Artificial intelligence-enabled non-intrusive vigilance assessment approach to reducing traffic controller’s human errors

作者：

Highlights：

• A novel temporal and spatial gaze patterns analytic method is proposed.

• Investigated fatigue-induced performance impairment in terms of perception, comprehension, and action.

• Gaze movements analytics explained the fatigue-motor performance paradox.

• A shallow neural network model was developed to assess vigilance levels.

摘要

•A novel temporal and spatial gaze patterns analytic method is proposed.•Investigated fatigue-induced performance impairment in terms of perception, comprehension, and action.•Gaze movements analytics explained the fatigue-motor performance paradox.•A shallow neural network model was developed to assess vigilance levels.

论文关键词：Eye-tracking,Gaze pattern,Fatigue,Maritime,Human performance,Shallow neural network

论文评审过程：Received 6 September 2021, Revised 28 October 2021, Accepted 24 December 2021, Available online 30 December 2021, Version of Record 14 January 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.108047