Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults
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Departamento de Electrónica e Informática, Universidad Técnica Federico Santa María, Concepción 4030000, Chile
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Escuela de Ingeniería Civil Biomédica & Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso 2340000, Chile
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Escuela de Ingeniería Informática & Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso 2340000, Chile
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Ecoframe SpA, Temuco 4780000, Chile
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Society and Health Research Center, Laboratory on Aging and Social Epidemiology & Millennium Nucleus on SocioMedicine, Facultad de Ciencias Sociales y Artes, Universidad Mayor, Santiago 7560908, Chile
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Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
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Robert N. Butler Columbia Aging Center, Columbia University, New York, NY 10032, USA
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Escuela de Ingeniería Informática, Universidad de Valparaíso & Millennium Nucleus on SocioMedicine, Valparaíso 2340000, Chile
*
Authors to whom correspondence should be addressed.
Academic Editors: Claudine Lamoth and Kim van Schooten
Sensors 2022, 22(6), 2321; https://doi.org/10.3390/s22062321
Received: 5 February 2022
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Revised: 7 March 2022
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Accepted: 10 March 2022
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Published: 17 March 2022
(This article belongs to the Special Issue Unobtrusive Monitoring of Mobility and Health during Everyday Life)
The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.
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Keywords:
fall; older adult; infrared sensor
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MDPI and ACS Style
Márquez, G.; Veloz, A.; Minonzio, J.-G.; Reyes, C.; Calvo, E.; Taramasco, C. Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults. Sensors 2022, 22, 2321. https://doi.org/10.3390/s22062321
AMA Style
Márquez G, Veloz A, Minonzio J-G, Reyes C, Calvo E, Taramasco C. Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults. Sensors. 2022; 22(6):2321. https://doi.org/10.3390/s22062321
Chicago/Turabian StyleMárquez, Gastón, Alejandro Veloz, Jean-Gabriel Minonzio, Claudio Reyes, Esteban Calvo, and Carla Taramasco. 2022. "Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults" Sensors 22, no. 6: 2321. https://doi.org/10.3390/s22062321
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