Smart Device Fall Detection System for Real-Time Monitoring and Emergency Response for Specially Abled Individuals

Fall detection systems are essential for protecting specially-abled individuals and the elderly, who face an increased risk of injury due to falls. This paper presents the design and implementation of a Smart Device Fall Detection System aimed at real-time monitoring and emergency response, supported by a dedicated Data Analysis Module for post-fall analysis. The system integrates multiple sensors, including an accelerometer, gyroscope, pressure sensor, cardiotachometer, and GPS module, all connected through an ESP32 microcontroller. It continuously monitors users’ movements and physiological data, which are transmitted to the Blynk cloud service for analysis. The algorithm processes the sensor data to detect fall events and trigger emergency alerts. The Data Analysis Module further analyzes fall data to extract patterns and trends, enhancing the understanding of fall incidents and informing preventive measures. The proposed system was evaluated through various fall and non-fall scenarios, achieving an impressive accuracy of $97.9 \%$ and a response time of just 1.45 seconds. Compared to existing solutions, this system demonstrates enhanced performance in fall detection accuracy, sensitivity, and emergency response efficiency. Ultimately, the Smart Device Fall Detection System, combined with the Data Analysis Module, offers a practical and scalable solution to improve the safety and well-being of individuals at risk of falls, paving the way for further advancements in fall prevention technology.