Abstract
Human reading states reflect people’s mental activities and are closely related with learning behaviors. Consequently, it is of great value to identify reading states in many aspects. For example, it can provide timely assistance in learning processes and boost the learning efficiency of learners in a personalized way. Conventionally, researchers usually make use of EEG and fMRI to recognize human reading states. However, these methods have shortcomings of high device cost, low portability and bad user experience. In this paper, we design a real-time reading states detection system named ETist with commodity wearable glasses that can identify four reading states including attention, browsing, mind wandering and drowsiness via tracking eye movement. Through our experiments, we demonstrate that this system can recognize four fine-grained states with an average accuracy of 84.0%, which is applicable in a wide area of applications.
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Acknowledgment
This research was supported in part by the China NSFC Grant (61802264, 61872248), Guangdong NSF 2017A030312008, Shenzhen Science and Technology Foundation (No. JCYJ20180305124807337, No. ZDSYS20190902092853047), Guangdong Science and Technology Foundation (2019B111103001, 2019B020209001), GDUPS (2015), and Natural Science Foundation of SZU (No. 860-000002110537).
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Yuan, B., Han, Y., Dai, J., Zou, Y., Liu, Y., Wu, K. (2020). I am Smartglasses, and I Can Assist Your Reading. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_26
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DOI: https://doi.org/10.1007/978-3-030-60239-0_26
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