D-STGCNT：基于Transformer的密集时空图卷积-门控循环单元网络用于患者身体康复评估
D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation
AI总结：
提出D-STGCNT模型，结合改进STGCN与Transformer，利用图结构处理骨架数据，在KIMORE和UI-PRMD数据集上超越现有方法，实现高效准确的康复锻炼自动评估。
摘要：
本文解决了对在没有临床医生监督的情况下进行身体康复锻炼的患者进行自动评估的挑战。目标是提供一个质量评分，以确保正确执行并达到预期效果。为实现这一目标，引入了一种新的基于图的模型，即密集时空图卷积-门控循环单元-Transformer网络（Dense Spatio-Temporal Graph Conv-GRU Network with Transformer）。该模型结合了改进版的STGCN和Transformer架构，以高效处理时空数据。关键思想是将骨架数据视为图，尊重其非线性结构，并检测在每个康复锻炼中起主要作用的关节。使用密集连接和GRU机制来快速处理大型3D骨架输入，并有效建模时间动态。Transformer编码器的注意力机制关注输入序列的相关部分，使其对评估康复锻炼非常有用。我们在KIMORE和UI-PRMD数据集上对所提方法的评估凸显了其潜力，在准确性和计算时间方面超越了最先进的方法。这导致了更快、更准确的康复锻炼学习和评估。此外，我们的模型通过定性插图提供有价值的反馈，有效突出特定锻炼中关节的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
