K. Kunze, Yuzuko Utsumi, Yuki Shiga, K. Kise, A. Bulling
{"title":"I know what you are reading: recognition of document types using mobile eye tracking","authors":"K. Kunze, Yuzuko Utsumi, Yuki Shiga, K. Kise, A. Bulling","doi":"10.1145/2493988.2494354","DOIUrl":null,"url":null,"abstract":"Reading is a ubiquitous activity that many people even perform in transit, such as while on the bus or while walking. Tracking reading enables us to gain more insights about expertise level and potential knowledge of users -- towards a reading log tracking and improve knowledge acquisition. As a first step towards this vision, in this work we investigate whether different document types can be automatically detected from visual behaviour recorded using a mobile eye tracker. We present an initial recognition approach that com- bines special purpose eye movement features as well as machine learning for document type detection. We evaluate our approach in a user study with eight participants and five Japanese document types and achieve a recognition performance of 74% using user-independent training.","PeriodicalId":90988,"journal":{"name":"The semantic Web--ISWC ... : ... International Semantic Web Conference ... proceedings. International Semantic Web Conference","volume":"30 1","pages":"113-116"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"81","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The semantic Web--ISWC ... : ... International Semantic Web Conference ... proceedings. International Semantic Web Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2493988.2494354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 81
Abstract
Reading is a ubiquitous activity that many people even perform in transit, such as while on the bus or while walking. Tracking reading enables us to gain more insights about expertise level and potential knowledge of users -- towards a reading log tracking and improve knowledge acquisition. As a first step towards this vision, in this work we investigate whether different document types can be automatically detected from visual behaviour recorded using a mobile eye tracker. We present an initial recognition approach that com- bines special purpose eye movement features as well as machine learning for document type detection. We evaluate our approach in a user study with eight participants and five Japanese document types and achieve a recognition performance of 74% using user-independent training.