{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:26:12Z","timestamp":1781216772475,"version":"3.54.1"},"reference-count":18,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.<\/jats:p>","DOI":"10.3390\/a15030084","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T09:24:53Z","timestamp":1646299493000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuqi","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abdulin, E., and Komogortsev, O. (2015, January 18\u201323). User eye fatigue detection via eye movement behavior. Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, Seoul, Korea.","DOI":"10.1145\/2702613.2732812"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.4103\/2320-3897.122661","article-title":"Computer vision syndrome: A review","volume":"2","author":"Bali","year":"2014","journal-title":"J. Clin. Ophthalmol. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1002\/jsid.991","article-title":"Subjective assessment on visual fatigue versus stereoscopic disparities","volume":"29","author":"Liu","year":"2021","journal-title":"J. Soc. Inf. Disp."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1177\/0018720811411297","article-title":"Evaluation of eye metrics as a detector of fatigue","volume":"53","author":"McKinley","year":"2011","journal-title":"Hum. 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