Falls are the leading cause of injury-related deaths among adults aged 65 and older. The age-adjusted fall mortality rate increased by 41%, from 0.0553% in 2012 to 0.0780% in 2021. The models OpenPose and MoveNet can be used to detect human movements associated with falls in dynamic images. In this study, we utilized these AI models to independently identify falls among elderly individuals living alone and compared the efficiency of these two AI fall detection models in capturing dynamic images. In this study, we employed a web camera as a sensor and integrated it with the two systems mentioned above to detect human limb movements, determine whether the monitored individual has fallen, and send emergency notifications to family members, caregivers, and nursing staff via communication software. AI-based posture detection technology is vital for elderly individuals who live alone, have poor health, or have limited mobility. Our evaluation of the detection efficiency of different AI fall detection models provides valuable references for applications in healthcare systems.