Enhancing Elderly Safety: A Wearable Fall Detection System with IoT and Machine Learning

Falls represent a significant health concern, especially among the elderly and individuals with mobility impairments, often resulting in severe injuries or fatalities. Current fall detection solutions, such as webcam-based systems and manual alert devices, are often costly, raise privacy issues, and are prone to false alarms, highlighting a critical need for a more effective, reliable, and affordable solution. This project presents a wearable fall detection and emergency alert system integrating the ESP32 microcontroller, MPU6050 accelerometer and gyroscope, NEO-6M GPS module, and a Support Vector Machine (SVM) algorithm. The system aims to provide real-time fall detection and instant alerts to caregivers or emergency services, enhancing safety for vulnerable individuals, especially the elderly. The MPU6050 sensor monitors body movements, while SVM processes data to distinguish falls with 96% accuracy. The NEO-6M GPS module provides precise location tracking, and the ESP32 transmits alerts via Wi-Fi to the Blynk application, ensuring a timely emergency response. Comprehensive testing demonstrates high accuracy and reliability. This integration of machine learning, IoT, and advanced sensors offers an innovative and practical solution for fall detection in healthcare applications.