Literature DB >> 23796506

Look before you (s)leep: evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry.

Drew Dawson1, Amelia K Searle2, Jessica L Paterson3.   

Abstract

Fatigue is a significant risk factor in workplace accidents and fatalities. Several technologies have been developed for organisations seeking to identify and reduce fatigue-related risk. These devices purportedly monitor behavioural correlates of fatigue and/or task performance and are understandably appealing as a visible risk control. This paper critically reviews evidence supporting fatigue detection technologies and identifies criteria for assessing evidence supporting these technologies. Fatigue detection devices, and relevant reliability and validation data, were identified by systematically searching the scientific, grey and marketing literature. Identified devices typically assessed correlates of fatigue using either psychophysiological measures or embedded performance measures drawn from the equipment being operated. Critically, the majority of the 'validation' data were not found within the scientific peer-reviewed literature, but within the quasi-scientific, grey or marketing literature. Based on the validation evidence available, none of the current technologies met all the proposed regulatory criteria for a legally and scientifically defensible device. Further, none were sufficiently well validated to provide a comprehensive solution to managing fatigue-related risk at the individual level in real time. Nevertheless, several of the technologies may be considered a potentially useful element of a broader fatigue risk management system. To aid organisations and regulators contemplating their use, we propose a set of evaluative and operational criteria that would likely meet the legal requirements for exercising due diligence in the selection and use of these technologies in workplace settings. Crown
Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Keywords:  FRMS; Fatigue; Fatigue detection; Fatigue risk management systems

Mesh:

Year:  2013        PMID: 23796506     DOI: 10.1016/j.smrv.2013.03.003

Source DB:  PubMed          Journal:  Sleep Med Rev        ISSN: 1087-0792            Impact factor:   11.609


  12 in total

1.  Eye-Blink Parameters Detect On-Road Track-Driving Impairment Following Severe Sleep Deprivation.

Authors:  Shamsi Shekari Soleimanloo; Vanessa E Wilkinson; Jennifer M Cori; Justine Westlake; Bronwyn Stevens; Luke A Downey; Brook A Shiferaw; Shantha M W Rajaratnam; Mark E Howard
Journal:  J Clin Sleep Med       Date:  2019-09-15       Impact factor: 4.062

2.  Prolonged Eyelid Closure Episodes during Sleep Deprivation in Professional Drivers.

Authors:  Pasquale K Alvaro; Melinda L Jackson; David J Berlowitz; Philip Swann; Mark E Howard
Journal:  J Clin Sleep Med       Date:  2016-08-15       Impact factor: 4.062

3.  Effects of driving time on microsaccadic dynamics.

Authors:  Leandro L Di Stasi; Michael B McCamy; Sebastian Pannasch; Rebekka Renner; Andrés Catena; José J Cañas; Boris M Velichkovsky; Susana Martinez-Conde
Journal:  Exp Brain Res       Date:  2014-11-23       Impact factor: 1.972

4.  Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses.

Authors:  Omid Dehzangi; Vikas Rajendra; Mojtaba Taherisadr
Journal:  Sensors (Basel)       Date:  2018-02-07       Impact factor: 3.576

Review 5.  Is it time to turn our attention toward central mechanisms for post-exertional recovery strategies and performance?

Authors:  Ben Rattray; Christos Argus; Kristy Martin; Joseph Northey; Matthew Driller
Journal:  Front Physiol       Date:  2015-03-17       Impact factor: 4.566

6.  Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals.

Authors:  Jinghai Yin; Jianfeng Hu; Zhendong Mu
Journal:  Healthc Technol Lett       Date:  2016-10-20

7.  Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

Authors:  Xiaoliang Zhang; Jiali Li; Yugang Liu; Zutao Zhang; Zhuojun Wang; Dianyuan Luo; Xiang Zhou; Miankuan Zhu; Waleed Salman; Guangdi Hu; Chunbai Wang
Journal:  Sensors (Basel)       Date:  2017-03-01       Impact factor: 3.576

8.  Development of the Fatigue Risk Assessment and Management in High-Risk Environments (FRAME) Survey: A Participatory Approach.

Authors:  Ashley E Shortz; Ranjana K Mehta; S Camille Peres; Mark E Benden; Qi Zheng
Journal:  Int J Environ Res Public Health       Date:  2019-02-13       Impact factor: 3.390

Review 9.  Fatigue management in the workplace.

Authors:  Khosro Sadeghniiat-Haghighi; Zohreh Yazdi
Journal:  Ind Psychiatry J       Date:  2015 Jan-Jun

Review 10.  Working Time Society consensus statements: A multi-level approach to managing occupational sleep-related fatigue.

Authors:  Imelda S Wong; Stephen Popkin; Simon Folkard
Journal:  Ind Health       Date:  2019-01-31       Impact factor: 2.179

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