Physiological signal analysis for fatigue level of experienced and inexperienced drivers

Li, Rui; Su, Wencheng; Lu, Zhangping

doi:10.1080/15389588.2016.1227073

Cited by 21 publications

(12 citation statements)

References 37 publications

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“…The vehicle seat and wheel positions were adjusted to ensure that the drivers were in their most comfortable driving positions. All experimental equipment, including the EEG, car music player and eye tracker, was adjusted accordingly.Procedure 2: Each driver first drove for 80 min without music (Li et al., 2017) and then drove for 60 min while listening to a selected music tempo at a volume of 65 dB (or in silence for the no-music condition). The driver’s body data started recording at the 75-min mark and then were tracked every 5 min until the end of the experiment.…”

Section: Methodsmentioning

confidence: 99%

“…To eliminate the impact of the music sequence, we used a Latin square sampling method to differently order the music tempos for each driver, aiming to examine how music affects fatigue during prolonged driving. Because the safe limit for monotonous highway driving is 80 min (Li, Su, & Lu, 2017), each driver was asked to drive for 80 min without music, thereby reaching a certain level of tiredness; then, they listened to the music of a certain tempo (or continued in silence for the no-music condition) for 60 min. The driver's physiological indices were continuously recorded.…”

Section: Experimental Conditionsmentioning

confidence: 99%

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Effect of Music Tempo on Long-Distance Driving: Which Tempo Is the Most Effective at Reducing Fatigue?

Chen

Zhang

2019

i-Perception

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This study investigated how music tempo impacted drivers’ fatigue and quality of attention in a long-distance monotonous highway environment. Sixteen drivers were enrolled in four sessions of real-road driving tests under the following four music conditions: no music, slow tempo, medium tempo and fast tempo. Specifically, the drivers’ electroencephalogram parameters and eye movement parameters were recorded to measure their extent of fatigue and quality of attention, respectively. Of the three tempos, medium-tempo music is the best choice to reduce fatigue and maintain attention for a long-distance driving. Slow-tempo music can temporarily boost the quality of attention, but after a long period of driving, it significantly deteriorates the driver’s levels of fatigue and attention. Fast-tempo music helps relieve driver fatigue but significantly deteriorates drivers’ attention after an extended driving time. This study offered practical references for drivers regarding the use of music to avoid fatigue, maintain attention and improve their driving safety. Based on previous theories of music and driving, we have explored the underlying mechanism of how music tempo maintains the alertness of drivers.

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Section: Methodsmentioning

confidence: 99%

Section: Experimental Conditionsmentioning

confidence: 99%

Effect of Music Tempo on Long-Distance Driving: Which Tempo Is the Most Effective at Reducing Fatigue?

Chen

Zhang

2019

i-Perception

Self Cite

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“…To address those problems, recent works have proposed to employ information obtained from the driver (Paul et al, 2016 ). For example, driver's face images has been used to recognize distractions (Sigari et al, 2013 ; Fernández et al, 2016 ) or drowsiness (Liu and Salvucci, 2001 ) while physiological activity has been employed to detect fatigue (Li et al, 2017a ) or drowsiness (Sahayadhas et al, 2012 ; lan Chen et al, 2015 ). The final aim in these approaches is to detect potential danger situations originated by human errors while driving (Janssen, 2001 ).…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Detection of Braking Intention Under Different Car Driving Conditions

Hernández

Mozos

Vicente

et al. 2018

Front. Neuroinform.

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The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents.

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“…In the case of EEG signals, the undesired artifacts such as eye blinking were reduced by using independent component analysis (ICA) in the EEGLAB software. The subjective fatigue level of participants was investigated before and after the real driving experiments by the Stanford Sleepiness Scale (SSS) [21] (1-2 = conscious, 3-4 = slight fatigue, 5 = medium fatigue, 6-7 = severe fatigue). The facial video was recorded using a video camera installed in front of the participants.…”

Section: Task and Eeg Datamentioning

confidence: 99%

Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram

Zhang

Chen

et al. 2021

Applied Sciences

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Safe driving plays a crucial role in public health, and driver fatigue causes a large proportion of crashes in road driving. Hence, this paper presents the development of an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals. The complexity of the EEG signal is then quantified with the sample entropy method. Finally, we explore the performance of multiple kernel-based algorithms based on sample entropy features for classifying fatigue and normal subjects by only analyzing noninvasive scalp EEG signals. Experimental results show that the highest classification accuracy of 97.2%, a sensitivity of 95.6%, a specificity of 98.9%, a precision of 98.9%, and the highest AUC value of 1 are achieved using SampEn feature and cubic SVM classifier (SCS Model). It is hence concluded that SampEn is an effectively distinguishing feature for classifying normal and fatigue EEG signals. The proposed system may provide us with a new and promising approach to monitoring and detecting driver fatigue at a relatively low computational cost.

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Physiological signal analysis for fatigue level of experienced and inexperienced drivers

Cited by 21 publications

References 37 publications

Effect of Music Tempo on Long-Distance Driving: Which Tempo Is the Most Effective at Reducing Fatigue?

Effect of Music Tempo on Long-Distance Driving: Which Tempo Is the Most Effective at Reducing Fatigue?

EEG-Based Detection of Braking Intention Under Different Car Driving Conditions

Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram

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