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Detail of Publication

Text Language English
Authors Md. Rabiul Islam, Shuji Sakamoto, Yoshihiro Yamada, Andrew W. Vargo, Motoi Iwata, Masakazu Iwamura, and Koichi Kise
Title Self-supervised Learning for Reading Activity Classification
Journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (ACM IMWUT journal)
Vol. 5
No. 3
Presentation number 105
Pages pp.1-22
Publisher ACM
Reviewed or not Reviewed
Month & Year September 2021
Abstract Reading analysis can relay information about user's confidence and habits and can be used to construct useful feedback. A lack of labeled data inhibits the effective application of fully-supervised Deep Learning (DL) for automatic reading analysis. We propose a Self-supervised Learning (SSL) method for reading analysis. Previously, SSL has been effective in physical human activity recognition (HAR) tasks, but it has not been applied to cognitive HAR tasks like reading. We first evaluate the proposed method on a four-class classification task on reading detection using electrooculography datasets, followed by an evaluation of a two-class classification task of confidence estimation on multiple-choice questions using eye-tracking datasets. Fully-supervised DL and support vector machines (SVMs) are used as comparisons for the proposed SSL method. The results show that the proposed SSL method is superior to the fully-supervised DL and SVM for both tasks, especially when training data is scarce. This result indicates the proposed method is the superior choice for reading analysis tasks. These results are important for informing the design of automatic reading analysis platforms.
DOI 10.1145/3478088
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