A novel adaptive spatial–temporal cross-graph convolutional fusion learning network for skeleton-based abnormal gait recognition

Developing 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 pat...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 154; S. 110922
Hauptverfasser:	Wang, Liang, Wu, Xiaoyan, Wu, Bin, Wu, Jianning
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.08.2025
Schlagworte:	Abnormal gait recognition Adaptive learning Graph convolutional networks Graph fusion learning Adaptive learning Graph fusion learning Graph convolutional networks Abnormal gait recognition
ISSN:	0952-1976
Online-Zugang:	Volltext
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