Fall Detection

Fall detection research aims to develop accurate and efficient systems for identifying falls, particularly among the elderly, to enable timely intervention and prevent injuries. Current research heavily utilizes computer vision techniques, employing deep learning models like YOLO variants, convolutional neural networks (CNNs), and recurrent neural networks (RNNs, including LSTMs), often combined with other sensor data (e.g., inertial sensors, radar, Wi-Fi signals) for improved accuracy and robustness. These advancements hold significant potential for improving healthcare, particularly in ambient assisted living, by providing automated fall detection and response systems that enhance safety and independence for vulnerable populations.