Literature DB >> 28237018

Electrocardiographic features for the measurement of drivers' mental workload.

Tobias Heine1, Gustavo Lenis2, Patrick Reichensperger2, Tobias Beran3, Olaf Doessel2, Barbara Deml3.   

Abstract

This study examines the effect of mental workload on the electrocardiogram (ECG) of participants driving the Lane Change Task (LCT). Different levels of mental workload were induced by a secondary task (n-back task) with three levels of difficulty. Subjective data showed a significant increase of the experienced workload over all three levels. An exploratory approach was chosen to extract a large number of rhythmical and morphological features from the ECG signal thereby identifying those which differentiated best between the levels of mental workload. No single rhythmical or morphological feature was able to differentiate between all three levels. A group of parameters were extracted which were at least able to discriminate between two levels. For future research, a combination of features is recommended to achieve best diagnosticity for different levels of mental workload.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Driving; Electrocardiogram; Feature comparison; Heart rate variability; Mental workload; Wave morphology

Mesh:

Year:  2017        PMID: 28237018     DOI: 10.1016/j.apergo.2016.12.015

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  8 in total

1.  Evaluating mental workload during multitasking in simulated flight.

Authors:  Wenbin Li; Rong Li; Xiaoping Xie; Yaoming Chang
Journal:  Brain Behav       Date:  2022-03-15       Impact factor: 3.405

2.  Assessment of mental workload based on multi-physiological signals.

Authors:  Xiaoli Fan; Chaoyi Zhao; Xin Zhang; Hong Luo; Wei Zhang
Journal:  Technol Health Care       Date:  2020       Impact factor: 1.285

3.  A Systematic Review of Physiological Measures of Mental Workload.

Authors:  Da Tao; Haibo Tan; Hailiang Wang; Xu Zhang; Xingda Qu; Tingru Zhang
Journal:  Int J Environ Res Public Health       Date:  2019-07-30       Impact factor: 3.390

4.  Real-time prediction of short-timescale fluctuations in cognitive workload.

Authors:  Udo Boehm; Dora Matzke; Matthew Gretton; Spencer Castro; Joel Cooper; Michael Skinner; David Strayer; Andrew Heathcote
Journal:  Cogn Res Princ Implic       Date:  2021-04-09

5.  Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.

Authors:  Serajeddin Ebrahimian; Ali Nahvi; Masoumeh Tashakori; Hamed Salmanzadeh; Omid Mohseni; Timo Leppänen
Journal:  Int J Environ Res Public Health       Date:  2022-08-29       Impact factor: 4.614

6.  Classification of Drivers' Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals.

Authors:  Daniela Cardone; David Perpetuini; Chiara Filippini; Lorenza Mancini; Sergio Nocco; Michele Tritto; Sergio Rinella; Alberto Giacobbe; Giorgio Fallica; Fabrizio Ricci; Sabina Gallina; Arcangelo Merla
Journal:  Sensors (Basel)       Date:  2022-09-26       Impact factor: 3.847

7.  Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving.

Authors:  Antonio R Hidalgo-Muñoz; Adolphe J Béquet; Mathis Astier-Juvenon; Guillaume Pépin; Alexandra Fort; Christophe Jallais; Hélène Tattegrain; Catherine Gabaude
Journal:  Front Hum Neurosci       Date:  2019-01-07       Impact factor: 3.169

8.  No Difference in Arousal or Cognitive Demands Between Manual and Partially Automated Driving: A Multi-Method On-Road Study.

Authors:  Monika Lohani; Joel M Cooper; Gus G Erickson; Trent G Simmons; Amy S McDonnell; Amanda E Carriero; Kaedyn W Crabtree; David L Strayer
Journal:  Front Neurosci       Date:  2021-06-10       Impact factor: 4.677

  8 in total

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