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。