Association of Drivers’ sleepiness with heart rate variability: A Pilot Study with Drivers on Real Roads

Abtahi, Farhad; Anund, Anna; Fors, Carina; Seoane, Fernando; Lindecrantz, Kaj

doi:10.1007/978-981-10-5122-7_38

Cited by 16 publications

(19 citation statements)

References 19 publications

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“…Compared with realworld driving, the subjects faced fewer events and less overall information to be processed. The reduced stimulation in the driving simulator could lead to performance decrements much earlier [30]. In our study, the subjects reported that they felt very drowsy and would fall in sleep if they kept operating the vehicle.…”

Section: Discussionmentioning

confidence: 61%

“…On the other hand, the individual factors may be another reason causing the controversial result. Abtahi [30] suggested individual factors as a main reason for controversial results while using ECG to detect driver's sleepiness. As can be seen in Table 1, the variances of (δ + θ)/(α + β), and especially (α + θ)/β, were large, thus there was no significant differences between alert and drowsy states.…”

Section: Discussionmentioning

confidence: 99%

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Determination of optimal electroencephalography recording locations for detecting drowsy driving

Zhang

Wang

Chen³

et al. 2018

IET Intelligent Transport Systems

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Determination of optimal electroencephalography recording locations for detecting drowsy driving

Zhang

Wang

Chen³

et al. 2018

IET Intelligent Transport Systems

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“…There is considerable individual variability in HRV and changes in HRV are different in every person [46]. To circumvent this issue, the use of personalised algorithms [36], [45] or individually normalized features [17] has been suggested.…”

Section: Discussionmentioning

confidence: 99%

Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions

Persson

Jonasson

Fredriksson

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Driver sleepiness is a contributing factor in many road fatalities. A long-standing goal in driver state research has therefore been to develop a robust sleepiness detection system. It has been suggested that various heart rate variability (HRV) metrics can be used for driver sleepiness classification. However, since heart rate is modulated not only by sleepiness but also by several other time-varying intra-individual factors such as posture, distress, boredom and relaxation, it is relevant to highlight not only the possibilities but also the difficulties involved in HRV-based driver sleepiness classification. This paper investigates the reliability of HRV as a standalone feature for driver sleepiness detection in a realistic setting. Data from three real-road driving studies were used, including 86 drivers in both alert and sleep-deprived conditions. Subjective ratings based on the Karolinska sleepiness scale (KSS) were used as ground truth when training four binary classifiers (k-nearest neighbours, support vector machine, AdaBoost, and random forest). The best performance was achieved with the random forest classifier with an accuracy of 85%. However, the accuracy dropped to 64% for three-class classification and to 44% for subject-independent, leave-one-participant-out classification. The worst results were obtained in the severely sleepy class. The results show that in realistic driving conditions, subject-independent sleepiness classification based on HRV is poor. The conclusion is that more work is needed to control for the many confounding factors that also influence HRV before it can be used as input to a driver sleepiness detection system.

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“…Tran et al [ 18 ] reported that a driver’s absolute power of the LF band ( P LF(abs) ), P LF(nu) and r LF/HF increased significantly and that the mean RR interval, M RR , and P HF(nu) decreased significantly. Abtahi et al [ 19 ] reported that the M RR , standard deviation of RR interval ( D RR ), number of successive RR interval pairs that differ by more than 50 ms ( N NN50 ), P LF(abs) , absolute power of the HF band ( P HF(abs) ) and absolute power of all three bands ( P tot(abs) ) increased significantly. These studies contribute to our understanding of the relationship between driving fatigue and ANS function and clarify the development of driving fatigue detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Sex Differences in Time-Domain and Frequency-Domain Heart Rate Variability Measures of Fatigued Drivers

Zeng

Wang

Chen

et al. 2020

IJERPH

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The effects of fatigue on a driver’s autonomic nervous system (ANS) were investigated through heart rate variability (HRV) measures considering the difference of sex. Electrocardiogram (ECG) data from 18 drivers were recorded during a simulator-based driving experiment. Thirteen short-term HRV measures were extracted through time-domain and frequency-domain methods. First, differences in HRV measures related to mental state (alert or fatigued) were analyzed in all subjects. Then, sex-specific changes between alert and fatigued states were investigated. Finally, sex differences between alert and fatigued states were compared. For all subjects, ten measures showed significant differences (Mann-Whitney U test, p < 0.01) between different mental states. In male and female drivers, eight and four measures, respectively, showed significant differences between different mental states. Six measures showed significant differences between males and females in an alert state, while ten measures showed significant sex differences in a fatigued state. In conclusion, fatigue impacts drivers’ ANS activity, and this impact differs by sex; more differences exist between male and female drivers’ ANS activity in a fatigued state than in an alert state.

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Association of Drivers’ sleepiness with heart rate variability: A Pilot Study with Drivers on Real Roads

Cited by 16 publications

References 19 publications

Determination of optimal electroencephalography recording locations for detecting drowsy driving

Determination of optimal electroencephalography recording locations for detecting drowsy driving

Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions

Sex Differences in Time-Domain and Frequency-Domain Heart Rate Variability Measures of Fatigued Drivers

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