Regression Based Continuous Driving Fatigue Estimation: Towards Practical Implementation

Rohit Bose, Hongtao Wang, Andrei Dragomir, Nitish Thakor, Anastasios Bezerianos, Junhua Li

Research output: Contribution to journalArticle

Abstract

Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

Original languageEnglish (US)
JournalIEEE Transactions on Cognitive and Developmental Systems
DOIs
StateAccepted/In press - Jan 1 2019
Externally publishedYes

Fingerprint

Fatigue of materials
Highway accidents
Monitoring
Electroencephalography
Spatial distribution
Frequency bands
Labels
Computational complexity
Pipelines
Electrodes
Processing

Keywords

  • Brain modeling
  • Driving Fatigue Estimation
  • Dry Electrode
  • Dynamic Time Warping.
  • EEG
  • Electrodes
  • Electroencephalography
  • Estimation
  • Fatigue
  • Feature extraction
  • Regression
  • Support vector machines
  • Wireless Transmission

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Regression Based Continuous Driving Fatigue Estimation : Towards Practical Implementation. / Bose, Rohit; Wang, Hongtao; Dragomir, Andrei; Thakor, Nitish; Bezerianos, Anastasios; Li, Junhua.

In: IEEE Transactions on Cognitive and Developmental Systems, 01.01.2019.

Research output: Contribution to journalArticle

@article{c99cad6877e44ad18da3bfbb6157fae6,
title = "Regression Based Continuous Driving Fatigue Estimation: Towards Practical Implementation",
abstract = "Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75{\%}, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.",
keywords = "Brain modeling, Driving Fatigue Estimation, Dry Electrode, Dynamic Time Warping., EEG, Electrodes, Electroencephalography, Estimation, Fatigue, Feature extraction, Regression, Support vector machines, Wireless Transmission",
author = "Rohit Bose and Hongtao Wang and Andrei Dragomir and Nitish Thakor and Anastasios Bezerianos and Junhua Li",
year = "2019",
month = "1",
day = "1",
doi = "10.1109/TCDS.2019.2929858",
language = "English (US)",
journal = "IEEE Transactions on Cognitive and Developmental Systems",
issn = "2379-8920",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Regression Based Continuous Driving Fatigue Estimation

T2 - Towards Practical Implementation

AU - Bose, Rohit

AU - Wang, Hongtao

AU - Dragomir, Andrei

AU - Thakor, Nitish

AU - Bezerianos, Anastasios

AU - Li, Junhua

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

AB - Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

KW - Brain modeling

KW - Driving Fatigue Estimation

KW - Dry Electrode

KW - Dynamic Time Warping.

KW - EEG

KW - Electrodes

KW - Electroencephalography

KW - Estimation

KW - Fatigue

KW - Feature extraction

KW - Regression

KW - Support vector machines

KW - Wireless Transmission

UR - http://www.scopus.com/inward/record.url?scp=85071036774&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071036774&partnerID=8YFLogxK

U2 - 10.1109/TCDS.2019.2929858

DO - 10.1109/TCDS.2019.2929858

M3 - Article

AN - SCOPUS:85071036774

JO - IEEE Transactions on Cognitive and Developmental Systems

JF - IEEE Transactions on Cognitive and Developmental Systems

SN - 2379-8920

ER -