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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.
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References
Grossberg, S.: The link between brain learning, attention, and consciousness. Consciousness and Cognition 8(1), 1–44 (1999)
Pantic, M., Rothkrantz, L.J.: Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1424–1445 (2000)
Cowie, R., Cornelius, R.R.: Describing the emotional states that are expressed in speech. Speech Communication 40(1-2), 5–32 (2003)
Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: Analysis of affective physiological state. Pattern Analysis and Machine Intelligence (2001)
Picard, R.W.: Toward agents that recognize emotion. In: Proceedings IMAGINA (1998)
Heraz, A., Razaki, R., Frasson, C.: Using machine learning to predict learner emotional state from brainwaves. In: Proceedings ICALT (2008)
Picard, R.W., Scheirer, J.: The galvactivator: A glove that senses and communicates skin conductivity. In: 9th International Conference on Human-Computer Interaction, New Orleans, August 2001, pp. 1538–1542 (2001)
Chen, H., Murray, A.: Continuous restricted boltzmann machine with an implementable training algorithm. IEE Proceedings-Vision Image and Signal Processing 150(3), 153–158 (2003)
Lu, C., Zhou, J., Shen, L., Shen, R.: Techniques for enhancing pervasive learning in standard natural classroom. In: Hybrid Learning and Education - First International Conference, pp. 202–212 (2008)
Haag, A., Goronzy, S., Schaich, P., Williams, J.: Emotion recognition using bio-sensors: First steps towards an automatic system. Affective Dialogue Systems, 36–48 (2004)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001),http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hinton, G.E., Salakhudinov, R.R.: Reducing the dimensionality of data with neural networks. Science (2006)
Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory, 194–281 (1986)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)
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Authors and Affiliations
Computer Science & Engineering Dept., Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
Jiaji Zhou, Heng Luo, Quanfeng Luo & Liping Shen
- Jiaji Zhou
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- Heng Luo
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- Quanfeng Luo
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- Liping Shen
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Editors and Affiliations
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China
Fu Lee Wang
Department of Computer Science, City University of Hong Kong, Hong Kong
Joseph Fong
Faculty of Education, University of Macau, Av. Padre Tomas Pereira, Taipa, Macau, China
Liming Zhang
School of Continuing and Professional Studies, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Victor S. K. Lee
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© 2009 Springer-Verlag Berlin Heidelberg
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Zhou, J., Luo, H., Luo, Q., Shen, L. (2009). Attentiveness Detection Using Continuous Restricted Boltzmann Machine in E-Learning Environment. In: Wang, F.L., Fong, J., Zhang, L., Lee, V.S.K. (eds) Hybrid Learning and Education. ICHL 2009. Lecture Notes in Computer Science, vol 5685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03697-2_3
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