Research on entertainment robots based on artificial intelligence interaction for human posture recognition and sports activity monitoring

•This article analyzes the research on human pose recognition and sports activity monitoring of entertainment robots.•The results show that the attitude recognition accuracy can be improved by adding the dynamic Bayesian network.•The sports activity monitoring system proposed in this paper can comprehensively monitor students’ sports activities. With the application of artificial intelligence technology in sports activity monitoring, new technologies such as interactive robots and the Internet of Things are being more widely used, which can provide entertainment and interactive guidance for sports training. This article analyzes the research on human pose recognition and sports activity monitoring of entertainment robots based on artificial intelligence interaction. This paper designs and develops an entertainment robot system that can recognize human posture and monitor physical activities. Users can interact with the robot and get real-time feedback about their posture and activities, thereby improving motor skills and physical fitness. After in-depth analysis of the theory of dynamic Bayesian network, we used EM algorithm to build a dynamic Bayesian network model, carried out detailed research on the random variables represented by the nodes of the dynamic model, and used the Internet of Things human posture recognition technology to extract the features of human behavior according to the characteristics of human behavior. The results show that the attitude recognition accuracy can be improved by adding the dynamic Bayesian network based on the random forest algorithm. The entertainment robot can accurately identify human posture and monitor sports activities, and has good real-time and stability. Through interaction with the robot, users can get personalized suggestions for individual postures and activities, improving the training effect and enjoyment of physical activities. Therefore, through the in-depth research and analysis of the dynamic Bayesian network and the human posture recognition of the Internet of Things, it is found that the sports activity monitoring system proposed in this paper can comprehensively monitor students’ sports activities, so as to achieve the goal of comprehensively improving students’ physical fitness.