IoT-Based Patient Fall Detection and Emergency

Abstract

The IoT-Based Patient Fall Detection and Emergency Alert System provides a smart healthcare framework that automatically identifies patient falls and generates immediate notifications for assistance. The proposed design employs an ESP32 microcontroller connected to an MPU6050 accelerometer and gyroscope sensor to continuously capture motion and orientation data. Using threshold analysis, the system differentiates between normal activities and fall events. When a fall is detected, it triggers a buzzer to alert nearby individuals and sends real-time data to the Blynk Cloud platform through Wi-Fi. Caregivers can remotely monitor patient activity and receive instant alerts via the Blynk mobile application, allowing quick medical response. This approach minimizes manual supervision, improves response time, and enhances patient safety, especially for elderly or physically challenged individuals. The system is developed to be cost-effective, dependable, and easily expandable, making it well-suited for use in smart healthcare setups and remote patient observation applications.