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@article{199797,
        author = {Dr.M.Suganthi and S.Harshavarthini and K.Myithili and T.Gayathri Priya},
        title = {AI-Based Wearable ECG Chest Band for Real-Time Heart Monitoring and Early Abnormality Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15932-15938},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199797},
        abstract = {Cardiovascular diseases (CVDs) are one of the leading causes of death globally, accounting for millions of fatalities each year. Early detection and continuous monitoring of heart conditions can significantly reduce mortality rates. Traditional diagnostic systems such as electrocardiograms (ECG) and Holter monitors are often expensive, bulky, and require hospital visits, making them unsuitable for continuous real-time monitoring.
This paper presents an AI-enabled wearable chest band designed for continuous heart monitoring and real-time classification of heart diseases. The system utilizes a photoplethysmography (PPG)-based optical sensor (MAX86141) to capture physiological signals. A biometric sensor hub (MAX32664) processes these signals to extract key parameters such as heart rate and oxygen saturation. An ESP32 microcontroller is used for further processing and wireless communication.
Machine learning algorithms are implemented to classify heart conditions such as normal rhythm, tachycardia, bradycardia, and arrhythmia. The processed data is transmitted to a mobile or cloud platform using Bluetooth or Wi-Fi, enabling remote monitoring and early diagnosis.
The proposed system is portable, cost-effective, non-invasive, and suitable for home healthcare and telemedicine applications},
        keywords = {Artificial intelligence, photoplethysmography, wearable devices, arrhythmia detection, IoT healthcare, real-time monitoring, embedded systems.},
        month = {April},
        }