Spatial–Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition
Accurate recognition of human actions and poses is essential for applications in fitness monitoring, rehabilitation, and virtual coaching. Despite progress in skeleton-based recognition using Graph Convolutional Networks (GCNs) and Transformers, existing methods often fail to effectively model compl...
Saved in:
| Main Authors: | Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11053804/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep Fusion of Skeleton Spatial–Temporal and Dynamic Information for Action Recognition
by: Song Gao, et al.
Published: (2024-11-01) -
Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
by: Haitao Tian, et al.
Published: (2024-12-01) -
An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition
by: Nguyen Hai Thanh, et al.
Published: (2025-01-01) -
Spatiotemporal decoupling attention transformer for 3D skeleton-based driver action recognition
by: Zhuoyan Xu, et al.
Published: (2025-02-01) -
Dynamic Graph Attention Network for Skeleton-Based Action Recognition
by: Zhenhua Li, et al.
Published: (2025-04-01)