Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals

Journal article (2017)

Authors

Amir Tjolleng University of Ulsan

Kihyo Jung University of Ulsan

Wongi Hong LIG Nex1

W. Lee Applied Ergonomics and Design -

Baekhee Lee Pohang University of Science and Technology

Heecheon You Pohang University of Science and Technology

Joonwoo Son Daegu Gyeongbuk Institute of Science and Technology

Seikwon Park Korea Air Force Academy

Research Group

Applied Ergonomics and Design () (TU Delft)

Heart rate variability Artificial neural network Cognitive workload classification

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Published Date

2017

Language

English

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Faculty

Industrial Design Engineering

Department

Human-Centered Design

Research Group

Applied Ergonomics and Design

Abstract

An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male
participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG
measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).

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