Key result
AI-based wearable sensor system proposed to continuously monitor vital signs for real-time cardiovascular risk alerts.
Why the study?
The study was conducted to enhance preventive healthcare, support remote patient monitoring, and improve cardiovascular disease management through an intelligent and cost-effective system.
The paper describes a proposed AI and IoT-based wearable system for continuous monitoring of vital signs to predict heart attack risk and generate real-time alerts.
Proposed AI-IoT cardiovascular risk system is preliminary; leaves open need for prospective validation before any clinical use.
If you're uploading your research paper to Zenodo, you can use this description: Description: This paper presents an AI-based Heart Attack Risk Prediction System that combines Internet of Things (IoT) technology with Machine Learning techniques for early detection of cardiovascular risk. The proposed system utilizes wearable sensors, including MAX30102 and MLX90614, connected to an ESP32 microcontroller to continuously monitor vital health parameters such as heart rate, blood oxygen saturation (SpO₂), and body temperature. The collected data is transmitted to a cloud platform where machine learning algorithms analyze the information and predict the likelihood of a heart attack. In high-risk situations, the system generates real-time alerts to users and healthcare providers, enabling timely medical intervention. The project aims to enhance preventive healthcare, support remote patient monitoring, and improve cardiovascular disease management through intelligent and cost-effective technology.
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Patil et al. (2026) studied Cardiovascular risk. AI-based Heart Attack Risk Prediction System was evaluated. An AI-based Heart Attack Risk Prediction System utilizing wearable sensors and machine learning was proposed to continuously monitor vital signs and generate real-time alerts for cardiovascular risk.
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