Metraipolis › archive › dc_77458b6f8bb6

SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities — arXiv:2609.08038

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08038
License (as archived): CC0-1.0
Archived: 2026-10-16T15:12:02.465Z
SHA-256 of the archived text: cd79bfc6bafef448ec1bacca43bc4e8f9d85cb6c478f0f3991d9cc6296eef5ed
Stable URL of this page: https://metraipolis.com/documento/dc_77458b6f8bb6

How to cite

SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities — arXiv:2609.08038 — arXiv (en), https://arxiv.org/abs/2609.08038 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_77458b6f8bb6 (Metraipolis archive dc_77458b6f8bb6, sha256 cd79bfc6bafef448ec1bacca43bc4e8f9d85cb6c478f0f3991d9cc6296eef5ed)

BibTeX

@misc{dc77458b6f8bb6,
  title = {SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities — arXiv:2609.08038},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08038},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_77458b6f8bb6; sha256 cd79bfc6bafef448ec1bacca43bc4e8f9d85cb6c478f0f3991d9cc6296eef5ed},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08038. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as falls, particularly for mobility-challenged individuals. Existing datasets are often clip-based, lacking the frame-level detail needed to recognize actions online, as they unfold. To address this, we introduce SAFER-Activities, a dataset for fall detection and physical activity monitoring, with a dedicated subset for wheelchair use scenarios. It comprises over 66 hours of video data captured by multiple cameras, with 85,310 action instances and frame-level annotations for 30 action classes. We benchmark action recognition on SAFER-Activities with 2D and 3D skeleton models, RGB models with frozen backbones, and multimodal fusion strategies, and evaluate on in-lab, out-of-distribution, and cross-dataset test sets. Skeleton-based models generalize best under domain shift; fusing frozen RGB features with the skeleton stream improves in-domain recognition over the baseline CNN1D, most clearly on the wheelchair subset, but degrades out of distribution. Cross-dataset and qualitative evaluations confirm that models trained on SAFER-Activities transfer well to unseen environments and external fall data. To support research on robust fall detection and activity monitoring, we release the dataset and code at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.08038 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T14:49:57.267Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).