Smart Desk in Hybrid Classroom: Detecting student's lack of concentration when studying

Students who do not concentrate when studying will find it difficult to absorb the lesson well. Usually, in order for all students to focus on the lesson, the teacher during the lecture will have to observe the students and come up with solutions if the students are not paying attention. However, in the case of many students, following to detect students who have not paid attention to the lesson is a task that requires teachers to put in a lot of effort. In this article, we propose to use machine learning algorithms based on the MediaPipe library to analyze facial features and expressions, including eyes closed, yawning, not looking at the board, or absent, to determine if students have been distracted or not to build a system to assist teachers in detecting student lack of concentration when studying in Smart Desks (Student desks are designed based on embedded devices, with cameras and screens). When detecting that students are not paying attention while studying, the system will warn the teacher so that the teacher can provide solutions. We tested the algorithm on a Jetson Nano embedded device with configuration [Quad-Core 64-bit ARM, 128-bit GPU CUDA, 4GB RAM] and obtained FPS: 8 ~ 18, accuracy achieved from 89 ~ 97% in lighting conditions from 300–400 lux.