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
According to the statistics, more than 90% of traffic accidents are caused at least in part by human mistakes [
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 [
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 [
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 [
The detection and warning systems for traffic safety based on drivers’ ECG signals have received increasing attention. Isikli Esener [
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.
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 [
Statistics of drivers’ information.
Number of drivers | 28 | 20 |
Gender | Male; female | Male; female |
Age | Youth (22–27 y) | Middle age(45–50 y) |
Driving experience | Novice (driving mileage ≤ 10,000 km) | Novice (driving mileage ≤ 10,000 km) |
Driving tendency | T1 (extraversion); T2 (middle type); T3 (introversion) | T1 (extraversion); T2 (middle type); T3 (introversion) |
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
Parts of the anxiety induction material. (a) Anxiety induction visual materials. (b) Pictures of different people in anxiety.
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
Real driving experimental route.
Real driving experimental equipment.
Screenshots of experimental scenes (in Xincun West Road).
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
Virtual driving experimental equipment.
The wearable wireless ECG sensors.
The simulation-based experiment route and street view.
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
The experimental process of real driving in 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
Beck anxiety inventory (BAI).
(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 |
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
Denoising preprocess interface for original ECG signal.
The definitions of parameters for denoising preprocess.
White-denoise | Baseline-denoise | Lowpass-denoise | Band stop |
---|---|---|---|
Remove white noise from ECG signals | High frequency signal is retained and low frequency signal is cut off | Low frequency signal is retained and high frequency signal is cut off | Remove power frequency interference |
Comparison of ECG signal before and after denoising.
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
Variables and symbols in the experiment.
Variable | Symbol |
---|---|
Gender | G |
Age (year) | A |
Driving experience (ten thousand kilometers) | D |
Driving tendency | T |
Emotion | Em |
R wave average peak ( | RWAVE |
T wave average peak ( | TWAVE |
Q wave average peak absolute value ( | Q |
S wave average peak absolute value ( | 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 band | UVLF/VLF |
Ratio of low frequency band to high frequency band | LF/HF |
Total power (ms2) | TP |
Distribution of driver’s ECG data distribution in anxiety. (a) Driver’s heart rate and frequency distribution (male
Statistics of driver’s ECG characteristic data.
No. | G | D | Em | AVHR | AVNN | SDNN | PNN50 | RMSSD | RWAVE |
---|---|---|---|---|---|---|---|---|---|
1 | Male | 0.4 | Anxiety | 95 | 632.60 | 129.36 | 15.56 | 158.32 | 2559.27 |
A | T | TWAVE | Q | S | UVLF/VLF | LF/HF | TP | ||
22 | Extraversion | 392.78 | −431.67 | −1554.49 | 0.07 | 1.07 | 2641.22 | ||
No. | G | D | Em | AVHR | AVNN | SDNN | PNN50 | RMSSD | RWAVE |
2 | Male | 0.50 | Anxiety | 88 | 680.96 | 57.79 | 11.90 | 32.10 | 2559.17 |
A | T | TWAVE | Q | S | UVLF/VLF | LF/HF | TP | ||
27 | Middle type | 363.74 | −469.37 | −1513.37 | 0.13 | 9.56 | 1123.26 | ||
No. | G | D | Em | AVHR | AVNN | SDNN | PNN50 | RMSSD | RWAVE |
3 | Female | 0.30 | Anxiety | 102 | 586.65 | 45.39 | 5.70 | 12.91 | 2234.62 |
A | T | TWAVE | Q | S | UVLF/VLF | LF/HF | TP | ||
24 | Extraversion | 360.99 | −399.78 | −1260.28 | 0 | 12.89 | 749.11 | ||
… | … | … | … | ||||||
No. | G | D | Em | AVHR | AVNN | SDNN | PNN50 | RMSSD | RWAVE |
Female | 1.30 | Anxiety | 84 | 713.37 | 31.37 | 2.50 | 27.41 | 1171.33 | |
A | T | TWAVE | Q | S | UVLF/VLF | LF/HF | TP | ||
50 | Introversion | 110.52 | −209.44 | −685.18 | 0.01 | 7.18 | 565.47 | ||
No. | G | D | Em | AVHR | AVNN | SDNN | PNN50 | RMSSD | RWAVE |
n | Male | 3.80 | Anxiety | 81 | 743.24 | 161.12 | 47.37 | 232.41 | 1920.80 |
A | T | TWAVE | Q | S | UVLF/VLF | LF/HF | TP | ||
50 | Introversion | 255.69 | −235.46 | −880.28 | 0.06 | 1.73 | 5497.79 |
Statistical analysis was performed using SPSS Statistics 23.0 where the confidence interval was set at 95%. The independent
Independent
df | Significance (2-tailed) | Mean difference | Standard error difference | 95% confidence interval of the difference | ||||
---|---|---|---|---|---|---|---|---|
Lower | Upper | |||||||
AVHR | M-F | −4.196 | 8 | −2.778 | 0.662 | −4.304 | −1.251 | |
AVNN | M-F | 5.218 | 8 | 22.135 | 4.242 | 12.353 | 31.918 | |
SDNN | M-F | 0.9 | 8 | 0.394 | 17.703 | 19.671 | −27.658 | 63.064 |
PNN50 | M-F | 2.291 | 8 | 0.051 | 10.841 | 4.732 | −0.718 | 21.754 |
RMSSD | M-F | 1.713 | 8 | 0.125 | 53.675 | 31.333 | −18.579 | 125.93 |
RWAVE | M-F | 4.197 | 8 | 341.24 | 81.302 | 153.761 | 528.726 | |
TWAVE | M-F | 9.601 | 8 | 109.6 | 11.416 | 83.281 | 135.932 | |
Q | M-F | −1.698 | 8 | 0.128 | −28.3 | 16.672 | −66.753 | 10.137 |
S | M-F | −15.118 | 8 | −219.3 | 14.509 | −252.808 | −185.892 | |
UVLF/VLF | M-F | −0.364 | 8 | 0.725 | −0.009 | 0.024 | −0.065 | 0.047 |
LF/HF | M-F | 0.034 | 8 | 0.973 | 0.104 | 3.035 | −6.894 | 7.104 |
TP | M-F | 0.88 | 8 | 0.404 | 298.59 | 339.158 | −483.501 | 1080.699 |
The results show that there are significant differences between male and female drivers in the five ECG indicators:
Male and female drivers’ ECG characteristics in anxiety. (a) 45–50 years old
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
The independent
Independent
df | Significane (2-tailed) | Mean difference | Standard error difference | 95% confidence interval of the difference | ||||
---|---|---|---|---|---|---|---|---|
Lower | Upper | |||||||
AVHR | M-Y | −5.358 | 5 | −8.5 | 1.586 | −12.578 | −4.422 | |
AVNN | M-Y | 5.604 | 5 | 69.225 | 12.352 | 37.473 | 100.977 | |
SDNN | M-Y | −0.461 | 5 | 0.664 | −13.6 | 29.487 | −89.403 | 62.193 |
PNN50 | M-Y | 2.082 | 5 | 0.092 | 10.488 | 5.038 | −2.462 | 23.439 |
RMSSD | M-Y | −0.553 | 5 | 0.604 | −10.84 | 19.621 | −61.286 | 39.589 |
RWAVE | M-Y | −5.907 | 5 | −715.7 | 121.187 | −1027.313 | −404.273 | |
TWAVE | M-Y | −3.812 | 5 | −71.08 | 18.646 | −119.01 | −23.15 | |
Q | M-Y | 7.604 | 5 | 171.05 | 22.495 | 113.231 | 228.882 | |
S | M-Y | 41.944 | 5 | 545.87 | 13.015 | 512.423 | 579.333 | |
UVLF/VLF | M-Y | 6.29 | 5 | 0.062 | 0.01 | 0.036 | 0.087 | |
LF/HF | M-Y | −1.651 | 5 | 0.16 | −3.937 | 2.384 | −10.064 | 2.191 |
TP | M-Y | −0.645 | 5 | 0.547 | −367.1 | 568.955 | −1829.7 | 1095.386 |
Young and middle age drivers’ ECG characteristics in anxiety. (a) Female
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.
The independent
Independent
df | Significance (2-tailed) | Mean difference | Standard error difference | 95% confidence interval of the difference | ||||
---|---|---|---|---|---|---|---|---|
Lower | Upper | |||||||
AVHR | N-E | 2.928 | 5 | 2.000 | 0.683 | 0.244 | 3.756 | |
AVNN | N-E | −2.535 | 5 | −18.510 | 7.304 | −37.289 | 0.259 | |
SDNN | N-E | 0.821 | 5 | 0.449 | 7.620 | 9.28 | −16.234 | 31.474 |
PNN50 | N-E | −0.221 | 5 | 0.834 | −1.010 | 4.577 | −12.775 | 10.755 |
RMSSD | N-E | −0.777 | 5 | 0.472 | −19.730 | 25.394 | −85.011 | 45.545 |
RWAVE | N-E | 3.414 | 5 | 98.015 | 28.706 | 24.223 | 171.807 | |
TWAVE | N-E | 2.323 | 5 | 0.068 | 29.490 | 12.693 | −3.139 | 62.119 |
Q | N-E | −1.011 | 5 | 0.358 | −7.527 | 7.446 | −26.667 | 11.613 |
S | N-E | −2.814 | 5 | −34.020 | 12.093 | −65.113 | −2.941 | |
UVLF/VLF | N-E | 0.989 | 5 | 0.368 | 0.030 | 0.03 | −0.048 | 0.108 |
LF/HF | N-E | −1.510 | 5 | 0.192 | −6.453 | 4.275 | −17.443 | 4.536 |
TP | N-E | −1.010 | 5 | 0.359 | −428.700 | 424.638 | −1520.36 | 662.77 |
Novice and experienced drivers’ ECG characteristics in anxiety. (a) Female
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.
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. 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. 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. 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.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest.
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).