Human Fall Detection from Sequences of Skeleton Features using Vision Transformer

Ali Raza, Ali Raza, Muhammad Yousaf, Muhammad Yousaf, Sergio Velastin, Sergio Velastin, Serestina Viriri

2023

Abstract

Detecting human falls is an exciting topic that can be approached in a number of ways. In recent years, several approaches have been suggested. These methods aim at determining whether a person is walking normally, standing, or falling, among other activities. The detection of falls in the elderly population is essential for preventing major medical consequences and early intervention mitigates the effects of such accidents. However, the medical team must be very vigilant, monitoring people constantly, something that is time consuming, expensive, intrusive and not always accurate. In this paper, we propose an approach to automatically identify human fall activity using visual data to timely warn the appropriate caregivers and authorities. The proposed approach detects human falls using a vision transformer. A Multi-headed transformer encoder model learns typical human behaviour based on skeletonized human data. The proposed method has been evaluated on the UR-Fall and UP-Fall datasets, with an accuracy of 96.12%, 97.36% respectively using RP normalization and linear interpolation comparable to state-of-the-art methods.

Paper Citation

in Harvard Style

Raza A., Yousaf M., Velastin S. and Viriri S. (2023). Human Fall Detection from Sequences of Skeleton Features using Vision Transformer. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 591-598. DOI: 10.5220/0011678800003417

in Bibtex Style

@conference{visapp23,
author={Ali Raza and Muhammad Yousaf and Sergio Velastin and Serestina Viriri},
title={Human Fall Detection from Sequences of Skeleton Features using Vision Transformer},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={591-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011678800003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Human Fall Detection from Sequences of Skeleton Features using Vision Transformer
SN - 978-989-758-634-7
AU - Raza A.
AU - Yousaf M.
AU - Velastin S.
AU - Viriri S.
PY - 2023
SP - 591
EP - 598
DO - 10.5220/0011678800003417
PB - SciTePress