IoT-Based Smart Walking Assistant for Fall Detection in the Elderly

Abstract

This study introduces the design, development, and evaluation of a smart walking assistant tailored to enhance the mobility, safety, and independence of elderly individuals. The system integrates an ESP32 microcontroller to interface with multiple sensors, including the MAX30102 for monitoring heart rate and blood oxygen saturation, the GY-906 infrared sensor for body temperature measurements, and the MPU6050 accelerometer and gyroscope for precise motion tracking and fall detection. A compact and modular control unit, seamlessly integrated into the walker, enables real-time data collection and wireless transmission using LoRa and Wi-Fi technologies. This connectivity facilitates the delivery of alerts to caregivers through a user-friendly mobile application. Rigorous testing, including simulated fall scenarios and physiological parameter measurements, validated the system’s accuracy, reliability, and responsiveness. The results demonstrated high precision in detecting obstacles, falls, and physiological anomalies, while the system’s integration with IoT-based communication platforms ensures timely intervention. The smart walking assistant offers a comprehensive and effective solution, promoting safety and quality of life for elderly users.