Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

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 F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.

What’s on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving

“…However, subjective measures are hard to obtain in real time as the driver's tasks must be interrupted to report his mental state. Additionally, most of the previous work studying MWL in the driving context use the n-back paradigm as the secondary task [4,70,71,90,92]. The 1,2,3-back versions of the task (the 0-back version is designed for vigilance and does not induce mental workload [56]) induce continuous task demand on the working memory, like monitoring, updating information, and rule-based task decisions [82].…”

Cognitive Load Monitoring With Wearables–Lessons Learned From a Machine Learning Challenge

“…Barua et al [38] used the n-back task to assess cognitive load in drivers while measuring their physiological signals (ECG, GSR, respiration, EEG, electrooculography). The authors used various ML models, including k-nearest neighbor (k-NN), SVM and random forest for classifying cognitive load, and random forest outperformed other methods.…”

Pattern analysis of physiological data for the assessment of mental workload

“…Yet, it has to be stated that not all algorithms can cope with physiological data, especially with the problem of simultaneously measured channels of different devices (e.g. ECG, eye tracking, EEG) and their meaningful interaction (Barua et al 2015(Barua et al , 2020. Furthermore, the varied latencies of the used parameters complicate the application.…”