Falls are a frequent and critical event among older adults, often leading to severe consequences such as injuries, loss of independence, fear of walking, and even death. Therefore, it is essential to detect and prevent them. AI services in homes and nursing facilities enable early fall detection and timely support. Another major concern in nursing homes and clinics is the frequency of bed-exit attempts by residents or patients who cannot stand independently. These individuals often overlook their limitations and attempt to get out of bed alone, increasing the risk of falling. Our approach focuses on bed-exit detection to notify caregivers in real time, potentially preventing these incidents. We present a system that uses depth video to detect falls and bed-exit attempts among older adults. A configuration interface is developed to provide seamless access to fall and bed-exit detection functionalities and their results. The interface is compatible with various operating systems and optimized for both CPU and GPU versions, making it suitable for home and nursing facility applications. Our system achieves 68% accuracy and a 69% F1-score for fall detection, and 82% accuracy and an 88% F1-score for bed-exit detection. Room occupancy detection, which determines whether an older adult is present in the room, achieved 92% accuracy and an 89% F1-score. This work demonstrates significant potential to enhance services that improve assisted living environments.