Investigation on driver stress utilizing ECG signals with on-board navigation systems in use

Yu, Ya Jun; Zhan, Yang; Oh, Beom-Seok; Yeo, Yong Kiang; Liu, Qinglai; Huang, Guang-Bin; Lin, Zhiping

doi:10.1109/icarcv.2016.7838780

Cited by 18 publications

(13 citation statements)

References 17 publications

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“…All included studies had an observational, nonrandomized design [15,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Data regarding participants enrolled in each study, investigated HRV parameters, clinical setting, and major findings were reported in Table 1 (Ref.…”

Section: Resultsmentioning

confidence: 99%

Accurate and early detection of sleepiness, fatigue and stress levels in drivers through Heart Rate Variability parameters: a systematic review

Burlacu

Brinza

Brezulianu

et al. 2021

Rev. Cardiovasc. Med.

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Sleepiness, fatigue, and stress in drivers are the leading causes of car crashes. In the late two decades, there is an endeavor to monitor vital signs, stress levels, and fatigue using adapted sensors supported by technological advances. To the best of our knowledge, this systematic review is the first to investigate the role of HRV measurement for sleepiness, fatigue, and stress level monitoring in car drivers. A search was performed in PubMed, Embase, and Cochrane databases using prespecified keywords. Studies were considered for inclusion if they reported original data regarding the association between different HRV measurements and drivers' sleepiness, fatigue, or stress levels. Of the retrieved 749 citations, 19 studies were finally included. The sensibility and specificity of HRV significantly varied across studies, respectively 47.1%-95% and 74.6%-98%. Accuracy was also different, ranging from 56.6% to 95%. Nevertheless, in real-world conditions, confounding factors could affect sympathovagal tone and HRV. Multiple HRV parameters measurement rather than one parameter approach seems to be the optimal strategy for evaluating the vigilance state in drivers that it would be possible to achieve a good performance. As all studies were observational, data should be confirmed in randomized controlled trials. In conclusion, HRV represents a potentially valuable marker for sleepiness, fatigue, and stress monitoring in car drivers. HRV measurements could be implemented in future clinical models and sensors to detect early sleepiness and fatigue and prevent car crashes. More studies with larger populations are needed to support this evidence.

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Section: Resultsmentioning

confidence: 99%

Accurate and early detection of sleepiness, fatigue and stress levels in drivers through Heart Rate Variability parameters: a systematic review

Burlacu

Brinza

Brezulianu

et al. 2021

Rev. Cardiovasc. Med.

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“…The in-car experiment is conducted to collect ECG data of subjects who is driving car when GPS navigation is in use. These data were collected in the preliminary study earlier [18], and re-used in this study. By analyzing the obtained data using the trained -NN classifier, the stress state induced due to the GPS navigation is studied.…”

Section: In-car Experimentsmentioning

confidence: 99%

“…A preliminary study to investigate driver's mental stress with GPS navigation in use by measuring and analyzing ECG signal and its deduced HRV features has been published in [18].…”

Section: Introductionmentioning

confidence: 99%

“…However, in most of these studies, the stress states of the drivers are obtained through questionnaire / self-assessment [4], [12], [16], video [13], stress rating based on task conditions [13], [14], [17], or determined by the statistical significance of the difference between the interested condition and the relaxed condition [18]. In these researches, especially in the investigation of drivers' stress when GPS navigation is in use, no ground truth was based.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Stress State for Drivers Under Different GPS Navigation Modes

et al. 2020

IEEE Access

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It is commonly known that Global Positioning System (GPS) can alleviate travelling difficulties of automobile drivers, and generally we hold the view that it reduces the driver's stress when they are in unfamiliar road conditions. In this research, an in-laboratory experiment and an in-car experiment are conducted to find out whether GPS instructions can reduce or may induce additional mental stress of drivers. Electrocardiography (ECG) signals are collected in the experiments and the extracted heart rate variability (HRV) features are used for analysis. Three binary classifiers, specifically Support Vector Machine,-Nearest Neighbor (k-NN) and Random Forest, are trained based on the data collected in the in-laboratory experiment, where the stress state is elicited by the Stroop color word Test. The k-NN classifier outperforms the other two classifiers, and thus is applied to the data collected in the in-car experiment, to identify drivers' stress state under different driving events, such as waiting for traffic lights, turning, under GPS instructions, and traffic conditions like overtaking, or changing lanes. During each event, whether the driver is in stress or relaxed state for each time instant is predicted based on the trained classifier. The percentages of time that the driver is in stress state for each type of events are computed. It shows that GPS instructions cause the second largest time-percentage of stress state, lower than that caused by the turning event, but higher than that caused by the events of waiting for traffic lights and other traffic conditions. INDEX TERMS Driver stress, GPS navigation, k-NN classifier, Stroop color word test, HRV features, ECG signal Jingbin Li received the B.Eng. degree in Information Engineering from the Southern

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“…Many use physical characteristics, e.g., facial or eye images [18,19]. In others, physiological characteristics are considered [20][21][22]. Among these, EDA can be used jointly with different signals [23,24].…”

Section: Introductionmentioning

confidence: 99%