survey

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

Authors:

Mohammad Naim RASTGOO,

Bahareh Nakisa,

Andry Rakotonirainy,

Vinod Chandran,

Dian TjondronegoroAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 5

Article No.: 88, Pages 1 - 35

https://doi.org/10.1145/3186585

Published: 04 September 2018 Publication History

Abstract

Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 5

September 2019

791 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3271482

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Published: 04 September 2018

Accepted: 01 February 2018

Revised: 01 December 2017

Received: 01 February 2017

Published in CSUR Volume 51, Issue 5

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