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基于物联网并使用Firebase云计算的老年健康监测系统

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

作者信息

Efendi Adhan, Ammarullah Muhammad Imam, Isa Indra Griha Tofik, Sari Meli Puspita, Izza Jasmine Nurul, Nugroho Yohanes Sinung, Nasrullah Hamid, Alfian Denny

机构信息

Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taiwan.

Department of Mechanical Engineering, Faculty of Engineering Universitas Diponegoro Semarang Central Java Indonesia.

出版信息

Health Sci Rep. 2025 Mar 2;8(3):e70498. doi: 10.1002/hsr2.70498. eCollection 2025 Mar.

DOI:10.1002/hsr2.70498
PMID:40041774
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11873372/
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.

摘要

背景与目的

老年人口的不断增加给医疗保健系统带来了重大挑战，因此需要创新的解决方案来进行持续的健康监测。本研究开发并验证了一种基于物联网的老年监测系统，旨在提高老年人的生活质量。该系统具有一个强大的基于安卓的用户界面，并与Firebase云平台集成，可确保实时数据收集和分析。此外，还实施了一种监督式机器学习技术，以根据实时参数对观察到的用户处于“稳定”或“不稳定”状态进行预测任务。

方法

系统架构采用物联网层，包括物理层、网络层和应用层。通过让六名参与者测量心率、血氧饱和度和体温的实时数据来进行设备验证，然后通过平均百分比误差（MAPE）进行分析以确定错误率。通过分析评估指标进行对比实验，以确定要部署到系统中的最佳监督式机器学习模型。同时，从可用性、舒适性、安全性和有效性方面评估用户满意度。

结果

已构建基于物联网的老年健康监测系统，各参数的平均百分比误差为0.90%，其中心率为1.68%，血氧饱和度为0.57%，体温为0.44%。机器学习实验表明，基于准确率和F1分数的评估指标，XGBoost模型具有最佳性能，分别为0.973和0.970。在基于可用性、舒适性、安全性和有效性的用户满意度方面，获得了86.55%的高评分。

结论

该系统为老年用户和护理人员提供了实际应用，能够实时监测健康状况。未来的改进可能包括与机器学习和深度学习等人工智能技术集成，以便从数据模式中预测健康状况，进一步提高系统在老年护理方面的能力和有效性。

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本文引用的文献

1
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Sci Rep. 2023 Nov 6;13(1):19213. doi: 10.1038/s41598-023-46322-6.
2
Remote Health Monitoring Systems for Elderly People: A Survey.老年人远程健康监测系统：调查。
Sensors (Basel). 2023 Aug 10;23(16):7095. doi: 10.3390/s23167095.
3
Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges.基于物联网的患者监测中的洞察解析：应对大数据挑战的方法。
Sensors (Basel). 2023 Jul 28;23(15):6760. doi: 10.3390/s23156760.
4
Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT.基于 IMU 可穿戴设备的跌倒识别和通过智能扬声器及物联网进行的跌倒验证。
Sensors (Basel). 2023 Jun 9;23(12):5472. doi: 10.3390/s23125472.
5
The Application of Internet of Things for the Elderly Health Safety: A Systematic Review.物联网在老年人健康安全中的应用：一项系统综述。
Adv Biomed Res. 2023 Apr 27;12:109. doi: 10.4103/abr.abr_197_22. eCollection 2023.
6
A novel gateway-based solution for remote elderly monitoring.一种基于网关的新型远程老年监护解决方案。
J Biomed Inform. 2020 Sep;109:103521. doi: 10.1016/j.jbi.2020.103521. Epub 2020 Jul 31.
7
An IoT System for Remote Health Monitoring in Elderly Adults Through a Wearable Device and Mobile Application.一种通过可穿戴设备和移动应用程序对老年人进行远程健康监测的物联网系统。
Geriatrics (Basel). 2019 May 7;4(2):34. doi: 10.3390/geriatrics4020034.
8
Health monitoring through wearable technologies for older adults: Smart wearables acceptance model.通过可穿戴技术进行健康监测：老年人的智能可穿戴设备接受模型。
Appl Ergon. 2019 Feb;75:162-169. doi: 10.1016/j.apergo.2018.10.006. Epub 2018 Oct 25.