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A critical review of proactive detection of driver stress levels based on multimodal measurements
journal contribution
posted on 2018-08-01, 00:00 authored by M N Rastgoo, Bahareh NakisaBahareh Nakisa, A Rakotonirainy, V Chandran, D Tjondronegoro© 2018 Association for Computing Machinery. 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.
History
Journal
ACM Computing SurveysVolume
51Issue
5Article number
88Pagination
1 - 35Publisher
Association for Computing MachineryLocation
New York, N.Y.Publisher DOI
ISSN
0360-0300eISSN
1557-7341Language
engPublication classification
C1.1 Refereed article in a scholarly journalUsage metrics
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No categories selectedKeywords
CCS-Concepts: Human-centered computingUbiquitous and mobile computingSurveys and overviewsComputing methodologiesMachine learning algorithmsDriver stress level detectionmultimodalityphysiological signalsECGEDArespirationphysical signalsvehicle dynamic datacontextual datamachine learningreal-time stress recognition systemScience & TechnologyTechnologyComputer Science, Theory & MethodsComputer ScienceHEART-RATE-VARIABILITYPSYCHOPHYSIOLOGICAL STRESSTRAFFIC CONGESTIONFEATURE-SELECTIONNEURAL-NETWORKPERFORMANCECLASSIFICATIONRECOGNITIONPERSONALITYALGORITHMS
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