Research On The Application Of Attention Detection Technology Based On Brain Wave And Computer Vision In E - Learning

With the fast development of the information and the internet technologies, the educational model has grown beyond the traditional model of classroom teaching. Nowadays more and more E-learning platforms have been used in the fields of education and enterprise training. Comparing from the traditional model, the E-learning model has the splendid point that it can break the constraint of the fixed time and space, it can actually provide learners a learning platform at anytime and anywhere. However, one disadvantage of all the existing E-learning systems is the lack of the affective interaction. That means students in E-learning system separated from other students and teachers, which makes the learners more easier to divert their attention from the learning, so as to reduce learning efficiency.In order to solve this problem, this paper proposed two kinds of methods to detect learner’s attention in the E-learning system:detection based on computer vision and detection based on brain waves. For the detection based on computer vision, this paper first introduces face detection and eyes detection, and then focuses on the feasible solution of combining face detection and eyes detection to detect the learner’s attention. This solution utilizes the adaboost algorithm based on haar classifier in OpenCV computer vision library; the results of the detection experiments proved the reliability and usability of the solution._For the detection based on brain waves, the paper summarizes the previous methods in brainwave collection, and then focuses on the superiority of wearable EEG equipment. In this study, Mindwave (a wearable EEG device from NeuroSky company) is used to get the learner’s brain wave data, and the learner’s attention status are valued based on these data.In this paper, the attention distraction is divided into three conditions:explicitl, explicit2, and implicit. Explicit conditions describes the attention distraction accompany with head turn or eye closing; Implicit condition describe the attention distraction without visual signs. Experiments are conducted to test the detection rate of the two methods (i.e., computer vision and brain waves) at the above three conditions. Merit and demerit of the two methods were analyzed based on the experiment results. At last, the advantage of combing the two methods were also proved by the experiment results.Three contributions of the present research on attention detection are as follows:First, by combining face detection and eye detection, a new computer vision method for attention detection was proposed.Second, a new attention detecting method based on wearable EEG device was proposed. If the detected attention value below the threshold value, the learner will be determined distraction.Third, a combination of the above two attention detection methods was proposed and the experiment results proved the usability of the proposed method.Finally, the experimental results show that the attention detection method based on computer vision is more suitable for detecting the explicit distractions. The detection method based on brain waves is more suitable for detecting the implicit distraction. In addition, the combination of these two methods will provide the best detection rate in attention detection.