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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="review-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Human Physiology</journal-id><journal-title-group><journal-title xml:lang="en">Human Physiology</journal-title><trans-title-group xml:lang="ru"><trans-title>Физиология человека</trans-title></trans-title-group></journal-title-group><issn publication-format="print">0131-1646</issn><issn publication-format="electronic">3034-6150</issn><publisher><publisher-name xml:lang="en">The Russian Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">664026</article-id><article-id pub-id-type="doi">10.31857/S0131164624030074</article-id><article-id pub-id-type="edn">BULTQU</article-id><article-categories><subj-group subj-group-type="toc-heading"><subject>ОБЗОРЫ</subject></subj-group><subj-group subj-group-type="article-type"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Related work analysis for determination of fatigue state based on eye movements monitoring</article-title><trans-title-group xml:lang="ru"><trans-title>Анализ исследований определения утомления на основе мониторинга окуломоторных событий</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Shoshina</surname><given-names>I. I.</given-names></name><name xml:lang="ru"><surname>Шошина</surname><given-names>И. И.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio><p>Институт когнитивных исследований</p></bio><email>shoshinaii@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Kovalenko</surname><given-names>S. D.</given-names></name><name xml:lang="ru"><surname>Коваленко</surname><given-names>С. Д.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>shoshinaii@mail.ru</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Kuznetsov</surname><given-names>V. V.</given-names></name><name xml:lang="ru"><surname>Кузнецов</surname><given-names>В. В.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>shoshinaii@mail.ru</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Brak</surname><given-names>I. V.</given-names></name><name xml:lang="ru"><surname>Брак</surname><given-names>И. В.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>shoshinaii@mail.ru</email><xref ref-type="aff" rid="aff4"/><xref ref-type="aff" rid="aff5"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Kashevnik</surname><given-names>A. M.</given-names></name><name xml:lang="ru"><surname>Кашевник</surname><given-names>А. М.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>shoshinaii@mail.ru</email><xref ref-type="aff" rid="aff6"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Saint-Petersburg State University</institution></aff><aff><institution xml:lang="ru">Санкт-Петербургский государственный университет</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">National Research University Higher School of Economics</institution></aff><aff><institution xml:lang="ru">Национальный исследовательский университет "Высшая школа экономики"</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Federal Research Center “Computer Science and Control”, RAS</institution></aff><aff><institution xml:lang="ru">Федеральный исследовательский центр "Информатика и управление" РАН</institution></aff></aff-alternatives><aff-alternatives id="aff4"><aff><institution xml:lang="en">Novosibirsk State University</institution></aff><aff><institution xml:lang="ru">Новосибирский государственный университет</institution></aff></aff-alternatives><aff-alternatives id="aff5"><aff><institution xml:lang="en">Privolzhsky Research Medical University, PRMU</institution></aff><aff><institution xml:lang="ru">Приволжский исследовательский медицинский университет Минздрава России</institution></aff></aff-alternatives><aff-alternatives id="aff6"><aff><institution xml:lang="en">Saint-Petersburg Federal Research Center, RAS</institution></aff><aff><institution xml:lang="ru">Санкт-Петербургский Федеральный исследовательский центр РАН</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-09-16" publication-format="electronic"><day>16</day><month>09</month><year>2024</year></pub-date><volume>50</volume><issue>3</issue><fpage>81</fpage><lpage>101</lpage><history><date date-type="received" iso-8601-date="2025-02-25"><day>25</day><month>02</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Russian Academy of Sciences</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Российская академия наук</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Russian Academy of Sciences</copyright-holder><copyright-holder xml:lang="ru">Российская академия наук</copyright-holder></permissions><self-uri xlink:href="https://journals.eco-vector.com/0131-1646/article/view/664026">https://journals.eco-vector.com/0131-1646/article/view/664026</self-uri><abstract xml:lang="en"><p>We have reviewed theoretical background of detecting functional state of fatigue based on the strategy of eye movements. Also, modern methods for assessing eye movements were considered. Based on our literature review, we can conclude that nowadays there are multitude numerical characteristics of eye movements, the dynamics of which can hypothetically make it possible to assess degree of fatigue. However, there are still no proposals for a method for determining the degree of fatigue based on an analysis of the strategy of eye movements. In this regard, according to the concepts of static and dynamic vision, it is proposed to consider the shift in the numerical characteristics of eye movements towards characteristics that reflect the strategy of dynamic vision as evidence of fatigue.</p></abstract><trans-abstract xml:lang="ru"><p>Рассмотрены теоретические предпосылки определения функционального состояния утомления на основе анализа стратегии глазных движений, современные методы оценки движений глаз. Анализ литературы позволяет сделать вывод о том, что в настоящее время существует огромное количество численных характеристик движений глаз, динамика которых гипотетически может позволить судить о степени утомления человека. Однако, пока отсутствуют предложения метода определения степени утомления на основе анализа стратегии глазных движений. В связи с этим, основываясь на представлениях о статическом и динамическом зрении, предложено рассматривать сдвиг численных характеристик движений глаз в сторону показателей, отражающих стратегию динамического зрения, как свидетельство утомления.</p></trans-abstract><kwd-group xml:lang="en"><kwd>fatigue</kwd><kwd>eye movements</kwd><kwd>eye movement strategies</kwd><kwd>oculomotor events</kwd><kwd>eye tracking</kwd><kwd>biometrics</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>утомление</kwd><kwd>глазные движения</kwd><kwd>стратегии движений глаз</kwd><kwd>окуломоторные события</kwd><kwd>айтрекинг</kwd><kwd>биометрия</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution xml:lang="ru">Фонд содействия инновациям</institution></institution-wrap><institution-wrap><institution xml:lang="en">Foundation for Assistance to Innovations</institution></institution-wrap></funding-source><award-id>ИИ-205491</award-id></award-group></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><citation-alternatives><mixed-citation xml:lang="en">Isakova M. Topic on VPP № 22 for military personnel serving under contract and conscription // Army Collection. 2021. V. 8. P. 126.</mixed-citation><mixed-citation xml:lang="ru">Исакова М. Тема по ВПП № 22 для военнослужащих, проходящих военную службу по контракту и призыву // Армейский Сборник. 2021. Т. 8. С. 126.</mixed-citation></citation-alternatives></ref><ref id="B2"><label>2.</label><citation-alternatives><mixed-citation xml:lang="en">Velichkovsky B.B. Cognitive effects of mental fatigue // Bulletin of Moscow University. Series 14. Psychology. 2019. № 1. P. 108.</mixed-citation><mixed-citation xml:lang="ru">Величковский Б.Б. Когнитивные эффекты умственного утомления // Вестник Московского университета. Серия 14. Психология. 2019. № 1. С. 108.</mixed-citation></citation-alternatives></ref><ref id="B3"><label>3.</label><mixed-citation>Borgianni Y., Rauch E., Maccioni L., Mark B.G. User experience analysis in industry 4.0 – The use of biometric devices in engineering design and manufacturing / IEEE International conference on Industrial Engineering and Engineering Management. (IEEM), Bangkok, Thailand, 16-19 December, 2018. IEEE Computer Society, 2019. P. 192.</mixed-citation></ref><ref id="B4"><label>4.</label><citation-alternatives><mixed-citation xml:lang="en">Velichkovsky B.M., Ushakov V.L. Cognitive sciences and new medical technologies // Modern Technologies in Medicine. 2019. V. 11. № 1. P. 8.</mixed-citation><mixed-citation xml:lang="ru">Величковский Б.М., Ушаков В.Л. Когнитивные науки и новые медицинские технологии // Современные технологии в медицине. 2019. Т. 11. № 1. С. 8.</mixed-citation></citation-alternatives></ref><ref id="B5"><label>5.</label><mixed-citation>Robertson C.V., Marino F.E. Cerebral responses to exercise and the influence of heat stress in human fatigue // J. Therm. Biol. 2017. V. 63. P. 10.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Bergasa L.M., Nuevo J., Sotelo M.-A. et al. Real-time system for monitoring driver vigilance // IEEE Trans. Intell. Transp. Syst. 2006. V. 7. № 1. P. 63.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>D’Orazio T., Leo M., Guaragnella C., Distante A. A visual approach for driver inattention detection // Patt. Recog. 2007. V. 40. № 8. P. 2341.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Al-Anizy G.J., Nordin M.J., Razooq M.M. Automatic driver drowsiness detection using haar algorithm and support vector machine techniques // Asian J. Appl. Sci. 2015. V. 8. № 2. P. 149.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Golz M., Sommer D., Chen M. et al. Feature fusion for the detection of microsleep events // J. VLSI Sign. Process Syst. Sign. Im. 2007. V. 49. № 2. P. 329.</mixed-citation></ref><ref id="B10"><label>10.</label><citation-alternatives><mixed-citation xml:lang="en">Liu Z., Peng Y., Hu W. Driver fatigue detection based on deeply-learned facial expression representation // J. Vis. Commun. Image Represent. 2020. V. 71. № 2. 102723.</mixed-citation><mixed-citation xml:lang="ru">Liu Z., Peng Y., Hu W. Driver fatigue detection based on deeply-learned facial expression representation // J. Vis. Commun. Image Represent. 2020. V. 71. № 2. P. 102723.</mixed-citation></citation-alternatives></ref><ref id="B11"><label>11.</label><mixed-citation>Mandal B., Li L., Wang G.S., Lin J. Towards detection of bus driver fatigue based on robust visual analysis of eye state // IEEE Trans. Intell. Transp. Syst. 2016. V. 18. № 3. P. 545.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Sigari M.H., Fathy M., Soryani M. A driver face monitoring system for fatigue and distraction detection // Int. J. Vehicul. Technol. 2013. V. 2013. P. 1.</mixed-citation></ref><ref id="B13"><label>13.</label><citation-alternatives><mixed-citation xml:lang="en">Lyapunov S.I., Shoshina I.I., Lyapunov I.S. Tremor eye movements as an objective marker of driver’s fatigue // Human Physiology. 2022. V. 48. № 1. P. 71.</mixed-citation><mixed-citation xml:lang="ru">Ляпунов С.И., Шошина И.И., Ляпунов И.С. Треморные колебания глаз как объективный показатель утомления водителей // Физиология человека. 2022. Т. 48. № 1. С. 89.</mixed-citation></citation-alternatives></ref><ref id="B14"><label>14.</label><mixed-citation>Golz M., Sommer D., Trutschel U. et al. Evaluation of fatigue monitoring technologies // Somnologie. 2010. V. 14. № 3. P. 187.</mixed-citation></ref><ref id="B15"><label>15.</label><citation-alternatives><mixed-citation xml:lang="en">Kubarko A.I., Likhachev S.A., Kubarko N.P. [Vision (neurophysiological and neuro-ophthalmological aspects): monograph in 2 volumes. V. 2: Neural mechanisms for controlling the installation and movement of the eyes and their disorders in diseases of the nervous system]. Minsk: BSMU, 2009. 352 p.</mixed-citation><mixed-citation xml:lang="ru">Кубарко А.И., Лихачев С.А., Кубарко Н.П. Зрение (нейрофизиологические и нейроофтальмологические аспекты): монография в 2 т. Т. 2: Нейронные механизмы контроля установки и движения глаз и их нарушения при заболеваниях нервной системы. Минск: БГМУ, 2009. 352 с.</mixed-citation></citation-alternatives></ref><ref id="B16"><label>16.</label><citation-alternatives><mixed-citation xml:lang="en">Barabanshchikov V.A., Zhegallo A.V. [Eye-tracking: methods of recording eye movements in psychological research and practice]. M.: Kogito-center, 2014. P. 117.</mixed-citation><mixed-citation xml:lang="ru">Барабанщиков В.А., Жегалло А.В. Айтрекинг: методы регистрации движений глаз в психологических исследованиях и практике. М.: Когито-центр, 2014. С. 117.</mixed-citation></citation-alternatives></ref><ref id="B17"><label>17.</label><citation-alternatives><mixed-citation xml:lang="en">Yarbus A. [The role of eye movements in the process of vision]. M.: Nauka, 1965. 161 p.</mixed-citation><mixed-citation xml:lang="ru">Ярбус А. Роль движений глаз в процессе зрения. М.: Наука, 1965. 161 с.</mixed-citation></citation-alternatives></ref><ref id="B18"><label>18.</label><mixed-citation>Holmqvist K. Eye tracking: A comprehensive guide to methods and measures. O.: OUP Oxford, 2011. 560 p.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Djanian S. Eye movement classification using deep learning. Aalborg University: Department of Electronic Systems, 2019. 76 p.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Mahanama B., Jayawardana Y., Rengarajan S. et al. Eye movement and pupil measures: A review // Front. Comput. Sci. 2022. V. 3. P. 733531.</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Li X., Fan Z., Ren Y. et al. Classification of eye movement and its application in driving based on a refined pre-processing and machine learning algorithm // IEEE Access. 2021. V. 9. P. 136164.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Salvucci D.D., Goldberg J.H. Identifying fixations and saccades in eye-tracking protocols / Proceedings of the 2000 symposium on Eye Tracking Research and Applications (ETRA ‘00). Palm Beach Gardens, FL, USA. November 6–8, 2000. Association for Computing Machinery (ACM). New York, NY, USA, 2000. P. 71.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Wang S., Wang Q., Chen H. Research and application of eye movement interaction based on eye movement recognition / MATEC Web Conf. 2018. V. 246. P. 5.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Carpenter R.H.S. Movements of the eyes: Part 1. Movements of the eyes. 2nd ed. Pion, 1988. P. 593.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Shojaeizadeh M., Djamasbi S., Trapp A.C. Density of gaze points within a fixation and information processing behavior / Universal Access in Human-Computer Interaction. Methods, Techniques, and Best Practices. UAHCI 2016. Lect. Notes Comput. // Eds. Antona M., Stephanidis C. Sci. Springer, Cham, 2016. V. 9737. P. 465.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Skaramagkas V., Giannakakis G., Ktistakis E. et al. Review of eye tracking metrics involved in emotional and cognitive processes // IEEE Rev. Biomed. Eng. 2023. V. 16. P. 260.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Foy H.J., Chapman P. Mental workload is reflected in driver behavior, physiology, eye movements and prefrontal cortex activation // Appl. Ergon. 2018. V. 73. P. 90.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Srimal R., Diedrichsen J., Ryklin E.B., Curtis C.E. Obligatory adaptation of saccade gains // J. Neurophysiol. 2008. V. 99. № 3. P. 1554.</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Russo M., Thomas M., Thorne D. et al. Oculomotor impairment during chronic partial sleep deprivation // Clin. Neurophysiol. 2003. V. 114. № 4. P. 723.</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Warren D.E., Thurtell M.J., Carroll J.N., Wall M. Perimetric evaluation of saccadic latency, saccadic accuracy, and visual threshold for peripheral visual stimuli in young compared with older adults // Invest. Ophthalmol. Vis. Sci. 2013. V. 54. № 8. P. 5778.</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Yang Y., McDonald M., Zheng P. Can drivers’ eye movements be used to monitor their performance? A case study // IET Intell. Transp. Syst. 2012. V. 6. № 4. P. 444.</mixed-citation></ref><ref id="B32"><label>32.</label><citation-alternatives><mixed-citation xml:lang="en">Nakayama M., Takahashi K., Shimizu Y. The act of task difficulty and eye-movement frequency for the “oculo-motor indices” / Proceedings of the 2002 symposium on Eye tracking research &amp; applications (ETRA ‘02). New Orleans Louisiana, March 25-27, 2002. Association for Computing Machinery (ACM), New York, NY, USA, 2002. P. 37.</mixed-citation><mixed-citation xml:lang="ru">Nakayama M., Takahashi K., Shimizu Y. The act of task difficulty and eye-movement frequency for the “oculo-motor indices” / Proceedings of the 2002 symposium on Eye tracking research &amp; applications (ETRA ‘02). New Orleans Louisiana, March 25-27, 2002. Association for Computing Machinery (ACM). New York, NY, USA. 2002. P. 37.</mixed-citation></citation-alternatives></ref><ref id="B33"><label>33.</label><mixed-citation>Van Orden K.F., Limbert W., Makeig S., Jung T.P. Eye activity correlates of workload during a visuospatial memory task // Hum. Factors. 2001. V. 43. № 1. P. 111.</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Amor T.A., Reis S.D., Campos D. et al. Persistence in eye movement during visual search // Sci. Rep. 2016. V. 6. P. 20815.</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Joseph A.W., Murugesh R. Potential eye tracking metrics and indicators to measure cognitive load in human-computer interaction research // J. Sci. Res. 2020. V. 64. № 01. P. 168.</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Chen S., Epps J., Ruiz N., Chen F. Eye activity as a measure of human mental effort in HCI / Proceedings of the 16th international conference on Intelligent user interfaces (IUI’11). Association for Computing Machinery (ACM), February 13–16, 2011. New York, NY, USA, 2011. P. 315.</mixed-citation></ref><ref id="B37"><label>37.</label><mixed-citation>Zagermann J., Pfeil U., Reiterer H. Measuring cognitive load using eye tracking technology in visual computing / Proceedings of the sixth workshop on Beyond Time and Errors on novel evaluation methods for visualization, BELIV ‘16. Baltimore MD USA, 24 October 2016. New York, NY: ACM Press, 2016. V. 24. P. 78.</mixed-citation></ref><ref id="B38"><label>38.</label><mixed-citation>Fahimi R., Bruce N.D.B. On metrics for measuring scan path similarity // Behav. Res. Methods. 2021. V. 53. № 2. P. 609.</mixed-citation></ref><ref id="B39"><label>39.</label><mixed-citation>Holland C., Komogortsev O.V. Biometric identification via eye movement scanpaths in reading / International Joint Conference on Biometrics. 11 October 2011. P. 1. https://api.semanticscholar.org/CorpusID:2528223.</mixed-citation></ref><ref id="B40"><label>40.</label><mixed-citation>Goldberg J.H., Kotval X.P. Computer interface evaluation using eye movements: Methods and constructs // Int. J. Ind. Ergon. 1999. V. 24. № 6. P. 631.</mixed-citation></ref><ref id="B41"><label>41.</label><mixed-citation>Yamada, Y., Kobayashi, M. Detecting mental fatigue from eye-tracking data gathered while watching video: Evaluation in younger and older adults // Artif. Intell. Med. 2018. V. 91. P. 39.</mixed-citation></ref><ref id="B42"><label>42.</label><mixed-citation>Kliegl R., Rolfs M., Laubrock J., Engbert R. Microsaccadic modulation of response times in spatial attention tasks // Psychol. Res. 2009. V. 73. № 2. P. 136.</mixed-citation></ref><ref id="B43"><label>43.</label><mixed-citation>Krejtz K., Duchowski A.T., Niedzielska A. et al. Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze // PLoS One. 2018. V. 13. № 9. P. e0203629.</mixed-citation></ref><ref id="B44"><label>44.</label><mixed-citation>Zandi A.S., Quddus A., Prest L., Comeau F.J. Non-Intrusive detection of drowsy driving based on eye tracking data // Transp. Res. Rec. 2019. V. 2673. № 6. P. 247.</mixed-citation></ref><ref id="B45"><label>45.</label><mixed-citation>Zemblys R., Niehorster D.C., Komogortsev O., Holmqvist K. Using machine learning to detect events in eye-tracking data // Behav. Res. Methods. 2018. V. 50. № 1. P. 160.</mixed-citation></ref><ref id="B46"><label>46.</label><mixed-citation>Chen J.T., Kuo Y.C., Hsu T.Y., Wang C.A. Fatigue and arousal modulations revealed by saccade and pupil dynamics // Int. J. Environ Res. Public Health. MDPI. 2022. V. 19. № 15. P. 9234.</mixed-citation></ref><ref id="B47"><label>47.</label><mixed-citation>Brezinova V., Kendell R.E. Smooth pursuit eye movements of schizophrenics and normal people under stress // Br. J. Psychiatry. 1977. V. 130. P. 59.</mixed-citation></ref><ref id="B48"><label>48.</label><mixed-citation>Rottach K.G., Zivotofsky A.Z., Das V.E. et al. Comparison of horizontal, vertical and diagonal smooth pursuit eye movements in normal human subjects // Vision Res. 1996. V. 36. № 14. P. 2189.</mixed-citation></ref><ref id="B49"><label>49.</label><mixed-citation>Ranti C., Jones W., Klin A., Shultz S. Blink rate patterns provide a reliable measure of individual engagement with scene content // Sci. Rep. 2020. V. 10. № 1. P. 8267.</mixed-citation></ref><ref id="B50"><label>50.</label><mixed-citation>Marquart G., Cabrall C., de Winter J. Review of eye-related measures of drivers’ mental workload // Procedia Manuf. 2015. V. 3. P. 2854.</mixed-citation></ref><ref id="B51"><label>51.</label><citation-alternatives><mixed-citation xml:lang="en">Haq Z.A., Hasan Z. Eye-blink rate detection for fatigue determination / India International Conference on Information Processing (IICIP 2016), Delhi, India, 12-14 August, 2016. Proceedings IEEE Inc., 2017. P. 1.</mixed-citation><mixed-citation xml:lang="ru">Haq Z.A., Hasan Z. Eye-blink rate detection for fatigue determination / India International Conference on Information Processing (IICIP 2016), Delhi, India, 12–14 August, 2016. Proceedings IEEE Inc., 2017. P. 1.</mixed-citation></citation-alternatives></ref><ref id="B52"><label>52.</label><mixed-citation>Horiuchi R., Ogasawara T., Miki N. Fatigue assessment by blink detected with attachable optical sensors of dye-sensitized photovoltaic cells // Micromachines (Basel). 2018. V. 9. № 6. P. 310.</mixed-citation></ref><ref id="B53"><label>53.</label><mixed-citation>Tolvanen O., Elomaa A. P., Itkonen M. et al. Eye-tracking indicators of workload in surgery: A systematic review // J. Invest. Surg. 2022. V. 35. № 6. P. 1340.</mixed-citation></ref><ref id="B54"><label>54.</label><citation-alternatives><mixed-citation xml:lang="en">Marshall S.P. The index of cognitive activity: Measuring cognitive workload / Proceedings of the IEEE 7th Conference on Human Factors and Power Plants. 19 September, Scottsdale, AZ, USA, 2002. P. 7. doi: 10.1109/HFPP.2002.1042860.</mixed-citation><mixed-citation xml:lang="ru">Marshall S.P. The index of cognitive activity: Measuring cognitive workload / Proceedings of the IEEE 7th Conference on Human Factors and Power Plants. 19 September, Scottsdale, AZ, USA, 2002. P. 7. doi: 10.1109/HFPP.2002.1042860</mixed-citation></citation-alternatives></ref><ref id="B55"><label>55.</label><mixed-citation>Duchowski A.T., Krejtz K., Krejtz I. et al. The index of pupillary activity: Measuring cognitive load vis-à-vis task difficulty with pupil oscillation / Proceedings of the Conference on Human Factors in Computing Systems (CHI ‘18). Association for Computing Machinery, New York, NY, USA, 2018. V. 282. P. 1. https://doi.org/10.1145/3173574.3173856</mixed-citation></ref><ref id="B56"><label>56.</label><mixed-citation>Duchowski A.T., Krejtz K., Gehrer N.A. et al. The low/high index of pupillary activity / Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ‘20). Association for Computing Machinery, New York, NY, USA, 2020. P. 1. https://doi.org/10.1145/3313831.3376394</mixed-citation></ref><ref id="B57"><label>57.</label><citation-alternatives><mixed-citation xml:lang="en">Alnajar F., Gevers T., Valenti R., Ghebreab S. Calibration-free gaze estimation using human gaze patterns / Proceedings of the IEEE International Conference on Computer Vision. 1–8 Dec. 2013, Sydney, NSW, Australia, 2013. P. 137. doi: 10.1109/ICCV.2013.24.</mixed-citation><mixed-citation xml:lang="ru">Alnajar F., Gevers T., Valenti R., Ghebreab S. Calibration-free gaze estimation using human gaze patterns / Proceedings of the IEEE International Conference on Computer Vision. 1-8 Dec. 2013, Sydney, NSW, Australia, 2013. P. 137. doi: 10.1109/ICCV.2013.24</mixed-citation></citation-alternatives></ref><ref id="B58"><label>58.</label><mixed-citation>Rigas I., Economou G., Fotopoulos S. Biometric identification based on the eye movements and graph matching techniques // Patt. Recogn. Lett. 2012. V. 33. № 6. P. 786.</mixed-citation></ref><ref id="B59"><label>59.</label><mixed-citation>Li J., Li H., Umer W. et al. Identification and classification of construction equipment operators’ mental fatigue using wearable eye-tracking technology // Autom. Constr. 2020. V. 109. P. 103000.</mixed-citation></ref><ref id="B60"><label>60.</label><mixed-citation>Bitkina O.V., Park J., Kim H.K. The ability of eye-tracking metrics to classify and predict the perceived driving workload // Int. J. Ind. Ergonom. 2021. V. 86. P. 103193.</mixed-citation></ref><ref id="B61"><label>61.</label><mixed-citation>Shiferaw B., Downey L., Crewther D. A review of gaze entropy as a measure of visual scanning efficiency // Neurosci. Biobehav. Rev. 2019. V. 96. P. 353.</mixed-citation></ref><ref id="B62"><label>62.</label><mixed-citation>Deravi F., Biosignals S.G. Gaze trajectory as a biometric modality / Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011). 2011. P. 335. doi: 10.5220/0003275803350341</mixed-citation></ref><ref id="B63"><label>63.</label><mixed-citation>Peißl S., Wickens C.D., Baruah R. Eye-Tracking measures in aviation: A selective literature review // Int. J. Aerosp. Psychol. 2018. V. 28. № 3–4. P. 98.</mixed-citation></ref><ref id="B64"><label>64.</label><mixed-citation>Meghanathan R.N., Nikolaev A.R., van Leeuwen C. Refixation patterns reveal memory-encoding strategies in free viewing // Atten. Percept. Psychophys. 2019. V. 81. № 7. P. 2499.</mixed-citation></ref><ref id="B65"><label>65.</label><mixed-citation>Fukushima K., Fukushima J., Warabi T., Barnes G.R. Cognitive processes involved in smooth pursuit eye movements: behavioral evidence, neural substrate and clinical correlation // Front. Syst. Neurosci. 2013. V. 7. P. 4.</mixed-citation></ref><ref id="B66"><label>66.</label><mixed-citation>Knox P.C., Davidson J.H., Anderson D. Age-related changes in smooth pursuit initiation // Exp. Brain Res. 2005. V. 165. № 1. P. 1.</mixed-citation></ref><ref id="B67"><label>67.</label><mixed-citation>Einhäuser W. The pupil as marker of cognitive processes / Computational and Cognitive Neuroscience of Vision. Cognitive Science and Technolog // Ed. Zhao Q. Springer, Singapore, 2017. P. 141.</mixed-citation></ref><ref id="B68"><label>68.</label><mixed-citation>Richstone L., Schwartz M.J., Seideman C. et al. Eye metrics as an objective assessment of surgical skill // Ann. Surg. 2010. V. 252. № 1. P. 1772.</mixed-citation></ref><ref id="B69"><label>69.</label><mixed-citation>Zargari Marandi R., Madeleine P., Omland Ø. et al. Eye movement characteristics reflected fatigue development in both young and elderly individuals // Sci. Rep. 2018. V. 8. № 1. P. 13148.</mixed-citation></ref><ref id="B70"><label>70.</label><mixed-citation>Catalbas M.C., Cegovnik T., Sodnik J., Gulten A. Driver fatigue detection based on saccadic eye movements / 10th International Conference on Electrical and Electronics Engineering (ELECO). IEEE. 30 November – 2 December 2017. Bursa, Turkey, 2017. P. 913.</mixed-citation></ref><ref id="B71"><label>71.</label><mixed-citation>Hu X., Lodewijks G. Exploration of the effects of task-related fatigue on eye-motion features and its value in improving driver fatigue-related technology // Transp. Res. Part F Traffic Psychol. Behav. 2021. V. 80. P. 150.</mixed-citation></ref><ref id="B72"><label>72.</label><mixed-citation>Ahlstrom C., Nyström M., Holmqvist K. et al. Fit-for-duty test for estimation of drivers’ sleepiness level: Eye movements improve the sleep/wake predictor // Transp. Res. Part C Emerg. Technol. 2013. V. 26. P. 20.</mixed-citation></ref><ref id="B73"><label>73.</label><mixed-citation>Abe T., Mishima K., Kitamura S. et al. Tracking intermediate performance of vigilant attention using multiple eye metrics // Sleep. 2020. V. 43. № 3. Р. zsz219.</mixed-citation></ref><ref id="B74"><label>74.</label><mixed-citation>Di Stasi L.L., McCamy M.B., Macknik S.L. et al. Saccadic eye movement metrics reflect surgical residents’ fatigue // Ann. Surg. 2014. V. 259. № 4. P. 824.</mixed-citation></ref><ref id="B75"><label>75.</label><mixed-citation>Di Stasi L.L., Renner R., Catena A. et al. Towards a driver fatigue test based on the saccadic main sequence: A partial validation by subjective report data // Transp. Res. Part C Emerg. Technol. 2012. V. 21. № 1. P. 122.</mixed-citation></ref><ref id="B76"><label>76.</label><mixed-citation>Finke C., Pech L.M., Sömmer C. et al. Dynamics of saccade parameters in multiple sclerosis patients with fatigue // J. Neurol. 2012. V. 259. № 12. P. 2656.</mixed-citation></ref><ref id="B77"><label>77.</label><mixed-citation>Renata V., Li F., Lee C.H., Chen C.H. Investigation on the correlation between eye movement and reaction time under mental fatigue influence / Proceedings of the 17th International Conference on Cyberworlds (CW 2018), Singapore 3-5 Oct 2018. Institute of Electrical and Electronics Engineers Inc., 2018. P. 207.</mixed-citation></ref><ref id="B78"><label>78.</label><mixed-citation>Herlambang M.B., Taatgen N.A., Cnossen F. The role of motivation as a factor in mental fatigue // Hum. Factors. 2019. V. 61. № 7. P. 1171.</mixed-citation></ref><ref id="B79"><label>79.</label><mixed-citation>Stone L.S., Tyson T.L., Cravalho P.F. et al. Distinct pattern of oculomotor impairment associated with acute sleep loss and circadian misalignment // J. Physiol. 2019. V. 597. № 17. P. 4643.</mixed-citation></ref><ref id="B80"><label>80.</label><mixed-citation>Gergelyfi M., Jacob B., Olivier E., Zénon A. Dissociation between mental fatigue and motivational state during prolonged mental activity // Front. Behav. Neurosci. 2015. V. 9. P. 176.</mixed-citation></ref><ref id="B81"><label>81.</label><citation-alternatives><mixed-citation xml:lang="en">Schweitzer T., Wyss T., Gilgen-Ammann R. Detecting soldiers’ fatigue using eye-tracking glasses: Practical field applications and research opportunities // Mil. Med. 2022. V. 187. № 11-12. P. e1330.</mixed-citation><mixed-citation xml:lang="ru">Schweitzer T., Wyss T., Gilgen-Ammann R. Detecting soldiers’ fatigue using eye-tracking glasses: Practical field applications and research opportunities // Mil. Med. 2022. V. 187. № 11–12. P. e1330.</mixed-citation></citation-alternatives></ref><ref id="B82"><label>82.</label><mixed-citation>Borghini G., Astolfi L., Vecchiato G. et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness // Neurosci. Biobehav. Rev. 2014. V. 44. P. 58.</mixed-citation></ref><ref id="B83"><label>83.</label><mixed-citation>Dziuda Ł., Baran P., Zieliński P. et al. Evaluation of a fatigue detector using eye closure-associated indicators acquired from truck drivers in a simulator study // Sensors. 2021. V. 21. № 19. P. 6449.</mixed-citation></ref><ref id="B84"><label>84.</label><mixed-citation>Schleicher R., Galley N., Briest S., Galley L. Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? // Ergonomics. 2008. V. 51. № 7. P. 982.</mixed-citation></ref><ref id="B85"><label>85.</label><mixed-citation>Hopstaken J.F., van der Linden D., Bakker A.B. et al. Shifts in attention during mental fatigue: Evidence from subjective, behavioral, physiological, and eye-tracking data // J. Exp. Psychol. Hum. Percept. Perform. 2016. V. 42. № 6. P. 878.</mixed-citation></ref><ref id="B86"><label>86.</label><mixed-citation>Pomplun M., Sunkara S. Pupil dilation as an indicator of cognitive workload in human-computer interaction / Human-Centered Computing. Cognitive, Social, and Ergonomic Aspects // Eds. Harris D., Duffy V., Smith M., Stephanidis C. Boca Raton: CRC Press, 2019. V. 3. P. 542.</mixed-citation></ref><ref id="B87"><label>87.</label><mixed-citation>Morad Y., Barkana Y., Zadok D. et al. Ocular parameters as an objective tool for the assessment of truck drivers’ fatigue // Accid. Anal. Prev. 2009. V. 41. № 4. P. 856.</mixed-citation></ref><ref id="B88"><label>88.</label><mixed-citation>Di Stasi L.L., Marchitto M., Antolí A., Cañas J.J. Saccadic peak velocity as an alternative index of operator attention: A short review // Eur. Rev. Appl. Psychol. 2013. V. 63. № 6. P. 335.</mixed-citation></ref><ref id="B89"><label>89.</label><mixed-citation>Diaz-Piedra C., Rieiro H., Suárez J. et al. Fatigue in the military: towards a fatigue detection test based on the saccadic velocity // Physiol. Meas. 2016. V. 37. № 9. P. N62.</mixed-citation></ref><ref id="B90"><label>90.</label><mixed-citation>Ito J., Yamane Y., Suzuki M. et al. Switch from ambient to focal processing mode explains the dynamics of free viewing eye movements // Sci. Rep. 2017. V. 7. № 1. P. 1082.</mixed-citation></ref><ref id="B91"><label>91.</label><mixed-citation>Pannasch S., Velichkovsky B.M. Distractor effect and saccade amplitudes: Further evidence on different modes of processing in free exploration of visual images // Vis. Cogn. 2009. V. 17. № 6-7. P. 1109.</mixed-citation></ref><ref id="B92"><label>92.</label><citation-alternatives><mixed-citation xml:lang="en">Velichkovsky B.M., Korosteleva A.N., Pannasch S. et al. [Two visual systems and their eye movements: A fixation-based event-related experiment with ultrafast fMRI reconciles competing views] // Sovrem. Tehnol. Med. 2019. V. 11. № 4. P. 7.</mixed-citation><mixed-citation xml:lang="ru">Величковский Б.М., Коростелева А.Н., Паннаш С. и др. Две системы зрения и их Движения глаз: эксперимент с фиксациями как событиями и сверхбыстрой фМРТ примиряет соперничающие взгляды // Современные технологии в медицине. 2019. Т. 11. № 4. С. 7.</mixed-citation></citation-alternatives></ref><ref id="B93"><label>93.</label><citation-alternatives><mixed-citation xml:lang="en">Shoshina I.I., Shelepin Yu.E. Mechanisms of global and local analysis of visual information in schizophrenia. St. Petersburg: VVM Publishing House, 2016. 300 p.</mixed-citation><mixed-citation xml:lang="ru">Шошина И.И., Шелепин Ю.Е. Механизмы глобального и локального анализа зрительной информации при шизофрении. СПб.: Изд-во ВВМ, 2016. 300 с.</mixed-citation></citation-alternatives></ref><ref id="B94"><label>94.</label><citation-alternatives><mixed-citation xml:lang="en">Shoshina I.I., Mukhitova Y.V., Tregubenko I.A. et al. Contrast Sensitivity of the Visual System and Cognitive Functions in Schizophrenia and Depression // Human Physiology. 2021. V. 47. № 5. P. 516.</mixed-citation><mixed-citation xml:lang="ru">Шошина И.И., Мухитова Ю.В., Трегубенко И.А. и др. Контрастная чувствительность зрительной системы и когнитивные функции при шизофрении и депрессии // Физиология человека. 2021. Т. 47. № 5. С. 48.</mixed-citation></citation-alternatives></ref><ref id="B95"><label>95.</label><mixed-citation>Milner A.D. How do the two visual streams interact with each other? // Exp. Brain Res. 2017. V. 235. № 5. P. 1297.</mixed-citation></ref><ref id="B96"><label>96.</label><mixed-citation>Kunasegaran K., Ismail A.M.H., Ramasamy S. et al. Understanding mental fatigue and its detection: a comparative analysis of assessments and tools // Peer J. 2023. V. 11. P. e15744</mixed-citation></ref><ref id="B97"><label>97.</label><mixed-citation>Tran Y., Craig A., Craig R. et al. The influence of mental fatigue on brain activity: Evidence from a systematic review with meta-analyses // Psychophysiology. 2020. V. 57. № 5. P. e13554.</mixed-citation></ref><ref id="B98"><label>98.</label><mixed-citation>Hsu T.-Y., Hsu Y.-F., Wang H.-Y., Wang C.-A. Role of the frontal eye field in human pupil and saccade orienting responses // Eur. J. Neurosci. 2021. V. 54. P. 4283.</mixed-citation></ref><ref id="B99"><label>99.</label><mixed-citation>Bafna T., Hansen J.P. Mental fatigue measurement using eye metrics: A systematic literature review // Psychophysiology. 2021. V. 58. № 6. P. e13828.</mixed-citation></ref><ref id="B100"><label>100.</label><mixed-citation>Ansari M.F., Kasprowski P., Obetkal M. Gaze tracking using an unmodified web camera and convolutional neural network // Appl. Sci. 2021. V. 11. № 19. P. 9068.</mixed-citation></ref><ref id="B101"><label>101.</label><mixed-citation>Naeeri S., Kang Z., Mandal S., Kim K. Multimodal analysis of eye movements and fatigue in a simulated glass cockpit environment // Aerospace. 2021. V. 8. № 10. P. 283.</mixed-citation></ref><ref id="B102"><label>102.</label><mixed-citation>Mengtao L., Fan L., Gangyan X., Su H. Leveraging eye-tracking technologies to promote aviation safety – A review of key aspects, challenges, and future perspectives // Saf. Sci. 2023. V. 168. P. 106295.</mixed-citation></ref><ref id="B103"><label>103.</label><mixed-citation>Hu X., Lodewijks G. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue // J. Safety Res. 2020. V. 72. P. 173.</mixed-citation></ref><ref id="B104"><label>104.</label><mixed-citation>Zhimin L., Ruilin L., Liqiang Y. et al. A benchmarking framework for eye-tracking-based vigilance prediction of vessel traffic controllers // Eng. Appl. Artif. Intell. 2024. V. 129. P. e107660.</mixed-citation></ref></ref-list></back></article>
