While automatic monitoring and coaching of exercises are showing encouraging results in non-medical applications, they still have limitations such as errors and limited use contexts. To allow the development and assessment of physical rehabilitation by an intelligent tutoring system, we identify in this article four challenges to address and propose a medical dataset of clinical patients carrying out low back-pain rehabilitation exercises. The dataset includes 3D Kinect skeleton positions and orientations, RGB videos, 2D skeleton data, and medical annotations to assess the correctness, and error classification and localisation of body part and timespan. Along this dataset, we perform a complete research path, from data collection to processing, and finally a small benchmark. We evaluated on the dataset two baseline movement recognition algorithms, pertaining to two different approaches: the probabilistic approach with a Gaussian Mixture Model (GMM), and the deep learning approach with a Long-Short Term Memory (LSTM). This dataset is valuable because it includes rehabilitation relevant motions in a clinical setting with patients in their rehabilitation program, using a cost-effective, portable, and convenient sensor, and because it shows the potential for improvement on these challenges.

翻译：尽管自动监测与运动指导在非医疗应用中已展现出令人鼓舞的成果，但其仍存在诸如识别错误和使用场景有限等局限性。为支持智能辅导系统在物理康复领域的开发与评估，本文提出了四个待解决的关键挑战，并构建了一个包含临床患者执行腰痛康复练习的医学数据集。该数据集包含3D Kinect骨架位置与朝向、RGB视频、2D骨架数据以及用于评估动作正确性、错误分类、身体部位及时间跨度定位的医学标注。围绕此数据集，我们完成了从数据采集、处理到小型基准测试的完整研究流程。我们在数据集上评估了两种基于不同方法的基线运动识别算法：采用高斯混合模型（GMM）的概率方法，以及采用长短期记忆网络（LSTM）的深度学习方法。本数据集具有重要价值，原因在于：它通过经济、便携且易用的传感器，在临床环境中采集了处于康复计划中的患者执行与康复相关的真实动作；同时，该数据集展现了在这些挑战上取得改进的潜力。