Behavior detection of the elderly based on language model

With the advancement of society, the phenomenon of population aging has evolved into a shared concern among nations worldwide. In response to this pressing challenge, this paper proposes the development of a predictive model for identifying risk events among the elderly. By scrutinizing behavioral patterns and instituting early warning mechanisms for potential hazards, the model aims to discern intricate and individual behaviors, thus bolstering the safety of elderly individuals, particularly those residing alone. Leveraging Kinect camera technology, the model captures human skeletal data alongside YuNet and MnasNet for facial information extraction. This amalgamation of data interprets the language of human body status, subsequently translated into natural language through the employed language model, facilitating high-precision real-time monitoring.