ArticleSensors (Basel, Switzerland)2022
Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comparative Performance Evaluation of GCN, GAT, and GraphSAGE Architectures for Drug-Gene Interaction Prediction in Ameloblastoma via MEK-Pathway Targeting.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Gait Recognition via Enhanced Visual-Audio Ensemble Learning with Decision Support Methods.Sensors (Basel, Switzerland) · 2025Article
- A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors.Sensors (Basel, Switzerland) · 2025Article
- FP-GCN: Frequency Pyramid Graph Convolutional Network for Enhancing Pathological Gait Classification.Sensors (Basel, Switzerland) · 2024Article
- Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets.Frontiers in public health · 2024Article
- Human Pose Estimation for Clinical Analysis of Gait Pathologies.Bioinformatics and biology insights · 2024Article
- Hybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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