IoT- Heart Attack Detection and Heart Monitor with Automatic Ambulance Alert System

Abstract

In recent years, the application of Internet of Things (IoT) technology in the healthcare sector has significantlyenhanced the monitoring and management of chronic illnesses and emergency health situations. This projectaims todevelopareal-timeIoT-basedsystemforheartattackdetectionandcontinuousheartratemonitoring.Thesystemcontinuously tracks the user’s heart rate and other vital parameters to identify early signs of a heart attack. Using sensorssuch as ECG (Electrocardiogram) and PPG (Photoplethysmography), it collects comprehensive heart activity data, which isprocessed and analyzed by a microcontroller integrated with a Wi-Fi module (ESP8266 or ESP32). The collected data istransmitted to a cloud server, where machine learning algorithms analyze it to detect irregular patterns and potential cardiacrisks. In the event of an abnormality, the system promptly alerts the user, caregivers, and healthcare professionals through amobileapplication,enablingtimelymedicalresponse.Thisapproachnotonlystrengthenspreventivehealthcareandemergencyresponse but alsosupports remote patient monitoring, offeringascalable and efficient solution toreducecardiac-related fatalities.

Introduction

Heart disease—especially heart attacks—remains a major global health challenge, often becoming fatal due to the absence of continuous monitoring for high-risk individuals. Traditional ECG and heart rate monitoring rely on clinical visits and bulky equipment, limiting real-time detection. The emergence of IoT has transformed healthcare by enabling wearable, connected devices capable of continuously collecting, processing, and transmitting vital health data outside hospital settings.

This project proposes an IoT-based heart attack detection and heart rate monitoring system that integrates ECG and PPG sensors with a microcontroller (Arduino/ESP32). The system acquires heart signals, filters them, analyzes abnormalities, and sends data to the cloud via Wi-Fi. Machine learning algorithms on the cloud identify irregular heart patterns that may signal a potential heart attack. When abnormalities are detected, alerts—with GPS location—are automatically sent to family members, doctors, or emergency services. Cloud-based data logging supports long-term medical analysis.

Normal heart rate varies across age groups: newborns have the highest rates (100–160 bpm), while adults average 60–100 bpm; athletes often show lower values due to superior cardiovascular fitness. Monitoring deviations from these ranges is critical for early detection.

A literature review shows rapid advancement in IoT-healthcare systems using AI, deep learning, wearable sensors, and cloud analytics for cardiac risk prediction and continuous monitoring.

Future improvements may include AI-driven predictive diagnosis, 5G-enabled real-time transmission, integration with wearable devices, improved data security, lower power consumption, and expanded access in rural areas. Overall, IoT-based systems have the potential to enable early heart attack detection, support continuous monitoring, and save lives by providing timely medical intervention.

Conclusion

The IoT-based Heart Attack Detection and Heart Monitoring System with Automatic Ambulance Alert is an innovative solution designed to save lives by enabling real-time health monitoring and instant emergency response. By continuously tracking vital signs and detecting abnormalities, the system can alert medical personnel and dispatch an ambulance automatically, reducing response time during critical situations. This project demonstrates how IoT and smart healthcare technologies can work together to provide timely assistance, improve patient safety, and pave the way for more advanced, connected healthcare systems in the future.

References

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Copyright

Copyright © 2025 Mrs. Poonam B. Bhopale, Ms. Sarika Yuvraj Khot, Ms. Nandini Maruti Kurhade, Ms. Nivasini Chandrakant Gurav, Ms. Mansi Bhauso Magdum. 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.