Temporal Graph Convolutional Networks (STGCN) are taken as a novel approach. We compared the algorithms in terms of data efficiency and performance, with evaluation performed on two LBP rehabilitation datasets: KIMORE and Keraal. Our study confirms that Kinect, OpenPose, and BlazePose data yield similar evaluation scores, and shows that STGCN outperforms GMM in most configurations.