dc.creator | Luna Perejón, Francisco | es |
dc.creator | Civit Masot, Javier | es |
dc.creator | Amaya Rodríguez, Isabel | es |
dc.creator | Durán López, Lourdes | es |
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Civit Balcells, Antón | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2019-12-16T10:01:35Z | |
dc.date.available | 2019-12-16T10:01:35Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Luna Perejón, F., Civit Masot, J., Amaya Rodríguez, I., Durán López, L., Domínguez Morales, J.P., Civit Balcells, A. y Linares Barranco, A. (2019). An Automated Fall Detection System Using Recurrent Neural Networks. En AIME 2019: 17th Conference on Artificial Intelligence in Medicine (36-41), Poznan, Poland: Springer. | |
dc.identifier.isbn | 978-3-030-21641-2 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/90929 | |
dc.description.abstract | Falls are the most common cause of fatal injuries in elderly
people, causing even death if there is no immediate assistance. Fall detection
systems can be used to alert and request help when this type of accident
happens. Certain types of these systems include wearable devices
that analyze bio-medical signals from the person carrying it in real time.
In this way, Deep Learning algorithms could automate and improve the
detection of unintentional falls by analyzing these signals. These algorithms
have proven to achieve high effectiveness with competitive performances
in many classification problems. This work aims to study 16
Recurrent Neural Networks architectures (using Long Short-Term Memory
and Gated Recurrent Units) for falls detection based on accelerometer
data, reducing computational requirements of previous research. The
architectures have been tested on a labeled version of the publicly available
SisFall dataset, achieving a mean F1-score above 0.73 and improving
state-of-the-art solutions in terms of network complexity. | es |
dc.description.sponsorship | Ministerio de Economía y Competitivida TEC2016-77785-P | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | AIME 2019: 17th Conference on Artificial Intelligence in Medicine (2019), pp. 36-41. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Fall detection | es |
dc.subject | Deep learning | es |
dc.subject | Recurrent Neural Networks | es |
dc.subject | Long Short-Term Memory | es |
dc.subject | Gated Recurrent Units | es |
dc.subject | Accelerometer | es |
dc.title | An Automated Fall Detection System Using Recurrent Neural Networks | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | 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 | TEC2016-77785-P | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-21642-9_6 | es |
dc.identifier.doi | 10.1007/978-3-030-21642-9_6 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | |
idus.format.extent | 6 | es |
dc.publication.initialPage | 36 | es |
dc.publication.endPage | 41 | es |
dc.eventtitle | AIME 2019: 17th Conference on Artificial Intelligence in Medicine | es |
dc.eventinstitution | Poznan, Poland | es |
dc.relation.publicationplace | Berlin | es |