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© 2026 Elsevier Ltd. The accurate recognition of elderly gait patterns can significantly contribute to clinical applications such as elderly health monitoring and fall risk prediction. However, how to develop a high-generalization elderly gait classification model has become a challenging problem in elderly gait quantization analysis. Considering the interaction coupling changes across joints in the kinetic chains of elderly gait, we propose an advanced adaptive edge-aware dual-graph convolutional network (AEDGCN) for high-accuracy elderly gait recognition. Our model integrates a gait-graph and a gait-hypergraph to capture high-order joint interaction coupling, which reflects subtle differences in elderly gait changes. By modeling these fine-grained spatial–temporal dependencies, the proposed model achieves strong generalization in accurately identifying elderly gait patterns. Specifically, the proposed technique employs an Edge-Aware Mechanism (EAM) to simultaneously model local spatial dependencies between joints from the gait-graph and cross-joint correlations from the gait-hypergraph. Additionally, the Hierarchical Deep Fully Convolution (HDFC) module is designed to enhance the modeling of temporal dependencies across multiple scales. Our Kinect-based gait dataset, comprising 45 healthy younger participants and 34 healthy elderly participants, with three walking patterns, is used to evaluate the feasibility of our method. In addition, experiments on the public KINECAL dataset further demonstrate the generalization capability of the proposed model. The experimental results confirm that our model outperforms state-of-the-art methods while keeping a low learning complexity. The proposed method effectively enables modeling of elderly gait dynamics, providing informative feature representations for understanding age-related locomotion changes and supporting downstream clinical assessments
Author(s): Wang B, Wu X, Wu J, Zeng Q, Lin Z
Publication type: Article
Publication status: Published
Journal: Engineering Applications of Artificial Intelligence
Year: 2026
Volume: 167
Print publication date: 01/03/2026
Online publication date: 13/01/2026
Acceptance date: 11/01/2026
ISSN (print): 0952-1976
ISSN (electronic): 1873-6769
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.engappai.2026.113849
DOI: 10.1016/j.engappai.2026.113849
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