The growing global elderly population and increasing number of individuals with disabilities highlight the urgent need for effective fall detection systems. Falls among elderly and disabled individuals pose significant health risks, especially for those living alone. Traditional methods of fall detection are often intrusive and unreliable, leading to a demand for more efficient, non-intrusive solutions. This review explores the application of advanced deep learning (DL) algorithms, particularly the YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and RetinaNet models, for human fall detection. These computer vision-based models offer real-time, high-accuracy detection and have demonstrated great potential in improving the safety and quality of life for vulnerable individuals. The paper delves into the strengths, challenges, and applications of each algorithm, discussing how these models are transforming fall detection systems for assistive living environments.

Keywords: Fall detection, YOLO, SSD, RetinaNet, deep learning, computer vision, assistive living, elderly care, non-intrusive detection, real-time systems.