⚡ Quick Summary
This study presents an IoT-based elderly health monitoring system utilizing Firebase cloud computing to enhance the quality of life for the elderly. The system achieved a remarkable mean average percentage error (MAPE) of 0.90% across key health parameters, demonstrating its effectiveness in real-time health monitoring.
🔍 Key Details
- 📊 Participants: 6 elderly individuals
- 🧩 Health parameters monitored: Heart rate, oxygen saturation, body temperature
- ⚙️ Technology: Firebase cloud platform, supervised machine learning
- 🏆 Optimal model: XGBoost with accuracy of 0.973 and F1 score of 0.970
🔑 Key Takeaways
- 📈 Real-time monitoring is crucial for elderly health management.
- 💡 The system integrates IoT technology with cloud computing for enhanced data analysis.
- 🤖 Machine learning plays a vital role in predicting health stability.
- 🏥 User satisfaction rated high at 86.55% based on usability, comfort, security, and effectiveness.
- 🌍 Future enhancements may include AI technologies for improved predictive capabilities.
📚 Background
The growing elderly population poses significant challenges for healthcare systems worldwide. Continuous health monitoring is essential to ensure timely interventions and improve the quality of life for elderly individuals. Traditional monitoring methods often lack the efficiency and immediacy required to address health fluctuations in real-time. This study aims to bridge that gap through innovative technology.
🗒️ Study
Conducted with six participants, this study developed an IoT-based monitoring system that collects real-time health data, including heart rate, oxygen saturation, and body temperature. The system architecture comprises three layers: physical, network, and application, ensuring seamless data flow and analysis. The data was validated using the mean average percentage error (MAPE) to assess accuracy.
📈 Results
The monitoring system demonstrated a MAPE of 0.90% across the monitored parameters, with specific errors of 1.68% for heart rate, 0.57% for oxygen saturation, and 0.44% for body temperature. The XGBoost model emerged as the most effective machine learning model, achieving an accuracy of 0.973 and an F1 score of 0.970, indicating its reliability in predicting the health status of users.
🌍 Impact and Implications
The implications of this study are profound, as it offers a practical solution for both elderly users and caregivers. By enabling real-time health monitoring, the system can facilitate timely medical interventions, potentially reducing hospital visits and improving overall health outcomes. The integration of AI technologies in future iterations could further enhance predictive capabilities, making this system a cornerstone in elderly care.
🔮 Conclusion
This study highlights the significant potential of an IoT-based health monitoring system in improving elderly care. With its impressive accuracy and user satisfaction ratings, the system stands as a promising tool for real-time health management. As technology continues to evolve, the incorporation of advanced AI techniques could lead to even greater advancements in elderly health monitoring.
💬 Your comments
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IoT-Based Elderly Health Monitoring System Using Firebase Cloud Computing.
Abstract
BACKGROUND AND AIMS: The increasing elderly population presents significant challenges for healthcare systems, necessitating innovative solutions for continuous health monitoring. This study develops and validates an IoT-based elderly monitoring system designed to enhance the quality of life for elderly people. The system features a robust Android-based user interface integrated with the Firebase cloud platform, ensuring real-time data collection and analysis. In addition, a supervised machine learning technology is implemented to conduct prediction task of the observed user whether in “stable” or “not stable” condition based on real-time parameter.
METHODS: The system architecture adopts the IoT layer including physical layer, network layer, and application layer. Device validation is conducted by involving six participants to measure the real-time data of heart-rate, oxygen saturation, and body temperature, then analysed by mean average percentage error (MAPE) to define error rate. A comparative experiment is conducted to define the optimal supervised machine learning model to be deployed into the system by analysing evaluation metrics. Meanwhile, the user satisfaction aspect evaluated by the terms of usability, comfort, security, and effectiveness.
RESULTS: IoT-based elderly health monitoring system has been constructed with a MAPE of 0.90% across the parameters: heart-rate (1.68%), oxygen saturation (0.57%), and body temperature (0.44%). In machine learning experiment indicates XGBoost model has the optimal performance based on the evaluation metrics of accuracy and F1 score which generates 0.973 and 0.970, respectively. In user satisfaction aspect based on usability, comfort, security, and effectiveness achieving a high rating of 86.55%.
CONCLUSION: This system offers practical applications for both elderly users and caregivers, enabling real-time monitoring of health conditions. Future enhancements may include integration with artificial intelligence technologies such as machine learning and deep learning to predict health conditions from data patterns, further improving the system’s capabilities and effectiveness in elderly care.
Author: [‘Efendi A’, ‘Ammarullah MI’, ‘Isa IGT’, ‘Sari MP’, ‘Izza JN’, ‘Nugroho YS’, ‘Nasrullah H’, ‘Alfian D’]
Journal: Health Sci Rep
Citation: Efendi A, et al. IoT-Based Elderly Health Monitoring System Using Firebase Cloud Computing. IoT-Based Elderly Health Monitoring System Using Firebase Cloud Computing. 2025; 8:e70498. doi: 10.1002/hsr2.70498