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IoT-Based Elderly Health Monitoring System Using Firebase Cloud Computing.

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This study presents an IoT system for continuous elderly health monitoring, achieving high accuracy in predicting user stability. The system demonstrates excellent user satisfaction, enhancing elderly care quality.

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Area of Science:

  • Internet of Things (IoT)
  • Machine Learning
  • Healthcare Technology

Background:

  • Addressing the healthcare challenges posed by the growing elderly population.
  • Need for innovative solutions for continuous health monitoring in elderly individuals.
  • Enhancing the quality of life for the elderly through technology.

Purpose of the Study:

  • To develop and validate an Internet of Things (IoT)-based elderly monitoring system.
  • To integrate real-time data collection and analysis using a cloud platform.
  • To implement supervised machine learning for predicting user health status (stable/unstable).

Main Methods:

  • System architecture design across IoT layers (physical, network, application).
  • Device validation with six participants measuring heart-rate, oxygen saturation, and body temperature.
  • Comparative analysis of supervised machine learning models using accuracy and F1 score.
  • Evaluation of user satisfaction based on usability, comfort, security, and effectiveness.

Main Results:

  • An IoT-based elderly health monitoring system was constructed with a low Mean Average Percentage Error (MAPE) of 0.90%.
  • The XGBoost machine learning model achieved optimal performance with accuracy (0.973) and F1 score (0.970).
  • High user satisfaction rating of 86.55% was recorded for usability, comfort, security, and effectiveness.

Conclusions:

  • The developed system provides practical real-time health monitoring for elderly users and caregivers.
  • Future integration with AI, including machine learning and deep learning, can further enhance predictive health capabilities.
  • The system effectively improves elderly care through continuous monitoring and predictive analysis.