Predicting Driver's mental workload using physiological signals: A functional data analysis approach

Chaeyoung Lee, Min Ju Shin, David Eniyandunmo, Alvee Anwar, Eunsik Kim, Kyongwon Kim, Jae Keun Yoo, Chris Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers’ mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

Original languageEnglish
Article number104274
JournalApplied Ergonomics
Volume118
DOIs
StatePublished - Jul 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • Driver mental workload
  • Functional data analysis
  • Physiological signals

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