{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:55:26Z","timestamp":1773248126017,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,19]],"date-time":"2018-05-19T00:00:00Z","timestamp":1526688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["UGC\/FDS13\/E01\/17"],"award-info":[{"award-number":["UGC\/FDS13\/E01\/17"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient user experience. In this paper, we constructed a complete prototype of the mobile gaze-tracking system \u2018Etracker\u2019 with a near-eye viewing device for human gaze tracking. We proposed a combined gaze-tracking algorithm. In this algorithm, the convolutional neural network is used to remove blinking images and predict coarse gaze position, and then a geometric model is defined for accurate human gaze tracking. Moreover, we proposed using the mean value of gazes to resolve pupil center changes caused by nystagmus in calibration algorithms, so that an individual user only needs to calibrate it the first time, which makes our system more convenient. The experiments on gaze data from 26 participants show that the eye center detection accuracy is 98% and Etracker can provide an average gaze accuracy of 0.53\u00b0 at a rate of 30\u201360 Hz.<\/jats:p>","DOI":"10.3390\/s18051626","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T04:07:30Z","timestamp":1526875650000},"page":"1626","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Etracker: A Mobile Gaze-Tracking System with Near-Eye Display Based on a Combined Gaze-Tracking Algorithm"],"prefix":"10.3390","volume":"18","author":[{"given":"Bin","family":"Li","sequence":"first","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics of CAS, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Department of Computer Science, Chu Hai College of Higher Education, Tuen Mun, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chu Hai College of Higher Education, Tuen Mun, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Desheng","family":"Wen","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics of CAS, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"WaiLun","family":"LO","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chu Hai College of Higher Education, Tuen Mun, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.3390\/s150202244","article-title":"Eye\/head tracking technology to improve HCI with iPad applications","volume":"15","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.4028\/www.scientific.net\/AMM.311.9","article-title":"Using eye-tracking and support vector machine to measure learning attention in elearning","volume":"311","author":"Liu","year":"2013","journal-title":"Appl. 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