IoT Based Sunstroke Detection System for Healthcare

Abstract

The increasing prevalence of heat stroke, caused by prolonged exposure to extreme temperatures, has emerged as a critical global health concern. This research article proposes an IoT-based simulation model for sunstroke detection that integrates real-time environmental monitoring with human vital parameters. The system uses DHT11 and DS18B20 sensors to measure ambient and body temperatures, respectively, while a pulse sensor monitors heart rate. A NodeMCU microcontroller processes the collected data and communicates with the Blynk Cloud API to trigger timely alerts when threshold values are exceeded, indicating sunstroke risks. The simulation was conducted using Proteus Design Suite, and the system was validated against multiple scenarios, demonstrating its effectiveness in providing real-time health monitoring. The results highlight the system's ability to identify abnormal conditions early and generate timely alerts, which is crucial for preventing heat-related health complications. This work emphasizes the significance of cost-effective IoT solutions for environmental healthcare, particularly in regions prone to extreme heat conditions. Future advancements include integrating predictive machine learning algorithms to enhance the system's reliability and accuracy.