  Analyzing human motion is an active research area, with various applications.
In this work, we focus on human motion analysis in the context of physical
rehabilitation using a robot coach system. Computer-aided assessment of
physical rehabilitation entails evaluation of patient performance in completing
prescribed rehabilitation exercises, based on processing movement data captured
with a sensory system, such as RGB and RGB-D cameras. 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
obtained from both RGB-D camera (Microsoft Kinect) and estimation from RGB
videos (OpenPose and BlazePose algorithms). A Gaussian Mixture Model (GMM) is
employed from position (and orientation) features, with performance metrics
defined based on the log-likelihood values from GMM. The evaluation is
performed on a medical database of clinical patients carrying out low back-pain
rehabilitation exercises, previously coached by robot Poppy.
