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Article

Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear

Department of BigData and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
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Academic Editors: Felipe Ortega and Emilio López Cano
Entropy 2021, 23(6), 777; https://doi.org/10.3390/e23060777
Received: 10 May 2021 / Revised: 5 June 2021 / Accepted: 15 June 2021 / Published: 19 June 2021
The 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. View Full-Text
Keywords: assistive technology; elderly people; wearable devices; smart footwear; deep learning; artificial neural networks assistive technology; elderly people; wearable devices; smart footwear; deep learning; artificial neural networks
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MDPI and ACS Style

Aznar-Gimeno, R.; Labata-Lezaun, G.; Adell-Lamora, A.; Abadía-Gallego, D.; del-Hoyo-Alonso, R.; González-Muñoz, C. Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear. Entropy 2021, 23, 777. https://doi.org/10.3390/e23060777

AMA Style

Aznar-Gimeno R, Labata-Lezaun G, Adell-Lamora A, Abadía-Gallego D, del-Hoyo-Alonso R, González-Muñoz C. Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear. Entropy. 2021; 23(6):777. https://doi.org/10.3390/e23060777

Chicago/Turabian Style

Aznar-Gimeno, Rocío, Gorka Labata-Lezaun, Ana Adell-Lamora, David Abadía-Gallego, Rafael del-Hoyo-Alonso, and Carlos González-Muñoz. 2021. "Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear" Entropy 23, no. 6: 777. https://doi.org/10.3390/e23060777

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