Cited 2 time in webofscience Cited 3 time in scopus

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

Title
Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals
Authors
Tjolleng, A[Tjolleng, Amir]Jung, K[Jung, Kihyo]Hong, WG[Hong, Wongi]Lee, W[Lee, Wonsup]Lee, B[Lee, Baekhee]You, H[You, Heecheon]Son, J[Son, Joonwoo]Park, S[Park, Seikwon]
DGIST Authors
Son, J[Son, Joonwoo]
Issue Date
2017-03
Citation
Applied Ergonomics, 59, 326-332
Type
Article
Article Type
Article
Keywords
AdultArtificial HeartArtificial Neural NetworkArtificial Neural Network ModelsAutomobile DrivingBackpropagationBackpropagation LearningBiomedical Signal ProcessingCar DrivingClassificationCognitionCognitive Workload ClassificationCognitive WorkloadsComputer SimulationData HandlingElectrocardiographyHeart RateHeart Rate VariabilityHumanHumansIndividual DifferencesLow and High FrequenciesMaleNeural NetworksNeural Networks (Computer)PhysiologyProcessing ProceduresPsychologyScaled Conjugate GradientsWorkloadYoung Adult
ISSN
0003-6870
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%). © 2016 Elsevier Ltd
URI
http://hdl.handle.net/20.500.11750/2040
DOI
10.1016/j.apergo.2016.09.013
Publisher
Elsevier Ltd
Related Researcher
Files:
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Collection:
Companion Diagnostics and Medical Technology Research Group1. Journal Articles


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