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Bedtime Monitoring for Fall Detection and Prevention in Older Adults

Author

Listed:
  • Jesús Fernández-Bermejo Ruiz

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

  • Javier Dorado Chaparro

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

  • Maria José Santofimia Romero

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

  • Félix Jesús Villanueva Molina

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

  • Xavier del Toro García

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

  • Cristina Bolaños Peño

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

  • Henry Llumiguano Solano

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

  • Sara Colantonio

    (Institute of Information Science and Technologies, National Research Council of Italy, Via G. Moruzzi, 1, 56124 Pisa, Italy)

  • Francisco Flórez-Revuelta

    (Department of Computing Technology, University of Alicante, 03080 Alicante, Spain)

  • Juan Carlos López

    (Computer Architecture and Networks Group, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13005 Ciudad Real, Spain)

Abstract

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.

Suggested Citation

  • Jesús Fernández-Bermejo Ruiz & Javier Dorado Chaparro & Maria José Santofimia Romero & Félix Jesús Villanueva Molina & Xavier del Toro García & Cristina Bolaños Peño & Henry Llumiguano Solano & Sara C, 2022. "Bedtime Monitoring for Fall Detection and Prevention in Older Adults," IJERPH, MDPI, vol. 19(12), pages 1-32, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7139-:d:836006
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    References listed on IDEAS

    as
    1. Omar Aziz & Jochen Klenk & Lars Schwickert & Lorenzo Chiari & Clemens Becker & Edward J Park & Greg Mori & Stephen N Robinovitch, 2017. "Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    2. Chung-Chih Lin & Chih-Yu Yang & Zhuhuang Zhou & Shuicai Wu, 2018. "Intelligent health monitoring system based on smart clothing," International Journal of Distributed Sensor Networks, , vol. 14(8), pages 15501477187, August.
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