000135995 001__ 135995
000135995 005__ 20240704095849.0
000135995 0247_ $$2doi$$a10.3390/e23060777
000135995 0248_ $$2sideral$$a138937
000135995 037__ $$aART-2021-138937
000135995 041__ $$aeng
000135995 100__ $$0(orcid)0000-0003-1415-146X$$aAznar-Gimeno, Rocío
000135995 245__ $$aDeep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear
000135995 260__ $$c2021
000135995 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135995 5203_ $$aThe increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.
000135995 536__ $$9info:eu-repo/grantAgreement/EC/H2020/760789/EU/Metallisation of Textiles to make Urban living for Older people more Independent Fashionable/MATUROLIFE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 760789-MATUROLIFE
000135995 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135995 590__ $$a2.738$$b2021
000135995 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b42 / 86 = 0.488$$c2021$$dQ2$$eT2
000135995 592__ $$a0.553$$b2021
000135995 593__ $$aElectrical and Electronic Engineering$$c2021$$dQ2
000135995 593__ $$aPhysics and Astronomy (miscellaneous)$$c2021$$dQ2
000135995 593__ $$aInformation Systems$$c2021$$dQ2
000135995 594__ $$a4.4$$b2021
000135995 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135995 700__ $$aLabata-Lezaun, Gorka
000135995 700__ $$aAdell-Lamora, Ana
000135995 700__ $$0(orcid)0000-0001-7296-7307$$aAbadía-Gallego, David$$uUniversidad de Zaragoza
000135995 700__ $$0(orcid)0000-0003-2755-5500$$adel-Hoyo-Alonso, Rafael
000135995 700__ $$aGonzález-Muñoz, Carlos
000135995 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000135995 773__ $$g23, 6 (2021), 777 [19 pp.]$$pEntropy$$tENTROPY$$x1099-4300
000135995 8564_ $$s975451$$uhttps://zaguan.unizar.es/record/135995/files/texto_completo.pdf$$yVersión publicada
000135995 8564_ $$s2764187$$uhttps://zaguan.unizar.es/record/135995/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135995 909CO $$ooai:zaguan.unizar.es:135995$$particulos$$pdriver
000135995 951__ $$a2024-07-04-07:59:48
000135995 980__ $$aARTICLE