SAFER‑Activities
A Dataset for Smart Assessment of Fall Events and Routine Activities
Overview
SAFER‑Activities is a dataset for fall detection and physical activity monitoring, with frame-level annotations over long, untrimmed, multi-camera videos and a dedicated subset for wheelchair users. We benchmark 2D/3D skeleton models, frozen RGB foundation-model features, and multimodal fusion for action recognition and fall detection across in-lab, out-of-distribution, wheelchair, and cross-dataset settings: skeleton models generalize best under domain shift, while RGB features help in-domain but degrade sharply out of distribution.
What's Inside
- Over 66 hours of untrimmed video from 46 participants.
- Frame-level start/end annotations for 30 action classes (85,310 action instances).
- 5,406 fall instances alongside look-alike actions such as lying down and sitting.
- A dedicated wheelchair subset and a pose-estimation set of 6,846 images / 8,324 annotated instances.
- 2D/3D pose skeletons, bounding boxes, and frozen pretrained visual features, with subject- and view-wise splits.
Non-wheelchair
Standing
Standing activity
Walking
Running
Jumping
Bending
Reaching
Sitting
Seated activity
Sitting down
Getting up
Lying posture
Lying down
Unstable
Fall
Wheelchair
Sitting
Seated activity
Bending
Picking and placing
Propelling
Getting propelled
Getting up
Sitting down
Standing
Preparing to transfer
Transferring
Lying posture
Lying down
Exercise
Fall
Detecting Falls in the Wild
Models trained on SAFER‑Activities detect falls in public videos far outside the training distribution, including wheelchair users.
Sources: non-wheelchair falls from FailArmy; wheelchair fall from regor09.
Citation
If you use this dataset, please cite our paper:
@misc{lamsal2026saferactivities,
title={SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities},
author={Diwas Lamsal and Pramod Wickramatilake and Jednipat Moonrinta and Mongkol Ekpanyapong and Matthew N. Dailey},
year={2026},
eprint={2609.08038},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2609.08038},
}