Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model

作者：Jungi Kim, Haneol Seo, Muhammad Tahir Naseem, Chan-Su Lee · 发表于：Sensors · 年份：2022 · DOI：10.3390/s22134863 · 被引用次数：16 · 研究领域：Gait Recognition and Analysis、Diabetic Foot Ulcer Assessment and Management、Human Pose and Action Recognition

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.