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@article{195637,
        author = {Diya P Nair and Ibthesam Sheikh and Gurusudhanva and Prajokta  Chakraborty and Dr M Kumaresan},
        title = {Design and Architectural Framework of Smart Health Monitoring System with AI-Driven Alerts Using IoT and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1572-1575},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195637},
        abstract = {The main aim of this project is to design a smart health monitoring system that uses the Internet of Things and machine learning model. The system collects real-time vitals of the user such as heart rate, oxygen saturation with a wearable compact hardware. The main innovation is the usage of a machine learning model (Random Forest) that will process the collected data and detect anomalies. If the system finds an anomaly it will send an alert to a user, family member or healthcare. Sensors like MAX30102, MAX86150 are used to acquire photoplethysmography (PPG), heart rate (HR) , electrocardiography (ECG) from the user. ESP32 is the microcontroller used as it integrates both wifi and bluetooth capabilities. Random Forest Algorithm is used on the publicly available cardiac dataset and the data from the sensor is classified as normal or anomalous. If any anomaly is detected by the machine learning model the ESP32 gives an alert to the GSM module which gives a call to the emergency contacts or healthcare. A technical architecture of a wearable health monitor is proposed in this paper.},
        keywords = {Smart Health Monitoring System, IoT, ESP32, Random Forest, Machine Learning Algorithm, Heart Attack Detection, Healthcare, Anomaly},
        month = {April},
        }