This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with Transformer, is introduced. This model combines a modified version of STGCN and transformer architectures for efficient handling of spatio-temporal data. The key idea is to consider skeleton data respecting its non-linear structure as a graph and detecting joints playing the main role in each rehabilitation exercise. Dense connections and GRU mechanisms are used to rapidly process large 3D skeleton inputs and effectively model temporal dynamics. The transformer encoder's attention mechanism focuses on relevant parts of the input sequence, making it useful for evaluating rehabilitation exercises. The evaluation of our proposed approach on the KIMORE and UI-PRMD datasets highlighted its potential, surpassing state-of-the-art methods in terms of accuracy and computational time. This resulted in faster and more accurate learning and assessment of rehabilitation exercises. Additionally, our model provides valuable feedback through qualitative illustrations, effectively highlighting the significance of joints in specific exercises.

本研究使用一种新的基于图的模型，即使用密集时空图卷积GRU网络与Transformer相结合的模型，对没有临床医生监督下进行的物理康复训练进行自动评估，以提供质量评分确保正确执行并达到期望的效果。在KIMORE和UI-PRMD数据集上的评估结果表明，与最先进的方法相比，该模型在准确性和计算时间方面具有潜力，实现了更快速和更准确的康复训练学习和评估，并通过定性插图提供有价值的反馈，有效地突出了具体训练中关节的重要性。

D-STGCNT: 基于Transformer的稠密时空图卷积GRU网络用于患者体能康复评估