Department of Automotive Lighting Convergence Engineering, Yeungnam University, Gyeongsan 38541, Korea.
Research Institute of Human Ecology, Yeungnam University, Gyeongsan 38541, Korea.
Sensors (Basel). 2022 Jun 27;22(13):4863. doi: 10.3390/s22134863.
Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This study investigated a spatiotemporal graph convolutional network model (ST-GCN), using attention techniques applied to pathological-gait classification from the collected skeletal information. The focus of this study was twofold. The first objective was extracting spatiotemporal features from skeletal information presented by joint connections and applying these features to graph convolutional neural networks. The second objective was developing an attention mechanism for spatiotemporal graph convolutional neural networks, to focus on important joints in the current gait. This model establishes a pathological-gait-classification system for diagnosing sarcopenia. Experiments on three datasets, namely NTU RGB+D, pathological gait of GIST, and multimodal-gait symmetry (MMGS), validate that the proposed model outperforms existing models in gait classification.
步行是一种使用人体肌肉和关节的运动，对于了解身体状况至关重要。通过步态分析人体运动已经在人体识别、运动科学和医学中得到了研究和应用。本研究调查了一种时空图卷积网络模型（ST-GCN），该模型使用注意力技术应用于从收集的骨骼信息中进行病理性步态分类。本研究有两个重点。第一个目标是从关节连接呈现的骨骼信息中提取时空特征，并将这些特征应用于图卷积神经网络。第二个目标是为时空图卷积神经网络开发一种注意力机制，以便关注当前步态中的重要关节。该模型为诊断肌肉减少症建立了一种病理性步态分类系统。在三个数据集，即 NTU RGB+D、GIST 的病理性步态和多模态步态对称性（MMGS）上的实验验证了所提出的模型在步态分类方面优于现有模型。
