ArticleiScience2024
Spatial and temporal attention embedded spatial temporal graph convolutional networks for skeleton based gait recognition with multiple IMUs.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Long short-term attention memory (LSTAM): a global-feature-integrated model for joint moment prediction in human rehabilitation.Scientific reports · 2026Article
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7 authors.
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Abstract
Gait recognition is one of the key technologies for exoskeleton robot control, while the current IMU-based gait recognition methods only use inertial data and do not fully consider the interconnections of human spatial structure and human joints. In this regard, a skeleton-based gait recognition approach with inertial measurement units using spatial temporal graph convolutional networks with spatial and temporal attention is proposed. A human forward kinematics solver module was used for constructing different human skeleton models and a temporal attention module was added for capturing the more important time frames in the gait cycle. Moreover, the two-stream structure was used to construct spatial temporal graph convolutional networks with spatial and temporal attention for gait recognition, and an average accuracy of about 99% was obtained in user experiments, which is the best performance compared to other algorithms, provides certain reference for gait recognition and real-time control of exoskeleton robots.
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