The Role of Eco-Driving and Wearable Sensors in Industry 4.0

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This study investigates the relationship between drivers’ electrodermal activity (EDA) and their eco-driving behaviours through real-time monitoring. Electrodermal activity, a physiological marker of sympathetic nervous system arousal, reflects emotional and cognitive states, providing a valuable window into drivers’ internal experiences. EDA and driving data were collected for 48 trips from 10 different drivers. Cluster analysis and the Pearson correlation coefficient was used to uncover potential patterns between driver EDA and their driving behaviour as measured using a driving score. The results follow the Yerkes-Dodson Law. Driving performance increase with EDA arousal, but only to a point. The investigation has implications for enhancing road safety, as it contributes to our understanding of how drivers’ emotional states influence their on-road performance. Additionally, it holds promise for developing innovative in-car systems that can adapt to drivers’ changing emotional states, promoting safer and more comfortable driving experiences. Ultimately, this study bridges the gap between psychophysiology and transportation, shedding light on the often-overlooked emotional aspects of driving behaviour.

Original languageEnglish (US)
Title of host publicationEngineering Asset Management Review
PublisherSpringer Science and Business Media Deutschland GmbH
Pages207-230
Number of pages24
DOIs
StatePublished - 2024

Publication series

NameEngineering Asset Management Review
Volume3
ISSN (Print)2190-7846
ISSN (Electronic)2190-7854

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Eco-driving
  • Electrodermal activity
  • Industry 4.0
  • Wearable sensors

Fingerprint

Dive into the research topics of 'The Role of Eco-Driving and Wearable Sensors in Industry 4.0'. Together they form a unique fingerprint.

Cite this