Tavakoli, A., Balali, V., Heydarian, A.: A Multimodal Approach for Monitoring Driving Behavior and Emotions (2020)
Google Scholar
Kleinginna, P.R., Kleinginna, A.M.: A categorized list of motivation definitions, with a suggestion for a consensual definition. Motiv. Emot. 5(3), 263–291 (1981). https://doi.org/10.1007/BF00993889
CrossRef
Google Scholar
Pittermann, J., Pittermann, A., Minker, W.: Emotion recognition and adaptation in spoken dialogue systems. Int. J. Speech Technol. 13(1), 49–60 (2010). https://doi.org/10.1007/s10772-010-9068-y
CrossRef
Google Scholar
Leahu, L., Schwenk, S., Sengers, P.: Subjective objectivity: negotiating emotional meaning. In: Proceedings of the 7th ACM Conference on Designing Interactive Systems, pp. 425–434 (2008)
Google Scholar
Picard, R.W., Klein, J.: Computers that recognise and respond to user emotion: theoretical and practical implications. Interact. Comput. 14(2), 141–169 (2002)
CrossRef
Google Scholar
Cohn, J.F.: Foundations of human computing: facial expression and emotion. In: Proceedings of the 8th International Conference on Multimodal Interfaces, pp. 233–238 (2006)
Google Scholar
Martis, J.E.: Effective emotion recognition of expressions from facial features. 5(06), 4–7 (2017)
Google Scholar
Geiger, A., Brandenburg, E., Stark, R.: Natural virtual reality user interface to define assembly sequences for digital human models. Appl. Syst. Innov. 3(1), 15 (2020)
CrossRef
Google Scholar
Sedenberg, E., Wong, R., Chuang, J.: A window into the soul: biosensing in public. arXiv preprint arXiv:1702.04235 (2017)
Liu, L., et al.: Deep learning for generic object detection: a survey. arXiv 2018. arXiv preprint arXiv:1809.02165 (2019)
Martinez-Conde, S., Macknik, S.L., Hubel, D.H.: The role of fixational eye movements in visual perception. Nat. Rev. Neurosci. 5(3), 229–240 (2004)
CrossRef
Google Scholar
Baujon, J., Basset, M., Gissinger, G.L.: Visual behaviour analysis and driver cognitive model. In: Proceedings of the 3rd IFAC Workshop on Advances in Automotive Control, Karlsruhe, Germany, pp. 47–52 (2001)
Google Scholar
Brackstone, M., Waterson, B.: Are we looking where we are going? An exploratory examination of eye movement in high-speed driving. In: Proceedings of the 83rd Transportation Research Board Annual Meeting, vol. 2, p. 602 (2004)
Google Scholar
Yan, Y., Yuan, H., Wang, X., Xu, T., Liu, H.: Study on driver’s fixation variation at entrance and inside sections of tunnel on highway. Adv. Mech. Eng. 7(1), 273427 (2015)
CrossRef
Google Scholar
Nadal, M., Munar, E., Marty, G., Cela-Conde, C.J.: Visual complexity and beauty appreciation: explaining the divergence of results. Empirical Stud. Arts 28(2), 173–191 (2010)
CrossRef
Google Scholar
Chan, M., Singhal, A.: Emotion matters: Implications for distracted driving. Saf. Sci. 72, 302–309 (2015)
CrossRef
Google Scholar
Zhang, W., Zhang, X., Feng, Z., Liu, J., Zhou, M., Wang, K.: The fitness-to-drive of shift-work taxi drivers with obstructive sleep apnea: an investigation of self-reported driver behavior and skill. Transp. Res. Part F: Traffic Psychol. Behav. 59, 545–554 (2018)
CrossRef
Google Scholar
Eherenfreund-Hager, A., Taubman-Ben-Ari, O., Toledo, T., Farah, H.: The effect of positive and negative emotions on young drivers a simulator study. Transp. Res. Part F: Traffic Psychol. Behav. 49, 236–243 (2017)
Google Scholar
Du, N., et al.: Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving. Transp. Res. Part C: Emerg. Technol. 112, 78–87 (2020)
CrossRef
Google Scholar
Hedlund, J., Simpson, H.M., Mayhew, D.R.: Summary of proceedings and recommendations. In: International Conference on Distracted Driving. The Traffic Injury Research Foundation, The Canadian Automobile Association, Ottawa (2006)
Google Scholar
Nyström, M., Holmqvist, K.: An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data. Behav. Res. Methods 42(1), 188–204 (2010). https://doi.org/10.3758/BRM.42.1.188
CrossRef
Google Scholar
Ekman, P., Friesen, W.V.: Facial Action Coding System: Investigator’s Guide. Consulting Psychologists Press, Palo Alto (1978)
Google Scholar
Steyer, R., Schwenkmezger, P., Notz, P., Eid, M.: MDMQ questionnaire (English version of MDBF). Jena: Friedrich-Schiller-Universität Jena, Institut für Psychologie, Lehrstuhl für Methodenlehre und Evaluationsforschung (2014). https://www.metheval.uni-jena.de/mdbf.php. Accessed 4 Apr 2016
Meinlschmidt, G., et al.: Smartphone-based psychotherapeutic micro-interventions to improve mood in a real-world setting. Front. Psychol. 7, 1112 (2016)
CrossRef
Google Scholar
Hinz, A., Daig, I., Petrowski, K., Brähler, E.: Die stimmung in der deutschen bevölkerung: referenzwerte für den mehrdimensionalen befindlichkeitsfragebogen MDBF. PPmP-Psychother. Psychosom. Med. Psychol. 62(02), 52–57 (2012)
CrossRef
Google Scholar
Park, J., Abdel-Aty, M., Yina, W., Mattei, I.: Enhancing in-vehicle driving assistance information under connected vehicle environment. IEEE Trans. Intell. Transp. Syst. 20(9), 3558–3567 (2018)
CrossRef
Google Scholar
Lynch, B.K.: Designing qualitative research by catherine marshall an Gretchen B. Rossman. Issues Appl. Linguist. 1(2), 1–9 (1990)
CrossRef
Google Scholar
Schutte, N.S., Malouff, J.M., Thorsteinsson, E.B., Bhullar, N., Rooke, S.E.: A meta-analytic investigation of the relationship between emotional intelligence and health. Pers. Individ. Differ. 42(6), 921–933 (2007)
CrossRef
Google Scholar
Guarnera, M., Hichy, Z., Cascio, M.I., Carrubba, S.: Facial expressions and ability to recognize emotions from eyes or mouth in children. Eur. J. Psychol. 11(2), 183 (2015)
CrossRef
Google Scholar
Li, J., Jin, K., Zhou, D., Kubota, N., Zhaojie, J.: Attention mechanism-based CNN for facial expression recognition. Neurocomputing 411, 340–350 (2020)
CrossRef
Google Scholar
Hassib, M., Braun, M., Pfleging, B., Alt, F.: Detecting and influencing driver emotions using psycho-physiological sensors and ambient light. In: Lamas, D., Loizides, F., Nacke, L., Petrie, H., Winckler, M., Zaphiris, P. (eds.) Human-Computer Interaction – INTERACT 2019, vol. 11746, pp. 721–742. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29381-9_43
CrossRef
Google Scholar
Mesken, J.: Determinants and consequences of drivers’ emotions. Stichting Wetenschappelijk Onderzoek Verkeersveiligheid SWOV (2006)
Google Scholar
Remington, R.W.: Attention and saccadic eye movements. J. Exp. Psychol.: Hum. Percept. Perform. 6(4), 726 (1980)
Google Scholar
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation and augmented reality tracking: an integrated system and evaluation for monitoring driver awareness. IEEE Trans. Intell. Transp. Syst. 11(2), 300–311 (2010)
CrossRef
Google Scholar
Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953 (2012)
CrossRef
Google Scholar
Mashko, A.: Subjective methods for assessment of driver drowsiness. Acta Polytech. CTU Proc. 12, 64–67 (2017)
CrossRef
Google Scholar