ArticleScientific reports2025
Multistage fall detection framework via 3D pose sequences and TCN integration.
Article in Scientific reports, 2025. 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.
- TFrontiers in psychology · 2025Article
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2 authors.
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Abstract
An accurate yet computationally efficient fall detection system for sports activities is a significant and challenging task. To address this, we propose a novel multi-stage fall detection framework that integrates 3D pose sequences with temporal convolutional modeling. The framework first performs 2D human pose estimation to extract and enhance multi-scale spatial features. Then, it reconstructs the 2D poses into 3D poses using a domain transfer architecture that aligns the 2D and 3D poses within a shared semantic space. Subsequently, we introduce a robust fall detection network that leverages temporal convolutions to process the 3D pose sequences, capturing long-term dependencies while maintaining low computational costs for fall event recognition. Evaluated on the large-scale benchmark action dataset NTU RGB+D, our method achieves a fall detection accuracy of 99.87%, demonstrating its state-of-the-art performance and effectiveness.
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