AI-Enabled IoT Health Monitoring System

Abstract

This study outlines that how a health monitoring system based on IoT technology implemented using an ESP32 microcontroller. Unlike conventional techniques, this system captures real-time data by attaching low-cost sensors (i.e Max30100,Ds18b20) that measure heart pulse rate, blood oxygen concentration, and skin temperature. These physiological parameters are detected using MAX30102 and DS18B20 respectively. The data collected from the sensors is then transmitted wirelessly to the locally hosted Flask server for receiving and processing. This data is stored in a local database and can be viewed instantly using dashboard interfaces presented on browsers, which display live data along with historical data analysis. In addition to monitoring, this system predicts diseases using a disease prediction algorithm based on the physiological parameters captured from sensors. During evaluation, it was found that the developed system was able to capture data using sensors effectively, had minimum communication delays, and visualized the results effectively.

Introduction

The Internet of Things (IoT) has significantly transformed healthcare by enabling continuous, remote, and real-time patient monitoring through connected devices, sensors, and wireless communication technologies. Traditional healthcare methods rely on frequent hospital visits and often fail to provide continuous observation, especially for elderly patients, individuals with chronic diseases, and people living in remote areas.

Modern IoT-based healthcare systems use biomedical sensors to monitor vital signs such as heart rate, blood oxygen saturation (SpO?), and body temperature. These measurements are transmitted wirelessly to computing platforms, allowing healthcare professionals to monitor patients remotely, reduce healthcare costs, and improve service efficiency. The proposed system utilizes an ESP32 microcontroller, MAX30102 sensor for heart rate and SpO? monitoring, and DS18B20 sensor for temperature measurement. Sensor data is sent to a Flask-based backend and displayed through a web dashboard for real-time visualization and analysis.

IoT has expanded healthcare services through wearable devices, smart applications, and remote monitoring systems. It enables doctors to access patient information through mobile devices, respond quickly to emergencies, and provide continuous care without hospitalization. Effective IoT healthcare systems should offer real-time monitoring, long battery life, affordability, accessibility for elderly and chronic patients, and user-friendly interfaces.

Recent research has explored various IoT healthcare architectures, including cloud-based systems, local-server solutions, and AI-integrated monitoring frameworks. Studies have demonstrated the use of machine learning and artificial intelligence for disease prediction, emergency response, and intelligent decision-making. However, challenges such as data privacy, security, interoperability, scalability, and limited AI integration still remain.

The proposed system addresses these challenges by combining IoT monitoring with machine learning. Biomedical sensors collect physiological data, which is processed by the ESP32 and transmitted to a server. A Random Forest machine learning model analyzes multiple health indicators simultaneously to predict potential diseases and detect abnormalities more accurately than traditional threshold-based methods. The system also supports cloud or hybrid data management, remote accessibility, and real-time health monitoring, making it a cost-effective and intelligent healthcare solution.

Conclusion

This project reports the development and operation of an AI enabled IoT health care monitoring system which we built using low cost and easily accessible components. We used sensors, ESP32 microcontroller, and wireless technology in the design which in turn enable us to collect and transmit vital signs like heart rate, SpO2, and temperature in real time. Also we incorporated a Random Forest machine learning model into the system for intelligent data analysis which in turn enables us to predict diseases and to detect anomalies. The present system reports on how we have seen great progress in embedded systems, low cost sensors, and internet connectivity which in turn has made remote health monitoring a practical and scalable option. We also present our web dashboard which in time continuously displays patient vitals and at the same time enables timely decision making and remote supervision. Also as a whole our proposed solution is very much put forth to improve the state of patient care, which is very true in the case of the elderly and chronically ill patients that benefit from a continuous health surveillance. In the future as we see ourselves adding in cloud storage, alert systems, and multi- patient support the system will turn into a strong platform for smart healthcare and will be an asset in preventative medicine and broad scale health services.

References

[1] N. N. Thilakarathne, W. D. M. Priyashan, and C. P. Premarathna, “Artificial intelligence - enabled IoT for health and wellbeing monitoring,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021, pp. 01–07. [2] S. Jegadeesan, P. Prasanth, P. Pradeep, P. P. Kumar, and B. Rahul, “AI-enabled efficient health monitoring system using IoT-based WBAN,” in 2024 4th Asian Conference on Innovation in Technology (ASIANCON), 2024, pp. 1–6. [3] S. Chowdhury et al., “An IoT-based wearable healthcare monitoring device and medical emergency response system,” in 2024 IEEE Students Conference on Engineering and Systems (SCES), 2024, vol. 10, pp. 1–5. [4] S. Farhan, P. Das, and S. Shabab, “IoT-based health monitoring system for real-time vital signs and hospital room conditions tracking,” in 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), 2025, pp. 1–6. [5] M. Pawar, S. Rusia, S. Tijare, A. Gadpalliwar, M. Ashtekar, and R. Pichkate, “Development of health monitoring wearable device using ESP32,” in 2024 International Conference on Inventive Computation Technologies (ICICT), 2024, pp. 2007–2012. [6] D. L. Reddy, M. R. Naik, and D. Srikar, “Health monitoring system based on IoT,” in 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), 2021, pp. 468–472. [7] T. H. Orpa, A. Ahnaf, T. B. Ovi, and M. I. Rizu, “An IoT based healthcare solution with ESP32 using machine learning model,” in 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), 2022, pp. 1–6. [8] M. Shakir, A. Arshad, M. O. Tariq, U. Sadiq, and U. Shabbir, “Development of IoT based smart system and data acquisition for patient monitoring,” in 2024 International Conference on Engineering and Emerging Technologies (ICEET), 2024, pp. 1–6. [9] Lata, P., et al. \"An Effective Investigation on Health Cloud Based IoT Based Virtual Health Monitoring System.\" 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. [10] Al-Zidi, Nasser M., et al. \"Smart system for real-time remote patient monitoring based on internet of things.\" 2021 2nd International Conference on Computational Methods in Science & Technology (ICCMST). IEEE, 2021. [11] J. Wan, M. A. A. H. Al-awlaqi, M. Li, M. O’Grady, X. Gu, J. Wang, and N. Cao, “Wearable IoT enabled real-time health monitoring system,” EURASIP Journal on Wireless Communications and Networking, vol. 2018, no. 1, pp. 1–10, 2018, doi: 10.1186/s13638-018-1308-x. [12] L. K. Ramasamy, F. Khan, M. Shah, B. V. V. S. Prasad, C. Iwendi, and C. Biamba, “Secure smart wearable computing through artificial intelligence-enabled Internet of Things and cyber-physical systems for health monitoring,” Sensors, vol. 22, no. 3, p. 1076, 2022, doi: 10.3390/s22031076. [13] M. Alshamrani, “IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 7, pp. 4687–4701, 2022, doi: 10.1016/j.jksuci.2021.06.005. [14] Y. Qian and K. L. Siau, “Advances in IoT, AI, and sensor-based technologies for disease treatment, health promotion, successful ageing, and ageing well,” Sensors, vol. 25, no. 6207, 2025, doi: 10.3390/s25196207.

Copyright

Copyright © 2026 Rishav Gupta, Uddeshya Rawat, Shobha , Subham Kumar, Sahil K Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.