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

Autor:	Jungi Kim, Haneol Seo, Muhammad Tahir Naseem, Chan-Su Lee
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	graph convolutional networks (GCN) gait classification spatiotemporal graph convolutional networks (ST-GCN) multiple-input branches (MIB) global average pooling (GAP) temporal convolutional network (TCN) Chemical technology TP1-1185
Zdroj:	Sensors, Vol 22, Iss 13, p 4863 (2022)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s22134863
Popis:	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.
Databáze:	Directory of Open Access Journals
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