Collaborative Big Data Management and Analytics in Complex Systems with Edge 2021 eaCamera: A Case Study on AI-Based Complex Attention Analysis with Edge System - Chaopeng Guo; Peimeng Zhu; Feng Li; Jie Song (Complexity)
As an extension of cloud computing, edge computing makes up for the deficiency of cloud computing to a certain extent. Edge computing reduces unnecessary data transmission and makes a significant contribution to the real-time and security of the system due to its characteristics that are closer to the terminal equipment. In this paper, we study the problem of attention detection. Attentional concentration during some specific tasks plays a vital role, which indicates the effectiveness and performance of human beings. Evaluation of attentional concentration status is essential in many fields. However, it is hard to define the behavior features related to the variety of tasks and behaviors. To solve this problem, we propose an intelligent edge system for attention concentration analysis, eaCamera, to recognize attentional concentration behaviors of students at the edge. To make objective measurements and save the label cost, eaCamera utilizes AI approaches to find the concentration behaviors based on a behavior analysis model with two perspectives, namely, individual perspective and group perspective. Individual perspective indicates personal behavior changes in time dimension while group perspective indicates the changes of the behavior within a group behavior manner. To evaluate the proposed system, a case study is done within a primary school to evaluate student’s performance in the classroom and offer teaching advice for teachers.