A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring

Ashwini Kanakapura Sriranga; Qian Lu; Stewart Birrell

doi:10.3390/s23042214

A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring

Ashwini Kanakapura Sriranga
, Qian Lu
, Stewart Birrell

Coventry University

Research output: Contribution to journal › Review article › peer-review

30 Citations (Scopus)

204 Downloads (Pure)

Abstract

The concept of vehicle automation ceases to seem futuristic with the current advancement of the automotive industry. With the introduction of conditional automated vehicles, drivers are no longer expected to focus only on driving activities but are still required to stay alert to resume control. However, fluctuations in driving demands are known to alter the driver's mental workload (MWL), which might affect the driver's vehicle take-over capabilities. Driver mental workload can be specified as the driver's capacity for information processing for task performance. This paper summarizes the literature that relates to analysing driver mental workload through various in-vehicle physiological sensors focusing on cardiovascular and respiratory measures. The review highlights the type of study, hardware, method of analysis, test variable, and results of studies that have used physiological indices for MWL analysis in the automotive context.

Original language	English
Article number	2214
Pages (from-to)	2214
Number of pages	1
Journal	Sensors
Volume	23
Issue number	4
Early online date	16 Feb 2023
DOIs	https://doi.org/10.3390/s23042214
Publication status	Published - 16 Feb 2023

Bibliographical note

Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)

Funding

This research was funded by Coventry University grant number BDN68KDUX.

Funders	Funder number
Coventry University	BDN68KDUX
UK Research and Innovation	103436

Keywords

HR
HRV
conditional automation
mental workload
respiration

ASJC Scopus subject areas

Analytical Chemistry
Information Systems
Instrumentation
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering
Biochemistry

Access to Document

10.3390/s23042214

PublishedFinal published version, 1 MBLicence: CC BY

Cite this

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abstract = "The concept of vehicle automation ceases to seem futuristic with the current advancement of the automotive industry. With the introduction of conditional automated vehicles, drivers are no longer expected to focus only on driving activities but are still required to stay alert to resume control. However, fluctuations in driving demands are known to alter the driver's mental workload (MWL), which might affect the driver's vehicle take-over capabilities. Driver mental workload can be specified as the driver's capacity for information processing for task performance. This paper summarizes the literature that relates to analysing driver mental workload through various in-vehicle physiological sensors focusing on cardiovascular and respiratory measures. The review highlights the type of study, hardware, method of analysis, test variable, and results of studies that have used physiological indices for MWL analysis in the automotive context.",

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AU - Birrell, Stewart

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KW - HR

KW - HRV

KW - conditional automation

KW - mental workload

KW - respiration

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A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this