Multistage fall detection framework via 3D pose sequences and TCN integration
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 p...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11325-y |
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| author | Leitao Qi Haibo Sun |
| author_facet | Leitao Qi Haibo Sun |
| author_sort | Leitao Qi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c2dee48390774584b24e0eb4b5351dfa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c2dee48390774584b24e0eb4b5351dfa2025-08-20T03:45:48ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-11325-yMultistage fall detection framework via 3D pose sequences and TCN integrationLeitao Qi0Haibo Sun1School of Basic Medical Sciences, Shandong Second Medical UniversitySchool of Basic Medical Sciences, Shandong Second Medical UniversityAbstract 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.https://doi.org/10.1038/s41598-025-11325-yFall detectionHuman pose estimation3D pose liftingDomain transferTemporal convolutional networksAnomaly detection |
| spellingShingle | Leitao Qi Haibo Sun Multistage fall detection framework via 3D pose sequences and TCN integration Scientific Reports Fall detection Human pose estimation 3D pose lifting Domain transfer Temporal convolutional networks Anomaly detection |
| title | Multistage fall detection framework via 3D pose sequences and TCN integration |
| title_full | Multistage fall detection framework via 3D pose sequences and TCN integration |
| title_fullStr | Multistage fall detection framework via 3D pose sequences and TCN integration |
| title_full_unstemmed | Multistage fall detection framework via 3D pose sequences and TCN integration |
| title_short | Multistage fall detection framework via 3D pose sequences and TCN integration |
| title_sort | multistage fall detection framework via 3d pose sequences and tcn integration |
| topic | Fall detection Human pose estimation 3D pose lifting Domain transfer Temporal convolutional networks Anomaly detection |
| url | https://doi.org/10.1038/s41598-025-11325-y |
| work_keys_str_mv | AT leitaoqi multistagefalldetectionframeworkvia3dposesequencesandtcnintegration AT haibosun multistagefalldetectionframeworkvia3dposesequencesandtcnintegration |