The application of machine-learning solutions to movement assessment from skeleton videos has attracted significant research attention in recent years. This advancement has made rehabilitation at home more accessible, utilizing movement assessment algorithms that can operate on affordable equipment for human pose detection and analysis from 2D or 3D videos. While the primary objective of automatic assessment tasks is to score movements, the automatic generation of feedback highlighting key movement issues has the potential to significantly enhance and accelerate the rehabilitation process. While numerous research works exist in the field of automatic movement assessment, only a handful address feedback generation. In this study, we explain the types of feedback that can be generated, review existing solutions for automatic feedback generation, and discuss future research directions. To our knowledge, this is the first comprehensive review of feedback generation in skeletal movement assessment.

翻译：近年来，机器学习解决方案在基于骨骼视频的运动评估中的应用引起了广泛研究关注。这一进展使得居家康复更加便捷，利用可在廉价设备上运行的运动评估算法，通过2D或3D视频进行人体姿态检测与分析。虽然自动评估任务的主要目标是对运动进行评分，但自动生成突出关键运动问题的反馈，有可能显著改善并加速康复过程。尽管自动运动评估领域已有大量研究工作，但涉及反馈生成的研究相对较少。在本研究中，我们阐释了可生成的反馈类型，综述了现有的自动反馈生成解决方案，并讨论了未来研究方向。据我们所知，这是首篇关于骨骼运动评估中反馈生成的全面综述。