Utilising raw psycho-physiological data and functional data analysis for estimating mental workload in human drivers

Author:

Eniyandunmo David1,Shin MinJu12,Lee Chaeyoung12,Anwar Alvee1,Kim Eunsik1ORCID,Kim Kyongwon2,Kim Yong Hoon3,Lee Chris3

Affiliation:

1. Mechanical, Automotive, and Materials Engineering, University of Windsor, Windsor, ON, Canada

2. Department of Statistics, Ewha Womans University, Seoul, Republic of Korea

3. Civil and Environmental Engineering, University of Windsor, Windsor, ON, Canada

Funder

University of Windsor

MSIT

the High-Potential Individuals Global Training Program

Innovating Sustainability Grant program

Publisher

Informa UK Limited

Reference66 articles.

1. A System For Drivers’ Cognitive Load Estimation Based On Deep Convolutional Neural Networks and Facial Feature Analysis

2. Anjaneya B. D. Snehal Z. Yu K. Eunsik S. Chao S. Xiaochen T. Ibrahim Al and R. Ling. 2022. Psychophysiological Assessment of Mental Workload. In Maynard’s Industrial and Systems Engineering Handbook New York: 6 McGraw Hill Professional.

3. Drivers’ Braking Behavior Affected by Cognitive Distractions: An Experimental Investigation with a Virtual Car Simulator

4. Driver inattention and driver distraction in serious casualty crashes: Data from the Australian National Crash In-depth Study

5. EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks;Berka Chris;Aviation, Space, and Environmental Medicine,2007

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