Elderly Fall Detection Using CCTV Cameras under Partial Occlusion of the Subjects Body

Activity Classification and Fall Detection Using Monocular Depth and Motion Analysis

“…Ethical considerations are essential in this context, emphasizing the need to prioritize user privacy and obtain explicit consent from the monitored person[40]-[44].• The presence of furniture in the surroundings can create obstacles, blocking the camera's view and leading to occlusion of the person, which may hinder accurate fall detection. Addressing challenges related to occlusion and developing algorithms capable of handling partial visibility of individuals are critical tasks[45],[46].• Variations in lighting conditions can alter the input characteristics and impact the real-time performance of fall detection. Consequently, robust algorithms are indispensable to mitigate the influence of different lighting conditions, including low light or intense backlighting, to ensure the system's performance [47]-[49].…”