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Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
2020 (English)In: Brain Sciences, E-ISSN 2076-3425, Vol. 10, no 8, article id 526Article in journal (Refereed) Published
Abstract [en]

One debatable issue in traffic safety research is that cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, k-nearest neighbour (k-NN), support vector machine (SVM) and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., Sequential Forward Floating Selection (SFFS) and Random Forest have been applied where out of 323 features, a sub-set of 42 features has been selected as the best feature subset. For the classification, the RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Also, the result shows that using multicomponent features classifiers could classify better than using features from a single source.

Place, publisher, year, edition, pages
Switzerland, 2020. Vol. 10, no 8, article id 526
Keywords [en]
Cognitive load, machine learning, multimodal data analytics, multicomponent signals
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-49984DOI: 10.3390/brainsci10080526ISI: 000564583500001PubMedID: 32781777Scopus ID: 2-s2.0-85090399516OAI: oai:DiVA.org:mdh-49984DiVA, id: diva2:1466113
Projects
VDM - Vehicle Driver MonitoringEmbedded Sensor Systems for Health PlusAvailable from: 2020-09-10 Created: 2020-09-10 Last updated: 2024-07-04Bibliographically approved

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Barua, ShaibalAhmed, Mobyen UddinBegum, Shahina

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