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- W4287883670 abstract "At present, the eye movement research of reading has accumulated rich research results and formed a more representative eye movement model, which mainly depends on the eye movement instrument to track the eye movement, but there is no popular software and hardware equipment for the detection of dyslexia in the market. This paper designs and implements a dyslexia detection model. The model detects the pupil movement of children through computer vision, tracks the eye movement trajectory, obtains the eye movement coordinates and interval time, establishes two indicators of reading speed and eye jump amplitude, analyzes the eye movement indicators using TOPSIS entropy weight method, and scientifically and reasonably distributes the weight comprehensive score. So as to identify readers with dyslexia and give scientific reading training suggestions and medical advice. It realizes the early screening of dyslexia patients. After practical test, our model achieves an accuracy of 89.7%. Therefore, our results lay a foundation for automatic detection of dyslexia in natural reading." @default.
- W4287883670 created "2022-07-26" @default.
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- W4287883670 date "2022-05-27" @default.
- W4287883670 modified "2023-10-16" @default.
- W4287883670 title "Research on Dyslexia Detection based on Eye Tracking" @default.
- W4287883670 doi "https://doi.org/10.1109/icetci55101.2022.9832243" @default.
- W4287883670 hasPublicationYear "2022" @default.
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