Attentiveness Detection Using Continuous Restricted Boltzmann Machine in E-Learning Environment

Abstract

Attentiveness is one of the key factors in human intelligent behavior. Especially, we are interested in the attentiveness states of learners. In recent years, lots of methods were proposed for attentiveness assessment, including computer vision, speech recognition, physiology and other approaches, and some of them already shown exciting results. We believe that physiological approach is very suitable to detect learners’ attentiveness. However, till now the performance of these methods were measured on the single testee in their experiments, which means their conclusions may not be generally valid. Although it is reasonable to restrict test subjects in early stage of research, generalized experiments involving multiple subjects are much more important to study. In this paper, we conducted a series of experiments that collected physiological data from 20 different subjects. Based on the experimental data, we revealed the huge individual differences of physiological features among those subjects. In order to smooth down such differences, we adopted continuous restricted Boltzmann machine to extract features from the original data. Finally we compared the method we used with other algorithms. The experimental result shows positive support towards generally applicable attentiveness detection by physiology approach.