Vision-based fall detection with convolutional neural networks

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dc.contributor.authorNúñez Marcos, Adrián
dc.contributor.authorAzkune Galparsoro, Gorka
dc.contributor.authorArganda-Carreras, Ignacio
dc.date.accessioned2025-12-17T12:21:49Z
dc.date.available2025-12-17T12:21:49Z
dc.date.issued2017-12-06
dc.date.updated2025-12-17T12:21:49Z
dc.description.abstractOne of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.en
dc.description.sponsorshipThe authors gratefully acknowledge the support of the Basque Government’s Department of Education for the predoctoral funding and NVIDIA Corporation for the donation of theTitan X used for this researchen
dc.identifier.citationNúñez-Marcos, A., Azkune, G., & Arganda-Carreras, I. (2017). Vision-based fall detection with convolutional neural networks. Wireless Communications and Mobile Computing, 2017. https://doi.org/10.1155/2017/9474806
dc.identifier.doi10.1155/2017/9474806
dc.identifier.eissn1530-8677
dc.identifier.issn1530-8669
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4596
dc.language.isoeng
dc.publisherHindawi Limited
dc.rightsCopyright © 2017 Adrián Núñez-Marcos et al.
dc.titleVision-based fall detection with convolutional neural networksen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleWireless Communications and Mobile Computing
oaire.citation.volume2017
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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