Wireless Sensor Network based Fall Detection and Emergency Alert System using Deep Learning

Falls among the elderly and individuals with mobility impairments pose significant health risks, often leading to severe injuries or fatalities. Wireless Sensor Networks (WSNs) have emerged as a promising solution to monitor the well-being of these individuals in real-time. This paper presents a Fall Detection and Emergency Alert System based on WSNs, integrated with deep learning algorithms to provide accurate and timely alerts for fall incidents. The system utilizes a network of sensors embedded in wearable devices or environmental installations to capture movement and activity data. Machine learning models, particularly deep learning techniques such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are employed to analyze sensor data and accurately detect falls. Once a fall is detected, the system triggers an emergency alert, notifying caregivers, family members, or medical personnel through mobile apps or automated messaging systems. The proposed system enhances the safety of vulnerable individuals by offering real-time monitoring and rapid response capabilities, reducing the risks associated with delayed fall detection. Experimental results demonstrate the high accuracy and reliability of the deep learning-based fall detection system, making it a valuable tool for health monitoring in a smart healthcare environment.