Enhancing Elderly Care through Low-Cost Wireless Sensor Networks and Artificial Intelligence: A Study on Vital Sign Monitoring and Sleep Improvement

Carolina Del-Valle-Soto (), Ramon A. Briseño, Ramiro Velázquez, Gabriel Guerra-Rosales, Santiago Perez-Ochoa, Isaac H. Preciado-Bazavilvazo, Paolo Visconti and José Varela-Aldás
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Carolina Del-Valle-Soto: Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Ramon A. Briseño: Centro Universitario de Ciencias Económico Administrativas, Universidad de Guadalajara, Zapopan 45180, Mexico
Ramiro Velázquez: Facultad de Ingeniería, Universidad Panamericana, Aguascalientes 20296, Mexico
Gabriel Guerra-Rosales: Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Santiago Perez-Ochoa: Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Isaac H. Preciado-Bazavilvazo: Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Paolo Visconti: Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
José Varela-Aldás: Centro de Investigación de Ciencias Humanas y de la Educación (CICHE), Universidad Indoamérica, Ambato 180103, Ecuador

Future Internet, 2024, vol. 16, issue 9, 1-24

Abstract: This research explores the application of wireless sensor networks for the non-invasive monitoring of sleep quality and vital signs in elderly individuals, addressing significant challenges faced by the aging population. The study implemented and evaluated WSNs in home environments, focusing on variables such as breathing frequency, deep sleep, snoring, heart rate, heart rate variability (HRV), oxygen saturation, Rapid Eye Movement (REM sleep), and temperature. The results demonstrated substantial improvements in key metrics: 68% in breathing frequency, 68% in deep sleep, 70% in snoring reduction, 91% in HRV, and 85% in REM sleep. Additionally, temperature control was identified as a critical factor, with higher temperatures negatively impacting sleep quality. By integrating AI with WSN data, this study provided personalized health recommendations, enhancing sleep quality and overall health. This approach also offered significant support to caregivers, reducing their burden. This research highlights the cost-effectiveness and scalability of WSN technology, suggesting its feasibility for widespread adoption. The findings represent a significant advancement in geriatric health monitoring, paving the way for more comprehensive and integrated care solutions.

Keywords: wireless sensor networks; elderly health monitoring; sleep quality; artificial intelligence integration (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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