dc.contributor.author | Núñez Marcos, Adrián | |
dc.contributor.author | Arganda Carreras, Ignacio | |
dc.date.accessioned | 2024-05-13T16:34:29Z | |
dc.date.available | 2024-05-13T16:34:29Z | |
dc.date.issued | 2024-06 | |
dc.identifier.citation | Engineering Applications of Artificial Intelligence 132 : (2024) // Article ID 107937 | es_ES |
dc.identifier.issn | 1873-6769 | |
dc.identifier.issn | 0952-1976 | |
dc.identifier.uri | http://hdl.handle.net/10810/67929 | |
dc.description.abstract | Falls pose a major threat for the elderly as they result in severe consequences for their physical and mental health or even death in the worst-case scenario. Nonetheless, the impact of falls can be alleviated with appropriate technological solutions. Fall detection is the task of recognising a fall, i.e. detecting when a person has fallen in a video. Such an algorithm can be implemented in lightweight devices which can then cater to the users’ needs, e.g. alerting emergency services or caregivers. At the core of those systems, a model capable of promptly recognising falls is crucial for reducing the time until help comes. In this paper we propose a fall detection solution based on transformers, i.e. state-of-the-art neural networks for computer vision tasks. Our model takes a video clip and decides if a fall has occurred or not. In a video stream, it would be applied in a sliding-window fashion to trigger an alarm as soon as it detects a fall. We evaluate our fall detection backbone model on the large UP-Fall dataset, as well as on the UR fall dataset, and compare our results with existing literature using the former dataset. | es_ES |
dc.description.sponsorship | This work is supported by grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and by grant GIU19/027 funded by the University of the Basque Country UPV/EHU . | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2021-126701OB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | fall detection | es_ES |
dc.subject | computer vision | es_ES |
dc.subject | transformer | es_ES |
dc.subject | health | es_ES |
dc.title | Transformer-based fall detection in videos | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/). | es_ES |
dc.rights.holder | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0952197624000952 | es_ES |
dc.identifier.doi | 10.1016/j.engappai.2024.107937 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoes | Lenguajes y sistemas informáticos | es_ES |
dc.departamentoeu | Hizkuntza eta sistema informatikoak | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |