Research and design of an attention monitoring system based on head posture estimation
Hong Wang, Xia Yu · 2021
Attention is one of the essential factors affecting learning effectiveness. The duration students focusing on the screen is a fundamental measure of concentration in the relaxed setting of using computers for experiments in universities. To effectively monitor students’ attention in experimental teaching, we propose a method for measuring students’ attention based on head posture estimation and provide a scheme design for a Raspberry Pi-based attention monitoring system by integrating the idea of edge computing. Firstly, the head angles of yaw and pitch are estimated using ERT and EPnP algorithms to determine whether the students’ eyesight is within the screen range in the Raspberry Pi. Secondly, the results are transmitted wirelessly to the central server. To this end, a visual web page of the students’ attention is provided based on these data to show and manage those devices. Experiments have shown that the system is feasible in detecting student attention and has broad application potential.