dc.creator | Piñero Fuentes, Enrique | es |
dc.creator | Canas Moreno, Salvador | es |
dc.creator | Ríos Navarro, José Antonio | es |
dc.creator | Domínguez Morales, Manuel Jesús | es |
dc.creator | Sevillano Ramos, José Luis | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2021-10-14T08:10:12Z | |
dc.date.available | 2021-10-14T08:10:12Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Piñero Fuentes, E., Canas Moreno, S., Ríos Navarro, J.A., Domínguez Morales, M.J., Sevillano Ramos, J.L. y Linares Barranco, A. (2021). A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders. Sensors, 21 (15) | |
dc.identifier.issn | 1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/126560 | |
dc.description.abstract | The change from face-to-face work to teleworking caused by the pandemic has induced
multiple workers to spend more time than usual in front of a computer; in addition, the sudden
installation of workstations in homes means that not all of them meet the necessary characteristics for
the worker to be able to position himself/herself comfortably with the correct posture in front of their
computer. Furthermore, from the point of view of the medical personnel in charge of occupational
risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in
a worker is needed. For this purpose, in this work, a system based on the postural detection of the
worker is designed, implemented and tested, using a specialized hardware system that processes
video in real time through convolutional neural networks. This system is capable of detecting the
posture of the neck, shoulders and arms, providing recommendations to the worker in order to
prevent possible health problems, due to poor posture. The results of the proposed system show
that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low
power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over
80% in terms of the pattern detected. | es |
dc.description.sponsorship | Agencia Estatal de Investigación PID2019- 105556GB-C33/ AEI/10.13039/501100011033 | es |
dc.description.sponsorship | Junta de Andalucía US-1263715 | es |
dc.format | application/pdf | es |
dc.format.extent | 16 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 21 (15) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Convolutional neural network | es |
dc.subject | Skeleton | es |
dc.subject | Posture | es |
dc.subject | Telework | es |
dc.subject | e-Health | es |
dc.title | A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | PID2019- 105556GB-C33/ AEI/10.13039/501100011033 | es |
dc.relation.projectID | US-1263715 | es |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/21/15/5236 | es |
dc.identifier.doi | 10.3390/s21155236 | es |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 21 | es |
dc.publication.issue | 15 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | Junta de Andalucía | es |