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© 2025 Elsevier LtdDeveloping graph-based abnormal gait classification models with high generalization has been a challenging problem in gait analysis. In this study, a novel adaptive spatial–temporal cross-graph convolutional fusion learning network is proposed to accurately recognize skeleton-based abnormal gait patterns. In the proposed model, with an adaptive fusion adjacency matrix including self-adaptive adjacency matrices and cross-adaptive adjacency matrices, a joint–bone gait graph convolutional fusion learning algorithm is constructed to capture spatial gait abnormality features hidden in skeleton data. A temporal convolution network is then adopted to explore temporal dependencies of gait abnormality embedded in the spatial feature space. This could discover the most discriminative spatial–temporal gait abnormality representations containing richer information about interaction coupling across joints and bones for high-generalization. The skeleton data of mimic abnormal gait from 57 participants were collected to evaluate the feasibility of our model. The experimental results based on the leave-one-subject-out (LOSO) cross-validation scheme show that our proposed model reaches the optimal performance with the highest accuracy of 99.43%, and significantly outcompetes several recent state-of-the-art models. Our model can feasibly take advantage of the adaptive fusion adjacency matrix to greatly enhance the aggregation degree of joints and bones. This helps to learn excellent gait abnormality representations containing richer interaction information for high generalization while keeping a low learning complexity. Our findings hopefully provide a powerful technical solution for abnormal gait recognition in practical clinical application.
Author(s): Wang L, Wu X, Wu B, Wu J
Publication type: Article
Publication status: Published
Journal: Engineering Applications of Artificial Intelligence
Year: 2025
Volume: 154
Print publication date: 15/08/2025
Online publication date: 05/05/2025
Acceptance date: 16/04/2025
ISSN (print): 0952-1976
ISSN (electronic): 1873-6769
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.engappai.2025.110922
DOI: 10.1016/j.engappai.2025.110922
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