Bedtime Monitoring for Fall Detection and Prevention in Older Adults
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http://hdl.handle.net/10045/124795
Títol: | Bedtime Monitoring for Fall Detection and Prevention in Older Adults |
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Autors: | Fernández-Bermejo Ruiz, Jesús | Dorado Chaparro, Javier | Santofimia Romero, Maria José | Villanueva Molina, Félix Jesús | Toro García, Xavier del | Bolaños Peño, Cristina | Llumiguano Solano, Henry | Colantonio, Sara | Flórez-Revuelta, Francisco | López, Juan Carlos |
Grups d'investigació o GITE: | Informática Industrial y Redes de Computadores |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Paraules clau: | Fall detection | Fall prevention | Wearable sensors | Bedtime monitoring | Assisted living |
Àrees de coneixement: | Arquitectura y Tecnología de Computadores |
Data de publicació: | 10-de juny-2022 |
Editor: | MDPI |
Citació bibliogràfica: | Fernández-Bermejo Ruiz J, Dorado Chaparro J, Santofimia Romero MJ, Villanueva Molina FJ, del Toro García X, Bolaños Peño C, Llumiguano Solano H, Colantonio S, Flórez-Revuelta F, López JC. Bedtime Monitoring for Fall Detection and Prevention in Older Adults. International Journal of Environmental Research and Public Health. 2022; 19(12):7139. https://doi.org/10.3390/ijerph19127139 |
Resum: | Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy. |
Patrocinadors: | This research was funded by H2020 European Union program under grant agreement No. 857159 (SHAPES project) and by MCIN/AEI/10.13039/501100011033 grant TALENT-BELIEF (PID2020-116417RB-C44) and by GoodBrother COST action 19121. |
URI: | http://hdl.handle.net/10045/124795 |
ISSN: | 1660-4601 |
DOI: | 10.3390/ijerph19127139 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.3390/ijerph19127139 |
Apareix a la col·lecció: | Investigacions finançades per la UE INV - AmI4AHA - Artículos de Revistas INV - I2RC - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Bermejo-Ruiz_etal_2022_IntJEnvironResPublicHealth.pdf | 3,25 MB | Adobe PDF | Obrir Vista prèvia | |
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