关键词: 学习疲劳, 网络学习, Gabor小波, 隐马尔可夫模型

Abstract: Distance education network learners can easily feel tired in the learning process due to the long-term lack of emotional interaction, and the learning fatigue usually presents by the eye state. To monitor the remote intelligent teaching system effectively, a kind of recognition algorithm of eye state in learning fatigue state is put forward based on Gabor wavelets and HMM. Due to the different characters of the eye openness degree in normal study, fatigue and confusion, the three learning states, the algorithm conducts gray difference processing to the eye image using Laplacian in YCbCr color space. It selects the second-dimension Gabor kernel function, constructing 48 most optimal filters, for 48 characteristic values. The 48 characteristic values will generate 48 characteristic vectors, and later HMM will be used to recognize the eye state of the eye by the set of observation sequence O formed by the characteristic vectors of the eye state image. The experimental result shows that the network learning fatigue recognition rate of this algorithm reaches 95.68%, with good robustness.

Key words: learning fatigue, E-learning, Gabor wavelet, Hidden Markov Model（HMM）