dc.creator | Luna Perejón, Francisco | es |
dc.creator | Muñoz Saavedra, Luis | es |
dc.creator | Civit Masot, Javier | es |
dc.creator | Civit Balcells, Antón | es |
dc.creator | Domínguez Morales, Manuel Jesús | es |
dc.date.accessioned | 2021-05-05T08:25:36Z | |
dc.date.available | 2021-05-05T08:25:36Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Luna 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.issn | 1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108526 | |
dc.description.abstract | Falls 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.sponsorship | Ministerio de Ciencia, Innovación y Universidades PID2019-105556GB-C33/AEI/10.13039/501100011033 | es |
dc.format | application/pdf | es |
dc.format.extent | 20 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 21 (5) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Accelerometer | es |
dc.subject | Deep learning | es |
dc.subject | Embedded system | es |
dc.subject | Fall detection | es |
dc.subject | Wearable | es |
dc.subject | Recurrent neural networks | es |
dc.title | AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks | 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.publisherversion | https://www.mdpi.com/1424-8220/21/5/1889 | es |
dc.identifier.doi | 10.3390/s21051889 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | es |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 21 | es |
dc.publication.issue | 5 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |