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.

翻译：人体运动分析是一个活跃的研究领域，具有多种应用前景。本研究聚焦于利用机器人教练系统进行物理康复背景下的人体运动分析。基于传感器系统（如RGB和RGB-D相机）采集的运动数据处理，计算机辅助的物理康复评估需要对患者完成规定康复训练的表现进行评价。随着基于RGB图像的二维和三维人体姿态估计取得显著进展，本研究旨在比较使用RGB-D相机（Microsoft Kinect）直接获取的运动数据与基于RGB视频估计（采用OpenPose和BlazePose算法）所得运动数据在物理康复训练评估中的应用效果。我们采用高斯混合模型处理位置（及方向）特征，并基于GMM的对数似然值定义性能指标。评估在一个由临床患者执行下腰痛康复训练动作的医疗数据库上进行，该数据库中的患者此前曾接受机器人Poppy的指导训练。