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Volume 2021 |Article ID 6640527 | https://doi.org/10.1155/2021/6640527

Yongqing Guo, Xiaoyuan Wang, Qing Xu, Quan Yuan, Chenglin Bai, Xuegang (Jeff) Ban, "Analysis of Differences in ECG Characteristics for Different Types of Drivers under Anxiety", Advances in Civil Engineering, vol. 2021, Article ID 6640527, 14 pages, 2021. https://doi.org/10.1155/2021/6640527

Analysis of Differences in ECG Characteristics for Different Types of Drivers under Anxiety

Academic Editor: Hui Yao
Received23 Dec 2020
Accepted07 Aug 2021
Published24 Aug 2021

Abstract

Anxiety is a complex emotion characterized by an unpleasant feeling of tension when people anticipate a threat or negative consequence. It is regarded as a comprehensive reflection of human thought processes, physiological arousal, and external stimuli. The actual state of emotion can be represented objectively by human physiological signals. This study aims to analyze the differences of ECG (electrocardiogram) characteristics for various types of drivers under anxiety. We used several methods to induce drivers’ mood states (calm and anxiety) and then conducted the real and virtual driving experiments to collect driver’s ECG signal data. Physiological changes in ECG during the experiments were recorded using the PSYLAB software. The independent sample t-test analysis was conducted to determine if there are significant differences in ECG characteristics for different types of drivers in anxious state during driving. The results show that there are significant differences in ECG signal characteristics of drivers by gender, age, and driving experience, in time domain, frequency domain, and waveform under anxiety. Our findings of this study contribute to the development of more intelligent and personalized driver warning system, which could improve road traffic safety.

1. Introduction

According to the statistics, more than 90% of traffic accidents are caused at least in part by human mistakes [1, 2], of which many errors result from drivers’ negative emotional motivations such as anxiety, anger, and contempt. Driver’s emotion as a psychological response has a substantial effect on the cognitive processes, including driver’s perception, judgment, action, and behavior. Therefore, it is of great significance to identify driver’s psychological and physiological characteristics in emotional states, in order to create safe and efficient driving.

Human emotions have a huge impact on how we live. The choices we make and the actions we take are influenced by the different types of emotions that we experience. There have been numerous studies to investigate the complex interactions between human emotion and physiological response in social, cultural, and economic fields, including household income [3], cultural diversity [4], physical health [5, 6], purchasing consumption [7, 8], Internet application [9], and environmental impact [1012]. For example, Jaeger et al. [10] found that anxious emotion makes people want to eat more spicy snacks and single snack intake compared to their calm state. The study by Zhang et al. [7] and Wang et al. [8] suggested that high brightness evokes people’s positive emotions and low brightness evokes people’s negative emotions.

In the transportation field, researchers and scholars have conducted the studies of the correlation between emotional state and driving behavior and explored the influence of human-vehicle-environment factors toward driver’ mood [1315]. For example, while driving, driver’s anxious emotion is more likely to be induced by environmental factors such as noisy and high arousal sound [14], low road visibility [16], and driver’ factors such as less driving experience [17].

Emotional states are combinations of psychological arousal and physiological response. Human emotions result in physical and physiological changes that influence behavior through autonomic nervous responses, such as electrocardiogram [18, 19], respiration frequency [20, 21], pulse [22], skin electricity [23, 24], electromyogram [25], and skin temperature [26]. Existing research focuses on the impact of human emotion on the ECG signal properties. For example, a study by Ba et al. showed that emotion is correlated with skin resistance, heart rate, and breathing rate [27]. Takahashi et al. [28] found that the heartbeat interval becomes shorter and the ratio of low frequency band to high frequency band becomes higher in anger than in calm. Herrero-Fernández [29] found that the QT interval variability of ECG waveform is positively associated with the level of anger, and the RR interval variability of ECG waveform is negatively associated with the level of anger.

The detection and warning systems for traffic safety based on drivers’ ECG signals have received increasing attention. Isikli Esener [30] recognized drivers’ distress level using subspace-based feature extraction on ECG signals and other physiological measurements. Balasubramanian and Bhardwaj [31] used a noncontact ECG measurement approach to determine the fatigue levels of drivers. Zhao et al. [32] measured drivers’ mental fatigue according to their ECG signals. Gromer et al. [33] applied a low-cost ECG sensor to detect drivers’ drowsiness, by extracting the main ECG parameters including heart rate, QRS-complex, and heart rate variability. Taherisadr et al. [34] proposed an ECG-based driver distraction detection system using convolutional neural networks.

In conclusion, there have been few attempts in the past to analyze the influence of driver’s emotions on their behavioral based on physiological signals. Hence, it is essential for transportation researchers to identify driver’s ECG characteristics in emotional states to gain a deep understanding of how driver’s emotions affect their behavior and reactions. This study focuses on examining the differences of ECG characteristics for various types of drivers in anxious state during driving.

2. Research Method

2.1. Participants

This study included 27 male drivers and 21 female drivers (age range: 22–50 y; mean age: 33.4 y). Participants were classified into three groups according to their driving propensities, which were determined by the propensity questionnaire [1]. The three groups were extraversion, middle type, and introversion, respectively. In this study, if drivers drove less than 10,000 kilometers, they would be considered as novice drivers, experienced drivers otherwise. Participants drove approximately 14,000 km miles on average. Prior to the experiment, they were told not to take any drugs that affect the brain and nervous system within one week and not to have tea, coffee, and wine that affect the mood and mental state within 48 hours. Moreover, they were asked to avoid any vigorous and high-intensity workout. Researchers provided a detailed description of the experimental design to the participants. Summary information of participants is presented in Table 1.


Number of drivers2820
GenderMale; femaleMale; female
AgeYouth (22–27 y)Middle age(45–50 y)
Driving experienceNovice (driving mileage ≤ 10,000 km)
Experienced (driving mileage > 10,000 km)
Novice (driving mileage ≤ 10,000 km)
Experienced (driving mileage > 10,000 km)
Driving tendencyT1 (extraversion); T2 (middle type); T3 (introversion)T1 (extraversion); T2 (middle type); T3 (introversion)

3. Experimental Material and Equipment

3.1. Emotional Induction Materials

The materials used for emotional induction in this study were primarily obtained from the International Affective Picture System (IAPS) and the Chinese Affective Picture System (CAPS). The two databases were designed for the experimental study of emotions, by providing a set of standardized emotional stimuli according to three dimensions: pleasure, arousal, and dominance. Different types of emotion-inducing materials were applied, including audio, visual olfactory, and taste materials. Moreover, participants were also asked to carry out difficult assignment with stress, in order to induce their anxious emotion. Parts of the anxiety induction material are shown in Figure 1.

3.2. Real Driving Experiment

The experimental route consists of a single loop, including two long sides with a length of 1.613 km (between Beijing Road and Nanjing Road) and two short sides with a length of 0.623 km (between Qingnian Road and Xincun West Road, as shown in Figure 2). The experimental equipment mainly includes two experimental vehicles, laser radar, laser ranging sensor, high-precision global positioning system, noncontact multifunction speedometer, vehicle recorder, PSYLAB human factor engineering equipment, pedal force manometer, high-definition camera, laptop, and unmanned aerial vehicle (Figure 3). The unmanned aerial vehicle was used for recording the experimental process. Screenshots of real experimental scenes (in Xincun West Road) are illustrated in Figure 4.

3.3. Virtual Driving Experiment

In the virtual driving experiments, a high-fidelity simulator from Japanese manufacturer FORUM 8 was used, which allowed users to construct 3D traffic environment. The Road Builder and UC-win/Road software were used in the driving simulator to build an experimentation platform of the road system with human, vehicle, and road components (Figure 5). The driving simulator was able to collect data on interactions between drivers and vehicles under various traffic conditions. It enables researchers to collect details of useful parameters for drivers’ action and vehicle performance, including distance traveled, offset from lane center, speed, acceleration, deceleration, braking, lane-changing, and steering angle. The driving simulator can reproduce the road situations similar to reality. Under the virtual environment, the subject can drive just like on the ordinary road, with the same responses as real-life driving. The wearable wireless ECG sensors are shown in Figure 6. The simulation-based experiment route and street view are shown in Figure 7.

3.4. Experimental Process

The real driving experiments in anxiety were carried out during morning peak hours of 7 : 00–9:00 and evening peak hours of 17 : 00–19 : 00 from Monday to Friday. The experimental process is shown in Figure 8.

3.5. Assessing the Level of the Induced Anxiety

Participant’s level of anxiety was detected, based on Beck Anxiety Inventory (BAI), self-perception, facial expression, and behavioral action. The BAI reflects the intensity of physical and cognitive anxiety (Table 2). In this study, the emotion-induction experiments end when subjects obtained a score of 26 points or more. During the driving experiment, experimenters irregularly communicate to the subjects to get their emotional states. After the driving experiment, subjects were asked to watch the recorded video to report their emotional experience during driving. For more details about the process of evaluating driver’s anxiety level, please refer to our another article by Guo et al. [35].


(1) Body numbness or thorns
(2) Feel feverish
(3) Leg tremble
(4) Cannot relax
(5) Fear of bad things
(6) Feel dizzy
(7) Palpitation
(8) Restless
(9) Frightened
(10) Tension
(11) Suffocation
(12) Hand trembling
(13) Body shake
(14) Afraid of out of control
(15) Difficult breathing
(16) Fear to die
(17) Feel panic
(18) Abdominal discomfort
(19) Faint
(20) Flush
(21) Sweat

Note. The 21 symptoms have four levels of induction. The score of each symptom can be expressed as “1 point-none;” “2 points-mild, no major annoyance;” “3 points-moderate, feel uncomfortable but still tolerable;” “4 points-heavy, can only barely endure.” The total score of 21 symptoms is 15–25 points for mild anxiety, 26–35 points for moderate anxiety, and more than 36 points for severe anxiety.

4. ECG Signal Data Collection and Preprocessing

4.1. ECG Signal Data Preprocessing

The raw ECG signals contain motion artifact, power frequency interference, and sensor internal interference noise. The PSYLAB software was used for reducing the noise in the ECG signal, as shown in Figure 9. The definitions of the parameters for denoising preprocess are given in Table 3. The comparison of ECG signal before and after denoising is shown in Figure 10. It was seen that after noise reduction, the noise can be controlled to an acceptable level. For more details about the ECG signal preprocessing, please refer to another article by Wang et al. [36].


White-denoiseBaseline-denoiseLowpass-denoiseBand stop

Remove white noise from ECG signalsHigh frequency signal is retained and low frequency signal is cut offLow frequency signal is retained and high frequency signal is cut offRemove power frequency interference

4.2. ECG Signal Data Collection

Each subject was involved in driving experiments multiple times. A total of 3849 groups of effective data were obtained, including 983 clusters from the real driving experiments and 2866 clusters from the driving simulators. The variables and symbols in the experiment are given in Table 4. Parts of the experimental data are given in Figure 11 and Table 5.


VariableSymbol

GenderG
Age (year)A
Driving experience (ten thousand kilometers)D
Driving tendencyT
EmotionEm
R wave average peak (uV)RWAVE
T wave average peak (uV)TWAVE
Q wave average peak absolute value (uV)Q
S wave average peak absolute value (uV)S
Average heart rate (bpm)AVHR
Atrioventricular interval (ms)AVNN
Standard deviation of NN intervals for period of interest (ms)SDNN
Percent of NN intervals>50 ms (%)PNN50
Root mean square of successive (ms)RMSSD
Ratio of ultralow frequency band to very low frequency bandUVLF/VLF
Ratio of low frequency band to high frequency bandLF/HF
Total power (ms2)TP


No.GDEmAVHRAVNNSDNNPNN50RMSSDRWAVE

1Male0.4Anxiety95632.60129.3615.56158.322559.27
ATTWAVEQSUVLF/VLFLF/HFTP
22Extraversion392.78−431.67−1554.490.071.072641.22

No.GDEmAVHRAVNNSDNNPNN50RMSSDRWAVE
2Male0.50Anxiety88680.9657.7911.9032.102559.17
ATTWAVEQSUVLF/VLFLF/HFTP
27Middle type363.74−469.37−1513.370.139.561123.26

No.GDEmAVHRAVNNSDNNPNN50RMSSDRWAVE
3Female0.30Anxiety102586.6545.395.7012.912234.62
ATTWAVEQSUVLF/VLFLF/HFTP
24Extraversion360.99−399.78−1260.28012.89749.11


No.GDEmAVHRAVNNSDNNPNN50RMSSDRWAVE
n − 1Female1.30Anxiety84713.3731.372.5027.411171.33
ATTWAVEQSUVLF/VLFLF/HFTP
50Introversion110.52−209.44−685.180.017.18565.47

No.GDEmAVHRAVNNSDNNPNN50RMSSDRWAVE
nMale3.80Anxiety81743.24161.1247.37232.411920.80
ATTWAVEQSUVLF/VLFLF/HFTP
50Introversion255.69−235.46−880.280.061.735497.79

5. Results and Discussion

5.1. Driver’s ECG Characteristics by Gender

Statistical analysis was performed using SPSS Statistics 23.0 where the confidence interval was set at 95%. The independent t-test was used to determine whether there are differences in ECG indicators between female and male drivers, and the results are given in Table 6.


tdfSignificance (2-tailed)Mean differenceStandard error difference95% confidence interval of the difference
LowerUpper

AVHRM-F−4.19680.003−2.7780.662−4.304−1.251
AVNNM-F5.21880.00122.1354.24212.35331.918
SDNNM-F0.980.39417.70319.671−27.65863.064
PNN50M-F2.29180.05110.8414.732−0.71821.754
RMSSDM-F1.71380.12553.67531.333−18.579125.93
RWAVEM-F4.19780.003341.2481.302153.761528.726
TWAVEM-F9.60180.000109.611.41683.281135.932
QM-F−1.69880.128−28.316.672−66.75310.137
SM-F−15.11880.000−219.314.509−252.808−185.892
UVLF/VLFM-F−0.36480.725−0.0090.024−0.0650.047
LF/HFM-F0.03480.9730.1043.035−6.8947.104
TPM-F0.8880.404298.59339.158−483.5011080.699

Note. The significance level is 0.05. M-F, male-female.

The results show that there are significant differences between male and female drivers in the five ECG indicators: , , , , and ( < 0.05). No significant difference was found between male and female drivers in the other indicators. The results in Figure 12 and Table 6 show that female drivers have higher average heart rate and S-point peaks and lower average heartbeat, R wave peaks, and T-point peaks than male drivers. The results indicate that compared to male drivers, female drivers tend to have a faster heart rate, a shorter heartbeat interval, and a more obvious manifestation of myocardial ischemia in anxiety while driving.

Under moderate and high levels of anxiety, female drivers are more likely to experience dizziness, slow response, and fidgeting due to rapid heartbeat and poor blood flow to the heart. Moreover, females are more likely to have chest distress, shortness of breath, as well as discomfort in the arms, neck, and shoulders as with myocardial ischemia. These symptoms might contribute to distraction, difficulty keeping the eyes from focusing, and slow reaction during driving. The results in Figure 12 suggest that these gender differences in the symptoms may be more pronounced in middle-aged drivers than in young ones, especially for novice and introverted drivers.

5.2. Driver’s ECG Characteristics by Age

The independent t-test results (in Table 7) show that there are significant differences between middle-aged and young drivers in the seven ECG indicators AVHR, , , , , , and ( < 0.05). There is no significant difference between middle-aged and young drivers in the other indicators. The results in Figure 13 and Table 7 show that young drivers have higher average heart rate, R wave peaks, and T-point peaks than middle-aged drivers. Furthermore, young drivers have lower average heartbeat interval, Q-point peaks, S-point peaks, and the ratio of ultralow frequency band to very low frequency band than middle-aged drivers. The results indicate that compared to middle-aged drivers, young drivers tend to have a faster heart rate, a shorter heartbeat interval, a higher pulse pressure, a greater sympathetic nerve activity, and a higher rate of left ventricular hypertrophy and hyperkalemia in anxiety.


tdfSignificane (2-tailed)Mean differenceStandard error difference95% confidence interval of the difference
LowerUpper

AVHRM-Y−5.35850.003−8.51.586−12.578−4.422
AVNNM-Y5.60450.00269.22512.35237.473100.977
SDNNM-Y−0.46150.664−13.629.487−89.40362.193
PNN50M-Y2.08250.09210.4885.038−2.46223.439
RMSSDM-Y−0.55350.604−10.8419.621−61.28639.589
RWAVEM-Y−5.90750.002−715.7121.187−1027.313−404.273
TWAVEM-Y−3.81250.012−71.0818.646−119.01−23.15
QM-Y7.60450.001171.0522.495113.231228.882
SM-Y41.94450.000545.8713.015512.423579.333
UVLF/VLFM-Y6.2950.0010.0620.010.0360.087
LF/HFM-Y−1.65150.16−3.9372.384−10.0642.191
TPM-Y−0.64550.547−367.1568.955−1829.71095.386

Note. The significance level is 0.05. M-Y, middle aged-young.

In moderate and severe cases, young drivers are more likely to feel dizziness and chest distress due to rapid heartbeat and poor blood flow to the heart. Young drivers are also more likely to suffer from muscle stiffness as with hyperkalemia. These symptoms might contribute to slow response and maintain head-down position (vision at low location). As a result, young drivers might pay less attention on traffic environment of the sides and the straight ahead in the far while driving. These age differences in the symptoms are more obvious in female drivers than in male ones. Moreover, it should be noted that high levels of sympathetic nerve activity, left ventricular hypertrophy, and pulse pressure occur rarely in young individuals during driving.

5.3. Driver’s ECG Characteristics by Driving Experiences

The independent t-test results (Table 8) show that there are significant differences between novice and experienced drivers in the four ECG indicators, AVHR, AVNN, RWAVE, and S (p < 0.05). No significant difference was found between novice and experienced drivers in the other indicators. The results in Figure 14 and Table 8 show that novice drivers have higher average heart rate and R-wave peaks and lower average heartbeat interval and S-point peaks than experienced drivers. The results indicate that compared to experienced drivers, novice drivers tend to have a faster heart rate, a shorter heartbeat interval, and an aberrant ventricular conduction in anxiety.


tdfSignificance (2-tailed)Mean differenceStandard error difference95% confidence interval of the difference
LowerUpper

AVHRN-E2.92850.0332.0000.6830.2443.756
AVNNN-E−2.53550.042−18.5107.304−37.2890.259
SDNNN-E0.82150.4497.6209.28−16.23431.474
PNN50N-E−0.22150.834−1.0104.577−12.77510.755
RMSSDN-E−0.77750.472−19.73025.394−85.01145.545
RWAVEN-E3.41450.01998.01528.70624.223171.807
TWAVEN-E2.32350.06829.49012.693−3.13962.119
QN-E−1.01150.358−7.5277.446−26.66711.613
SN-E−2.81450.037−34.02012.093−65.113−2.941
UVLF/VLFN-E0.98950.3680.0300.03−0.0480.108
LF/HFN-E−1.51050.192−6.4534.275−17.4434.536
TPN-E−1.01050.359−428.700424.638−1520.36662.77

Note. The significance level is 0.05. N-E, novice-experienced.

In moderate and severe cases, novices are more likely to experience sweating and nervous intense due to rapid heartbeat. Novices are also more likely to suffer from shortness of breath as with aberrant ventricular conduction. These symptoms might cause long fixation duration and behavioral inflexibility to react to sudden events during driving.

6. Conclusion

This study identified the differences of ECG characteristics for different types of drivers under anxiety. The real and virtual driving experiments were designed and conducted to collect driver ECG signal data. The data were analyzed by gender, age, and driving experience. The main findings are demonstrated as follows.(1)Compared to male drivers, female drivers tend to have a faster heart rate, a shorter heartbeat interval, and a more obvious manifestation of myocardial ischemia in anxiety. Under moderate and high levels of anxiety, female drivers are more likely to experience dizziness, slow response, and fidgeting due to rapid heartbeat. Moreover, females are more likely to have chest distress, shortness of breath, as well as discomfort in the arms, neck, and shoulders as with myocardial ischemia.(2)Compared to middle-aged drivers, young drivers tend to have a faster heart rate, a shorter heartbeat interval, a higher pulse pressure, a greater sympathetic nerve activity, and a higher rate of left ventricular hypertrophy and hyperkalemia in anxiety. In moderate and severe cases, young drivers are more likely to feel dizziness and chest distress due to rapid heartbeat. Young drivers are also more likely to suffer from muscle stiffness as with hyperkalemia.(3)Compared to experienced drivers, novice drivers tend to have a faster heart rate, a shorter heartbeat interval, and an aberrant ventricular conduction in anxiety. In moderate and severe cases, novices are more likely to experience sweating and nervous intense due to rapid heartbeat. Novices are also more likely to suffer from shortness of breath as with aberrant ventricular conduction.

Our findings of this study suggest that ECG signals closely reflect driver’s emotional state and can be used to detect driver’s physical state. The findings also contribute to the development of the intelligent and personalized driver warning system, which could improve road traffic safety. Further studies are required to gather additional ECG data for different types of drivers and determine the factors affecting the ECG characteristics in emotional states.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

This study was supported by the Joint Laboratory for Internet of Vehicles, Ministry of Education–China Mobile Communications Corporation (ICV-KF2018-03), Qingdao Top Talent Program of Entrepreneurship and Innovation (19-3-2-8-zhc), the National Natural Science Foundation of China (71901134, 61074140, and 61573009), and the National Key R&D Program of China (2017YFC0803802).

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Copyright © 2021 Yongqing Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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