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

Chaeyoung Lee¹, MinJu Shin¹, David Eniyandunmo²

¹Mechanical, Automotive, and Materials Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada; Department of Statistics, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea.

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|March 23, 2024

Summary

This summary is machine-generated.

This study accurately predicts driver mental workload using physiological signals like ECG and EEG. Advanced driver-assistance systems can be improved by understanding these workload levels for enhanced safety.

Keywords:

Driver mental workload Functional data analysis Physiological signals

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Area of Science:

Human-Computer Interaction
Cognitive Ergonomics
Automotive Engineering

Background:

Advanced driver-assistance systems (ADAS) are increasingly integrated into vehicles.
Assessing driver mental workload is crucial for ADAS design and traffic safety.
Existing methods for workload assessment can be intrusive or subjective.

Purpose of the Study:

To investigate the impact of ADAS on driver mental workload.
To develop a predictive model for categorizing mental workload levels (low, adequate, high).
To evaluate the efficacy of physiological signals in workload assessment.

Main Methods:

Collected physiological data including ECG, EMG, EDA, EEG (theta band, af4/fc6 channels), and eye diameter.
Utilized functional linear regression models for workload prediction.
Tested 31 different combinations of physiological variables.

Main Results:

Achieved a highest accuracy rate of 90% in predicting mental workload.
Identified 9 specific combinations of physiological signals yielding optimal prediction accuracy.
Demonstrated the feasibility of using raw physiological data for workload assessment.

Conclusions:

Raw physiological signals, analyzed with functional data methods, can effectively assess driver mental workload.
The findings support the integration of physiological monitoring into ADAS for improved performance and safety.
This approach offers a more objective and precise method for understanding driver cognitive states.