Literature DB >> 29409621

An analysis of the suitability of a low-cost eye tracker for assessing the cognitive load of drivers.

Tomaž Čegovnik1, Kristina Stojmenova2, Grega Jakus3, Jaka Sodnik4.   

Abstract

This paper presents a driving simulator study in which we investigated whether the Eye Tribe eye tracker (ET) is capable of assessing changes in the cognitive load of drivers through oculography and pupillometry. In the study, participants were asked to drive a simulated vehicle and simultaneously perform a set of secondary tasks with different cognitive complexity levels. We measured changes in eye properties, such as the pupil size, blink rate and fixation time. We also performed a measurement with a Detection Response Task (DRT) to validate the results and to prove a steady increase of cognitive load with increasing secondary task difficulty. The results showed that the ET precisely recognizes an increasing pupil diameter with increasing secondary task difficulty. In addition, the ET shows increasing blink rates, decreasing fixation time and narrowing of the attention field with increasing secondary task difficulty. The results were validated with the DRT method and the secondary task performance. We conclude that the Eye Tribe ET is a suitable device for assessing a driver's cognitive load.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cognitive load; Driver; Driving simulator; Eye Tribe; Eye tracking; Pupillometry

Mesh:

Year:  2017        PMID: 29409621     DOI: 10.1016/j.apergo.2017.10.011

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  4 in total

1.  Eye tracking: empirical foundations for a minimal reporting guideline.

Authors:  Kenneth Holmqvist; Saga Lee Örbom; Ignace T C Hooge; Diederick C Niehorster; Robert G Alexander; Richard Andersson; Jeroen S Benjamins; Pieter Blignaut; Anne-Marie Brouwer; Lewis L Chuang; Kirsten A Dalrymple; Denis Drieghe; Matt J Dunn; Ulrich Ettinger; Susann Fiedler; Tom Foulsham; Jos N van der Geest; Dan Witzner Hansen; Samuel B Hutton; Enkelejda Kasneci; Alan Kingstone; Paul C Knox; Ellen M Kok; Helena Lee; Joy Yeonjoo Lee; Jukka M Leppänen; Stephen Macknik; Päivi Majaranta; Susana Martinez-Conde; Antje Nuthmann; Marcus Nyström; Jacob L Orquin; Jorge Otero-Millan; Soon Young Park; Stanislav Popelka; Frank Proudlock; Frank Renkewitz; Austin Roorda; Michael Schulte-Mecklenbeck; Bonita Sharif; Frederick Shic; Mark Shovman; Mervyn G Thomas; Ward Venrooij; Raimondas Zemblys; Roy S Hessels
Journal:  Behav Res Methods       Date:  2022-04-06

Review 2.  Detection-Response Task-Uses and Limitations.

Authors:  Kristina Stojmenova; Jaka Sodnik
Journal:  Sensors (Basel)       Date:  2018-02-14       Impact factor: 3.576

3.  Identifying the Causes of Drivers' Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements.

Authors:  Ali Darzi; Sherif M Gaweesh; Mohamed M Ahmed; Domen Novak
Journal:  Front Neurosci       Date:  2018-08-14       Impact factor: 4.677

Review 4.  Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis.

Authors:  Qaisar Abbas; Abdullah Alsheddy
Journal:  Sensors (Basel)       Date:  2020-12-24       Impact factor: 3.576

  4 in total

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