Design and Implementation of a Fail-Safe IoT-Based Real-Time Health Monitoring System for Comatose Patients Using ESP32

Abstract

Continuous observation of vital parameters is indispensable for comatose patients, who are unable to notify caregivers of any abnormality. Standard intensive care unit configurations are characterised by accuracy, yet they remain expensive and immobile, and cannot easily be transported. This work presents a cost-efficient, IoT-enabled system that provides real-time patient monitoring using an ESP32 microcontroller and a set of biomedical sensors. The monitor measures heart rate and blood oxygen saturation with a MAX30102 sensor, body temperature with a DS18B20 sensor, and performs electrocardiography and electroencephalography monitoring with an AD8232 amplifier and an AD620 analog amplifier, respectively. In addition, the proposed prototype includes a power management subsystem that switches automatically from mains supply to a backup battery source. The monitor operates over two communication networks: Wi-Fi, which connects the device to the Blynk IoT cloud application, and GSM, which delivers short message alerts through a SIM800L module. This arrangement enables live monitoring and notification of abnormalities, and early testing showed high stability and efficiency of the prototype in terms of data acquisition, alert delivery and visualisation on an OLED interface. The proposed system offers a low-cost, portable and scalable solution suitable for both hospital-based and home-based monitoring of comatose patients.

Introduction

The text presents a fail-safe IoT-based health monitoring system for comatose patients. Since coma patients cannot communicate discomfort, continuous monitoring of vital signs is essential to detect life-threatening abnormalities such as cardiac arrest, low oxygen levels, abnormal temperature, and neurological problems.

Traditional ICU monitoring equipment is accurate but expensive, bulky, and dependent on continuous supervision, making it unsuitable for home use. Existing IoT healthcare systems are more affordable and portable, but many depend on a single communication method and a single power source, which can be dangerous during emergencies.

To address these limitations, the proposed system uses an ESP32 microcontroller connected to multiple biomedical sensors:

• MAX30102 – measures heart rate and SpO?.

• DS18B20 – measures body temperature.

• AD8232 – measures ECG signals.

• AD620-based circuit – acquires EEG signals.

The system processes sensor data using filtering techniques and compares readings with predefined medical thresholds. When abnormal values are detected, it generates alerts through a buzzer, Wi-Fi, and GSM/SMS. Wi-Fi connects to the Blynk cloud platform, while GSM provides an alternative communication channel if Wi-Fi is unavailable.

A key feature is power redundancy. The system uses a two-cell lithium-ion battery, TP5100 charger, and MP1584 voltage regulator to maintain operation during power failures. A DS3231 real-time clock is also used for timestamping measurements, while an OLED display provides local information.

The proposed workflow continuously collects physiological data, filters it, checks for abnormalities, sends normal readings to the cloud, and triggers local and remote alerts when critical conditions are detected.

Initial experiments showed that the hardware operated reliably for 30–40 minutes during power-source switching, with stable voltage, ESP32 operation, and OLED display performance. Sensor testing produced stable measurements, including heart rates of 70–95 bpm, SpO? levels of 96–99%, and temperatures of 36–37°C under normal resting conditions.

Conclusion

This paper has described the development of an Internet of Things based health monitoring system for comatose patients, using an ESP32 microcontroller and several biomedical sensors. The system provides continuous monitoring of heartbeat, SpO?, body temperature and the electrocardiogram, and it has been designed with reliable power management based on automatic switching through diodes. The use of FreeRTOS allows multitasking to be implemented so that data are processed in real time. Alerts have been configured over both Wi-Fi and GSM networks, so that the user can receive notifications in any scenario. The experimental results show reliable operation of the developed system. Overall, the proposed monitoring system is compact and cost-effective and can be used both in hospitals and at home.’

References

[1] S. Abdulmalek, A. Alharthi, Z. Alshammari, et al., “IoT-based healthcare monitoring system towards improving quality of life: a review,” Sensors, vol. 22, no. 15, pp. 1–19, 2022. [2] H. Z. Almarzouki, M. Alsharekh, M. E. Ahmed, et al., “An Internet of Medical Things (IoMT)-based model for real-time monitoring using wearable devices,” IEEE Access, vol. 9, pp. 167777–167788, 2021. [3] Y. H. Li, C. T. Lin, and S. C. Lee, “Real-time cuffless continuous blood pressure estimation using ECG and PPG,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 6, pp. 1901–1910, 2020. [4] X. Ding, B. P. Yan, Y.-T. Zhang, et al., “Pulse transit time-based continuous cuffless blood pressure measurement: extension and evaluation,” Scientific Reports, vol. 7, no. 1, p. 11507, 2017. [5] R. Uddin, S. Hasan, and M. Rahman, “Real-time remote patient monitoring: a review,” Applied Sciences, vol. 14, no. 2, pp. 123–138, 2024. [6] H. L. Yimer, F. Cunico, and A. Giorgi, “IoT-based coma patient monitoring system,” arXiv preprint arXiv:2402.01034, 2024. [Online]. Available: https://arxiv.org/abs/2402.01034 [7] M. S. Tanveer and M. K. Hasan, “Cuffless blood pressure estimation from ECG and PPG using waveform-based ANN-LSTM network,” IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 9, pp. 2161–2171, 2018. [8] M. M. R. K. Mamun, R. Hossain, and A. Khatun, “Cuffless blood pressure measurement using linear and non-linear methods,” Sensors, vol. 22, no. 9, p. 3342, 2022. [9] S. Patel, P. Rao, and N. Kumar, “IoT-based smart ICU patient monitoring system using Wi-Fi and GSM,” [publication details to be completed].

Copyright

Copyright © 2026 Leena Chaudhari, Swaraj Kharmale, Harsh Kapse, Sameeksha Rokade. 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.