ArticleBrain sciences2020
Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Article
- Continuous Assessment of Mental Workload During Complex Human-Machine Interaction: Inferring Cognitive State from Signals External to the Operator.Sensors (Basel, Switzerland) · 2025Article
- Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking Environments.Sensors (Basel, Switzerland) · 2024Article
- Working Memory Ability Evaluation Based on Fuzzy Support Vector Regression.Sensors (Basel, Switzerland) · 2023Article
- A systematic review of physiological signals based driver drowsiness detection systems.Cognitive neurodynamics · 2023Review
- Food Choices after Cognitive Load: An Affective Computing Approach.Sensors (Basel, Switzerland) · 2023Article
- Assessing Fitness-To-Drive among Older Drivers: A Comparative Analysis of Potential Alternatives to on-Road Driving Test.International journal of environmental research and public health · 2020Article
- Pattern Recognition of Cognitive Load Using EEG and ECG Signals.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One debatable issue in traffic safety research is that the 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, the 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 subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.