An IoT-Based Vital Signs System for Early Heart Attack Detection
DOI:
https://doi.org/10.69996/jsihs.2025017Keywords:
Real-Time Monitoring, IoT-Enabled Healthcare Systems, Deep Learning Algorithms, Wearable Sensors, Real-Time Alert SystemsAbstract
Heart disease, particularly heart attack, remains a leading cause of mortality worldwide, necessitating the development of accurate, real-time early detection systems. This study proposes a realtime heart attack monitoring and prediction system that integrates the Internet of Things (IoT) and advanced Deep Learning (DL) techniques—two powerful and trending technologies, especially in the field of healthcare. Critical physiological data, including Systolic Blood Pressure (SysBP), Diastolic Blood Pressure (DBP) and Heart Rate (HR), are collected continuously in real time using wearable IoT sensors that provide immediate data transmission to the ThingSpeak cloud for timely monitoring of vital signs, while also locally processing the data to trigger immediate alerts. Immediate alerts are triggered by the system when abnormal signs are detected. The system concurrently transmits notifications to the patient’s family or care providers in case of heart attack or fall detection, and a buzzer with an accelerometer sensor is activated in the event of a fall.The trained model and incoming patient data from the cloud are continuously loaded into the customized prediction script for real-time analysis and heart attack prediction. In real-time testing involving 373 data entries, the model maintained a prediction accuracy of 93.55%, showcasing its stability and scalability for continuous data streams. The overall IoT system’s average response time was approximately 3.889 seconds, reflecting efficient data processing and timely alert generation. These results validate the proposed IoT-enabled DL system as an effective and efficient solution for continuous heart attack monitoring and early warning.The proposed system’s realtime predictive capability and rapid alert mechanism represent a significant advancement in remote cardiovascular monitoring, offering a practical solution for early intervention and potentially reducing heart attack-related mortality.
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