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

Author

Listed:
  • Carolina Del-Valle-Soto

    (Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
    These authors contributed equally to this work.)

  • Ramon A. Briseño

    (Centro Universitario de Ciencias Económico Administrativas, Universidad de Guadalajara, Zapopan 45180, Mexico
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Carolina Del-Valle-Soto & Ramon A. Briseño & Ramiro Velázquez & Gabriel Guerra-Rosales & Santiago Perez-Ochoa & Isaac H. Preciado-Bazavilvazo & Paolo Visconti & José Varela-Aldás, 2024. "Enhancing Elderly Care through Low-Cost Wireless Sensor Networks and Artificial Intelligence: A Study on Vital Sign Monitoring and Sleep Improvement," Future Internet, MDPI, vol. 16(9), pages 1-24, September.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:9:p:323-:d:1472674
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    References listed on IDEAS

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    1. Carolina Del-Valle-Soto & Ramon A. Briseño & Leonardo J. Valdivia & Ramiro Velázquez & Juan Arturo Nolazco-Flores, 2023. "Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security," Future Internet, MDPI, vol. 15(9), pages 1-31, August.
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