The ageing population poses significant challenges to healthcare systems, particularly regarding the monitoring and prevention of sedentary behaviour among the elderly. Prolonged inactivity is associated with increased risks of chronic diseases and reduced quality of life. This study aims to evaluate an innovative IoT-based intelligent monitoring system designed to track sedentary behaviour and automate attendance registration in elderly care centers. The system integrates a miniaturized and ergonomically designed wearable device with a fixed station to monitor physical activity levels and attendance in real-time. Data is transmitted to an IoT platform, providing caregivers with actionable insights and timely alerts regarding prolonged sedentary periods. A pilot study involving three elderly volunteers was conducted primarily to assess technological feasibility and the accuracy of the system in distinguishing between sedentary and non-sedentary behaviour using the Metabolic Equivalent of Task (MET) as a reference. Results indicate that the system accurately differentiates activity states; however, further studies with larger sample sizes are recommended to validate these findings comprehensively and achieve statistically significant outcomes.

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Intelligent System for Monitoring Sedentary Behaviour to Support Elderly Care

  • Santiago Lynett,
  • Santiago Mesa,
  • Wilder Castellanos,
  • Martha Zequera,
  • Pilar Castellanos

摘要

The ageing population poses significant challenges to healthcare systems, particularly regarding the monitoring and prevention of sedentary behaviour among the elderly. Prolonged inactivity is associated with increased risks of chronic diseases and reduced quality of life. This study aims to evaluate an innovative IoT-based intelligent monitoring system designed to track sedentary behaviour and automate attendance registration in elderly care centers. The system integrates a miniaturized and ergonomically designed wearable device with a fixed station to monitor physical activity levels and attendance in real-time. Data is transmitted to an IoT platform, providing caregivers with actionable insights and timely alerts regarding prolonged sedentary periods. A pilot study involving three elderly volunteers was conducted primarily to assess technological feasibility and the accuracy of the system in distinguishing between sedentary and non-sedentary behaviour using the Metabolic Equivalent of Task (MET) as a reference. Results indicate that the system accurately differentiates activity states; however, further studies with larger sample sizes are recommended to validate these findings comprehensively and achieve statistically significant outcomes.