AI-Powered Fall Detection Systems: A Solution for Elderly Safety and Well-Being

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Abstract

As the world's population ages, safeguarding the elderly and their health becomes an increasingly important issue. Falls are the most common source of injury for the elderly, and early detection is key to minimizing health hazards and enhancing performance. This work introduces a Fall Detection System integrating motion sensing and location tracking technology to improve geriatric care. With an ESP32 microcontroller, an MPU6050 sensor for motion, and a GPS module, the system tracks movement and detects falls based on analysis of information from the accelerometer and gyroscope of the MPU6050. Upon detection of a fall, the system sends real-time motion immediately to a Blynk app, which alerts caregivers or family members, allowing for an immediate response. Apart from fall detection, the system also records the location of the person using the GPS module, offering real-time latitude and longitude information. This aspect ensures that in the event of a fall, the location of the elderly individual is available, facilitating emergency responders or caregivers to locate them quickly. The Blynk application is a basic and user-friendly system for caregivers to monitor both motion and location data on a smartphone or another device. This dual-purpose system provides a more advanced solution for elderly care, blending fall detection and location tracking to increase safety and peace of mind. The article addresses the design, deployment, and evaluation of the system, and shows how the system can enhance elderly care through prompt fall alert and real-time location data. This technology can serve as an important tool for enabling independence and safety for older adults while providing useful assistance for caregivers and family members.

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Keywords

Artificial Intelligence, real-time monitoring, fall detection