{"title":"海报:文字默读过程中基于眼睛注视的注意力识别的初步研究","authors":"Saki Tanaka, Airi Tsuji, K. Fujinami","doi":"10.1145/3517031.3531632","DOIUrl":null,"url":null,"abstract":"We propose machine learning models to recognize state of non-concentration using eye-gaze data to increase the productivity. The experimental results show that Random Forest classifier with a 12 s window can divide the states with an F1-score more than 0.9.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Poster: A Preliminary Investigation on Eye Gaze-based Concentration Recognition during Silent Reading of Text\",\"authors\":\"Saki Tanaka, Airi Tsuji, K. Fujinami\",\"doi\":\"10.1145/3517031.3531632\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose machine learning models to recognize state of non-concentration using eye-gaze data to increase the productivity. The experimental results show that Random Forest classifier with a 12 s window can divide the states with an F1-score more than 0.9.\",\"PeriodicalId\":339393,\"journal\":{\"name\":\"2022 Symposium on Eye Tracking Research and Applications\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Symposium on Eye Tracking Research and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3517031.3531632\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Symposium on Eye Tracking Research and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3517031.3531632","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster: A Preliminary Investigation on Eye Gaze-based Concentration Recognition during Silent Reading of Text
We propose machine learning models to recognize state of non-concentration using eye-gaze data to increase the productivity. The experimental results show that Random Forest classifier with a 12 s window can divide the states with an F1-score more than 0.9.
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