Authors : Poushali Das; Supratik Chatterjee; Rituparna Mondal; Debasmita Dutta; Upama Bose; Sudeshna Dey; Siddhartha Chatterjee

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/9pbkrrzf

Scribd : https://tinyurl.com/4b32ezse

DOI : https://doi.org/10.38124/ijisrt/26jan1371

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rising levels of particulate matter in city environments pose a significant risk to public health, especially concerning sudden cardiopulmonary issues. Conventional monitoring systems typically focus on air pollutants alone, neglecting individual physiological susceptibilities. This study introduces an innovative Intelligent Geofenced Cardiopulmonary Health Framework that combines environmental IoT sensors with real-time monitoring of physiological data through wearables. By utilizing the Haversine formula for accurate spatial geofencing, the system links localized Air Quality Index (AQI) metrics—specifically PM2.5 and CO— with real-time cardiac and respiratory indicators, such as Heart Rate (HR), Heart Rate Variability (HRV), and Oxygen Saturation (SpO2). The proposed system employs a Random Forest (RF) ensemble classifier to integrate multimodal data into a comprehensive Total Health Risk Index (THRI), while a Long Short-Term Memory (LSTM) network offers predictive insights into potential respiratory and cardiac stress events. To facilitate rapid intervention, an Edge-AI strategy is used, which sends automatic, personalized health alerts via Firebase Cloud Messaging (FCM) when physiological limits are exceeded within high -pollution geofenced areas. Experimental findings demonstrate that combining biological feedback with environmental geofencing greatly enhances the accuracy of health interventions compared to static AQI monitoring. This research offers a scalable, user-focused approach to precision environmental medicine, effectively linking urban IoT infrastructure with personalized cardiovascular protection.

Keywords : Geofenced Health Monitoring, Cardiopulmonary Biomarkers, Wearable IoT Sensors, PM2.5 Air Quality, Edge Artificial Intelligence (Edge-AI), Random Forest–LSTM Hybrid Model, Total Health Risk Index (THRI).

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