Literature DB >> 25857673

Physiological responses related to moderate mental load during car driving in field conditions.

Henrik Wiberg1, Emma Nilsson2, Per Lindén2, Bo Svanberg2, Leo Poom3.   

Abstract

We measured physiological variables on nine car drivers to capture moderate magnitudes of mental load (ML) during driving in prolonged and repeated city and highway field conditions. Ecological validity was optimized by avoiding any artificial interference to manipulate drivers ML, drivers were alone in the car, they were free to choose their paths to the target, and the repeated drives familiarized drivers to the procedure. Our aim was to investigate if driver's physiological variables can be reliably measured and used as predictors of moderate individual levels of ML in naturally occurring unpredictably changing field conditions. Variables investigated were: heart-rate, skin conductance level, breath duration, blink frequency, blink duration, and eye fixation related potentials. After the drives, with support from video uptakes, a self-rating and a score made by external raters were used to distinguish moderately high and low ML segments. Variability was high but aggregated data could distinguish city from highway drives. Multivariate models could successfully classify high and low ML within highway and city drives using physiological variables as input. In summary, physiological variables have a potential to be used as indicators of moderate ML in unpredictably changing field conditions and to advance the evaluation and development of new active safety systems.
Copyright © 2015 Elsevier B.V. All rights reserved.

Keywords:  Car driving; Field conditions; Mental load; Multivariate models; Physiology; Workload

Mesh:

Year:  2015        PMID: 25857673     DOI: 10.1016/j.biopsycho.2015.03.017

Source DB:  PubMed          Journal:  Biol Psychol        ISSN: 0301-0511            Impact factor:   3.251


  3 in total

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Authors:  William Romine; Noah Schroeder; Tanvi Banerjee; Josephine Graft
Journal:  Sensors (Basel)       Date:  2022-09-28       Impact factor: 3.847

2.  A comprehensive prediction and evaluation method of pilot workload.

Authors:  Chuanyan Feng; Xiaoru Wanyan; Kun Yang; Damin Zhuang; Xu Wu
Journal:  Technol Health Care       Date:  2018       Impact factor: 1.285

3.  Using Machine Learning to Train a Wearable Device for Measuring Students' Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom Use.

Authors:  William L Romine; Noah L Schroeder; Josephine Graft; Fan Yang; Reza Sadeghi; Mahdieh Zabihimayvan; Dipesh Kadariya; Tanvi Banerjee
Journal:  Sensors (Basel)       Date:  2020-08-27       Impact factor: 3.576

  3 in total

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