Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator

Mani, Mohan Prasath; Balasubramanian, Venkatesh

doi:10.1016/j.apergo.2014.02.001

Cited by 90 publications

(66 citation statements)

References 37 publications

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“…The measurement points were six frontal channels (F3, F4, F7, F8, AF3, and AF4) out of a total of 16 channels (as shown in Figure 2). This brain measurement followed previous studies (Otmani et al, 2005;Jagannath & Balasubramanian, 2014;Perrier et al, 2016), which stated that the frontal part of the brain controls cognitive skill and is affected by fatigue.…”

Section: Methodsmentioning

confidence: 99%

“…Electroencephalogram (EEG) has been accepted as the 'gold standard' of fatigue measurement in much of the prior literature (Jap et al, 2011;Jagannath & Balasubramanian, 2014;Perrier et al, 2016). Previous studies have suggested that prolonged driving duration had varied effects on EEG fluctuations, consisting of disappearance of the alpha band with increments in theta and delta (Cajochen et al, 1995), increments in the alpha and theta bands (Jagannath & Balasubramanian, 2014;Perrier et al, 2016), and a decrement in the beta band (Otmani et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have suggested that prolonged driving duration had varied effects on EEG fluctuations, consisting of disappearance of the alpha band with increments in theta and delta (Cajochen et al, 1995), increments in the alpha and theta bands (Jagannath & Balasubramanian, 2014;Perrier et al, 2016), and a decrement in the beta band (Otmani et al, 2005). Furthermore, Jap et al (2011) studied the effects of the monotony of train driving for 30 minutes and compared several parameters of EEG; θ/β, θ/(α+β), (θ+α)/β, and (θ+α)/(α+β).…”

Section: Introductionmentioning

confidence: 99%

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Effect Of Driving Duration On Eeg Fluctuations

Puspasari¹,

Iridiastadi

Sutalaksana

et al. 2017

IJTech

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Road accidents are a major issue in Indonesia, and their number increases every year. Based on previous studies, mental fatigue is one of the biggest factors leading to road accidents and is majorly affected by mental workload. Driving duration is one of the factors that triggers mental fatigue. The prior literature cites electroencephalogram (EEG) measurement as the gold standard for measuring fatigue. However, there has been only limited study to examine the EEG indicators that are affected by driving duration, and the prior research still contains disagreements regarding the best EEG parameter for use in measuring fatigue. Therefore, this study aimed to evaluate the effect of driving duration on EEG fluctuation and determine the best EEG parameter related to fatigue. Seven participants were asked to spend three hours driving in a medium-fidelity simulator. A one-way ANOVA and correlation analysis were performed to measure the effect of driving duration on the EEG indicators and determine the correlation of the indicators. A Receiver Operating Characteristics (ROC) curve was also utilized to determine the variable with the greatest correlation with the subjective sleepiness indices. The results showed that at the end of three hours' driving, there was an increment in delta and theta activities, followed by a decrement in alpha and beta activities. In addition, the correlation of all bands was significant, with positive results for the alpha-beta and theta-delta bands, and a negative result in relation to each other. Furthermore, the results from the ROC curve revealed the Relative Power Ratio (RPR) of theta, the RPR of alpha, and the ratio of θ/α+β to be the best indicators among others, demonstrating a high degree of accuracy (above 85%).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect Of Driving Duration On Eeg Fluctuations

Puspasari¹,

Iridiastadi

Sutalaksana

et al. 2017

IJTech

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show abstract

“…The awareness of the increased accident risk in the transport sector, has led to development and use of computerised mathematical process models, dual-models and 3-process models for predicting level of fatigue and its effect on cognitive functions, e.g. alertness/performance in daily life, for navy, airline and railway applications [9][10][11] as well as multimodal fatigue measure assessment for measuring early onset of drivers fatigue [12], but so far these have not been implemented in the fishing industry.…”

Section: Introductionmentioning

confidence: 99%

A review of fatigue in fishermen: a complicated and underprioritised area of research

Høvdanum¹,

Jensen²,

Pétursdóttir³

et al. 2014

International Maritime Health

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“…It does not easily decrease even if they breaks down and reduces concentration and work efficiency during driving. It is necessary to know the symptoms of the neck pain and to be active in self care [5].…”

Section: Introductionmentioning

confidence: 99%

Effects of Neck Exercise Program on a Taxi Driver's with Chronic Neck Pain

Bak¹

2017

IJANER

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Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator

Cited by 90 publications

References 37 publications

Effect Of Driving Duration On Eeg Fluctuations

Effect Of Driving Duration On Eeg Fluctuations

A review of fatigue in fishermen: a complicated and underprioritised area of research

Effects of Neck Exercise Program on a Taxi Driver's with Chronic Neck Pain

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