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dc.creatorLuna Perejón, Franciscoes
dc.creatorMuñoz Saavedra, Luises
dc.creatorCivit Masot, Javieres
dc.creatorCivit Balcells, Antónes
dc.creatorDomínguez Morales, Manuel Jesúses
dc.date.accessioned2021-05-05T08:25:36Z
dc.date.available2021-05-05T08:25:36Z
dc.date.issued2021
dc.identifier.citationLuna Perejón, F., Muñoz Saavedra, L., Civit Masot, J., Civit Balcells, A. y Domínguez Morales, M.J. (2021). AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks. Sensors, 21 (5)
dc.identifier.issn1424-8220es
dc.identifier.urihttps://hdl.handle.net/11441/108526
dc.description.abstractFalls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.es
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades PID2019-105556GB-C33/AEI/10.13039/501100011033es
dc.formatapplication/pdfes
dc.format.extent20es
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofSensors, 21 (5)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAccelerometeres
dc.subjectDeep learninges
dc.subjectEmbedded systemes
dc.subjectFall detectiones
dc.subjectWearablees
dc.subjectRecurrent neural networkses
dc.titleAnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networkses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadoreses
dc.relation.projectIDPID2019-105556GB-C33/AEI/10.13039/501100011033es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/21/5/1889es
dc.identifier.doi10.3390/s21051889es
dc.contributor.groupUniversidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitaciónes
dc.journaltitleSensorses
dc.publication.volumen21es
dc.publication.issue5es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (MICINN). Españaes

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