Open AccessReal‐time eye tracking for the assessment of driver fatigue
Author(s) -
Xu Junli,
Min Jianliang,
Hu Jianfeng
Publication year - 2018
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2017.0020
Subject(s) - eye tracking , eye movement , pupil , computer science , eye tracking on the iss , fixation (population genetics) , artificial intelligence , gaze , computer vision , simulation , driving simulator , computation , psychology , medicine , algorithm , population , environmental health , neuroscience
Eye‐tracking is an important approach to collect evidence regarding some participants’ driving fatigue. In this contribution, the authors present a non‐intrusive system for evaluating driver fatigue by tracking eye movement behaviours. A real‐time eye‐tracker was used to monitor participants’ eye state for collecting eye‐movement data. These data are useful to get insights into assessing participants’ fatigue state during monotonous driving. Ten healthy subjects performed continuous simulated driving for 1–2 h with eye state monitoring on a driving simulator in this study, and these measured features of the fixation time and the pupil area were recorded via using eye movement tracking device. For achieving a good cost‐performance ratio and fast computation time, the fuzzy K ‐nearest neighbour was employed to evaluate and analyse the influence of different participants on the variations in the fixation duration and pupil area of drivers. The findings of this study indicated that there are significant differences in domain value distribution of the pupil area under the condition with normal and fatigue driving state. Result also suggests that the recognition accuracy by jackknife validation reaches to about 89% in average, implying that show a significant potential of real‐time applicability of the proposed approach and is capable of detecting driver fatigue.