Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals. (March 2017)

• Record Type: Journal Article
• Authors: Tjolleng, Amir; Jung, Kihyo; Hong, Wongi; Lee, Wonsup; Lee, Baekhee; You, Heecheon; Son, Joonwoo; Park, Seikwon
• Is Part Of: Applied ergonomics. Volume 59:Part A(2017)
• Page Start: 326
• Page End: 332
• Publication Date: 2017-03
• Subjects: Cognitive workload classification -- Heart rate variability -- Artificial neural network; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2016.09.013
• Languages: English
• ISSNs: 0003-6870

Abstract

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%). Highlights: An artificial neural network (ANN) model was developed to classify the level of cognitive workload. A three-step data processing was performed to compensate for individual differences in heart response. Six ECG measures in time (mean IBI, SDNN, and RMSSD) and frequency (LF, HF, and LF/HF) domains were collected. Accuracy of the ANN model was found satisfactory for learning data (95%) and testing data (82%).