Citation

Eniyandunmo D, Shin MJ, Lee C, Anwar A, Kim E, Kim K, Kim YH, Lee C. Ergonomics 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, Informa - Taylor and Francis Group)

DOI

10.1080/00140139.2024.2379949

PMID

39037945

Abstract

Recent studies have focused on accurately estimating mental workload using machine learning algorithms and extracting features from physiological measures. However, feature extraction leads to the loss of valuable information and often results in binary classifications that lack specificity in the identification of optimum mental workload. This study investigates the feasibility of using raw physiological data (EEG, facial EMG, ECG, EDA, pupillometry) combined with Functional Data Analysis (FDA) to estimate the mental workload of human drivers. A driving scenario with five tasks was employed, and subjective ratings were collected.

RESULTS demonstrate that the FDA applied nine different combinations of raw physiological signals achieving a maximum 90% accuracy, outperforming extracted features by 73%. This study shows that the mental workload of human drivers can be accurately estimated without utilising burdensome feature extraction. The approach proposed in this study offers promise for mental workload assessment in real-world applications.

Language: en

Keywords

Bio-signals; estimation model; functional data analysis; human driver; mental workload; model comparison; physiological; Psycho-physiological; subjective ratings