Estimating Attention Level from Blinks and Head Movement

Abstract

In this paper, we develop a real-time method for estimating the level of attention while performing a task. This method uses only a low frame rate video from a standard camera so that it can be available even on a small computer. Eye blinks and head movements are calculated from a video by using landmarks. Existing blink detection methods use standard frame rate videos, making them difficult to process on a computer with low processing performance. One obvious solution is to use videos with a reduced frame rate. We investigate the error caused by reducing the frame rate, and then to overcome the error, we further develop a new method that uses the head movements calculated from the reduced frame rate videos. Then we demonstrate the error is within acceptable ranges by using the method, and show it is effective to estimate the attention level. Since this method uses only landmark information obtained from facial images, it reduces the mental burden on the user, and also partially protects personal information. In this paper, we explain the details of the method and report the experimental results.

Exploring Patterns of Generative AI Utilization in Education

“…Additional possibilities include utilizing videos captured during the learning process and physiological data gathered from sensors. Reference [13] proposes a technique for estimating the degree of attention by extracting blink information from videos of students engaged in learning. This method can help determine whether students are inappropriately relying on GAI while tackling a task.…”