Experiment design for Stress data collection while driving in a simulator

1877-0509 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems 10.1016/j.procs.2023.10.435 10.1016/j.procs.2023.10.435 1877-0509 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 00 (2022) 000–000 www.elsevier.com/locate/procedia 1877-0509 © 2022The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2023) Experiment design for Stress data collection while driving in a simulator. Wilhelm Daniel Scherza*, Ralf Seepolda, Juan Antonio Ortegab, aHTWG Konstanz –University of Applied Sciences, Alfred-Wachtel-Straße 8, Konstanz and DE-78462, Germany bUniversity of Seville , Avda. Reina Mercedes s/n, Seville and 41004, Spain Abstract The principal objective of this study is to investigate the impact of perceived stress on traffic and road safety. Therefore, we designed a study that allows the generation and collection of stress-relevant data. Drivers often experience stress due to their perception of lack of control during the driving process. This can lead to an increased likelihood of traffic accidents, driver errors, and traffic violations. To explore this phenomenon, we used the Stress Perceived Questionnaire (PSQ) to evaluate perceived stress levels during driving simulations and the EPQR questionnaire to determine the personality of the driver. With the presented study, participants can categorised based on their emotional stability and personality traits. Wearable devices were utilised to monitor each participant's instantaneous heart rate (HR) due to their non-intrusive and portable nature. The findings of this study deliver an overview of the link between stress and traffic and road safety. These findings can be utilised for future research and implementing strategies to reduce road accidents and promote traffic safety. © 2022The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International Keywords: Stress, PSQ, EPQR, Driving simulator 1. Introduction Stress is a topic of great importance and interest in modern society [1], and it has a high impact on security and safety while driving [2]. According to the World Health Organization, stress is even recognised as a disease [1] due to its presence in modern life [1]; it can worsen the cognitive capacity of drivers [2]. The German Federal Statistical Office (Destatis) reported in their Press release Nr. 177 from 22 April 2022: 2,569 for 2021, 2,719 for 2020 and * Corresponding author. Tel.: +49-7531-206-698. E-mail address: [email protected] Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 00 (2022) 000–000 www.elsevier.com/locate/procedia 1877-0509 © 2022The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2023) Experiment design for Stress data collection while driving in a simulator. Wilhelm Daniel Scherza*, Ralf Seepolda, Juan Antonio Ortegab, a HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Straße 8, Konstanz and DE-78462, Germany b University of Seville , Avda. Reina Mercedes s/n, Seville and 41004, Spain Abstract The principal objective of this study is to investigate the impact of perceived stress on traffic and road safety. Therefore, we designed a study that allows the generation and collection of stress-relevant data. Drivers often experience stress due to their perception of lack of control during the driving process. This can lead to an increased likelihood of traffic accidents, driver errors, and traffic violations. To explore this phenomenon, we used the Stress Perceived Questionnaire (PSQ) to evaluate perceived stress levels during driving simulations and the EPQR questionnaire to determine the personality of the driver. With the presented study, participants can categorised based on their emotional stability and personality traits. Wearable devices were utilised to monitor each participant's instantaneous heart rate (HR) due to their non-intrusive and portable nature. The findings of this study deliver an overview of the link between stress and traffic and road safety. These findings can be utilised for future research and implementing strategies to reduce road accidents and promote traffic safety. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International Keywords: Stress, PSQ, EPQR, Driving simulator 1. Introduction Stress is a topic of great importance and interest in modern society [1],and it has a high impact on security and safety while driving [2]. According to the World Health Organization, stress is even recognised as a disease [1] due to its presence in modern life [1]; it can worsen the cognitive capacity of drivers [2]. The German Federal Statistical Office (Destatis) reported in their Press release Nr. 177 from 22 April 2022: 2,569 for 2021, 2,719 for 2020 and * Corresponding author. Tel.: +49-7531-206-698. E-mail address: [email protected] 4382 Wilhelm Daniel Scherz et al. / Procedia Computer Science 225 (2023) 4381–4388 2Wilhelm Daniel Scherz/ Procedia Computer Science 00 (2023) 000–000 3,046 for 2019 fatalities due to traffic accidents, while according to the World Health Organization, approximately 1.3 million people worldwide die each year due to traffic accidents [2]. Some human factors that have an impact on traffic accidents are stress, health conditions, fatigue, and sleepiness [2, 3]. Stress plays a vital role in risk evaluation while driving, making it an essential factor to understand. Stress can be described as anything that affects the internal equilibrium that maintains our bodies working [4]. External stimuli, perceived thread and demands that can create stress are called stressors. In addition to external stressors, there are also internal stressors like worries that can trigger the responses of the body [5]. The response of the body to these stressors is called the stress response, as seen in Fig. 1. The stress response is an adaptation to the surrounding situation, but these responses, when they are too severe or long-lasting, have a major influence on health and can even lead to illness. Among the well-known negative effects of stress, i.e. on the cardiovascular system, there are [6, 7, 8] the worsening of the immune response [9], reduction of the ability to give an adequate response to physical and mental demands [10, 11, 12] and even triggering panic attacks [13]. Generally speaking, stress can reduce the quality of life, and its presence in our lives should preferably be avoided. Nevertheless, not all effects of stress are adverse. It would be wrong to limit stress to the top of a dramatic event; stress is also a method that our bodies use to cope with situations that require fast response and elevated activity. That means that in the case of a real threat, stress serves as a mechanism that allows us to react more efficiently and prioritise body functions to achieve maximal performance [14]. The response to stress can generally be classified into acute stress response and chronic stress response. In acute stress, the body reorganises the priorities to react fast and efficiently to demands and threads. Hormones enable the release of energy for organs that are vital for surviving threads (like muscles and the brain), and systems that are less critical for surviving (like digestion, taste, etc.), are reduced [15]. Some typical hormones that are involved in this response are adrenalin and cortisol. Fig. 1 Symptoms and physical response of stress. Wilhelm Daniel Scherz et al. / Procedia Computer Science 225 (2023) 4381–4388 4383 Wilhelm Daniel Scherz/ Procedia Computer Science 00 (2023) 000–000 The second kind of stress is the chronic stress response. The chronic stress response is more severe and lasting than the short-term stress. Compared to the other types of stress, it degenerates and breaks the regular work of the body. It can be compared to a system running beyond the specifications. In today's society, health awareness and the collection of vital biological data have become topics of significant importance, including a particular emphasis on stress management [16]. There exist various methodologies for measuring stress levels, which can be broadly categorised as self-report questionnaires, biomarker measurements (such as adrenaline and cortisol [17]), behavioural pattern measurements [18], and physiological parameter measurements using sensors [19]. However, the use of a limited set of parameters can make it challenging to differentiate between healthy physical activity and negative factors such as stress. The following presented study design aims to generate data while a driver is relaxed and when he is driving under stress. The study should help to answer the following question. How does stress behave while driving, and how does the stress change depending on the personality traits (neuroticism, extroversion and psychoticism) of the driver? 2. Materials and Methods To be able to generate and obtain reference values and stress-related data as well as to be able to analyse the influence of stress on road safety, a strict procedure is defined, as seen in Fig. 2 Flowchart Experiment. Each participant is required to fill out the consent form where the aim of the study is explained and his/her rights to the protection and procession of personal data are explained. Only when the participants sign the data consent agreement can the experiment start. After signing the agreement of consent, each participant gets a clean and disinfected Polar H10 chest strap that collects vital biodata like heart rate (HR). For the study of stress, the instantaneous heart rate is the most important because it contains the information that helps to detect and quantify stress. After the participant places the H10 chest strap, the participant gets two questionnaires. For filling out each of the questionnaires, it is used an ID number. No names or personal information are mentioned in the questionnaire. This aims to protect the data and the anonymity of the participants, so that there Fig. 2 Flowchart Experiment 4384 Wilhelm Daniel Scherz et al. / Procedia Computer Science 225 (2023) 4381–4388 4 Wilhelm Daniel Scherz/ Procedia Computer Science 00 (2023) 000–000 is no possibility of associating the participant with the ID. The first questionnaire is the Revised Eysenck Personality Questionnaire (EPQR-S). After the participant finishes filling out the EPQR-S questionnaire, he/she is given the Perceived Stress (PSQ-20) questionnaire. In both questionnaires, the identical IDs are used. After both questionnaires are filled, the participant takes place by the driving simulator and tries to get as comfortable as possible. After the participant feels comfortable, a clean noise-damping headset is given, and relaxing classical music is played for 5 minutes. While the music is playing, the HR is recorded using the Polar H10. This first set of data, which is obtained during the relaxation phase, is labelled as relaxation. After the first phase is finished, the music stops, and the participant starts driving in the driving simulator (see Fig. 3). The duration of this second phase is 25 minutes; the data obtained in this phase is labelled driving. It contains recordings of the bio-vital parameters as well as the characteristics of the driving style. During the driving phase, the participant is exposed to different reallife stressors like cars driving in the opposite lane, pedestrians crossing the street, and traffic jams. At the end of the experiment, the participant has to finish the second part of the EPQR-S questionnaire. 2.1. Driving simulator The City Car Driving simulator software was used to conduct driving tests because it incorporates sophisticated car physics, which leads to a remarkably realistic driver experience. Furthermore, the simulator permits the customisation of pedestrians and other road users’ behaviour, as well as traffic density. For the experiment, normal parameters were set, including random events that induce stress. The rules used in the simulator correspond to European traffic regulations. Each time the participant makes a mistake, he/she is informed by the system. The driving behaviour is stored in a log file for future processing. The Logitech G29 steering wheel is used as the input device in the experiment. This electronic steering wheel was created for driving video games with authentic force feedback; additionally, it includes three pedals and a gearbox. It enables an immersive experience in the virtual environment. The execution of the driving simulator on a computer and its technical characteristics are included in Table 1. Table 1 Specifications of the PC on which the driving simulator is run. An example of a column heading Column A (t) Model Alien Ware Area51 R4 Processor Intel Core I7 7800x Memory 16 GB DDR4 2666 MHz GPU 2 X Geforce 1080 TI SLI Storage 128 GB SanDisk M.2 SSD 2.2. Heart Signal As mentioned before, this experiment is designed to generate stress data, and one of the main parameters to measure the stress data is HR. Using the HR obtained from the chest strap, we can calculate the HRV (Heart Rate Variability. This value gives essential information about stress because it offers a deep insight into the autonomous nervous system. In order to analyse heart rate variability, we can study it by analysing two distinct domains: time and frequency. The time domain approach involves calculating the average or standard deviation of the time intervals between two successive heartbeats. Alternatively, the frequency domain method is based on the quantity of heart signal present in two different frequency bands, namely (0.04-0.15 Hz) and (0.15-0.4 Hz), which are used specifically for heart rate analysis. Wilhelm Daniel Scherz et al. / Procedia Computer Science 225 (2023) 4381–4388 4385 Wilhelm Daniel Scherz/ Procedia Computer Science 00 (2023) 000–000 Following a widely used principle as recommended in [20], the following analysis of the data can be done: • pNN50 (%): is the proportion of consecutive heartbeat intervals that differ by more than 50 milliseconds. A high PNN50 value shows that the heart rate fluctuates strongly. If driving stress is high, this variable decreases. LF/HF: LF (low frequency): 0.04 - 0.15 Hz; covers oscillations in the range of about 10 seconds and matches the periodic activity of the vasomotor part of the baroreflex loop (Mayer wave activity). HF (high frequency): 0.15 - 0.40 Hz; covers oscillations in the range of seconds (2 - 7 seconds). LF/HF is the modulation of the sympathetic and parasympathetic nervous systems divided by the HF associated with the parasympathetic activity. This value represents the global sympatric balance [21], and a high value means that the sympatric system is dominant, giving hints for elevated stress. In this experiment, the Polar H10 chest strap is used to collect the HR. The advantage of the Polar H10 is the possibility to measure accurately the RR intervals (time between two heartbeats), and the results are comparable with the data obtained from a holter [22]. To obtain the data from the chest strap, the application Elite HRV can be used [23], as well as an Android OS or an Apple OS smartphone. The advantage of the described system is that the data can be exported directly as a single file with all the obtained parameters. Table 2 shows the specifications of the Polar H10. Table 2 Polar H10 specifications. An example of a column heading Column A (t) Battery Type CR 2025 Battery sealing ring O-ring 20.0 × 0.90 Silicone Battery lifetime 400 h Sampling rate 100 Hz Operating temperature −10 °C to +50 °C Connector material ABS, ABS + GF, PC, Stainless steel Strap material 38% Polyamide, 29% Polyurethane, 20% 2.3. Music Tempo 4386 Wilhelm Daniel Scherz et al. / Procedia Computer Science 225 (2023) 4381–4388 6 Wilhelm Daniel Scherz/ Procedia Computer Science 00 (2023) 000–000 Many studies have shown the influence of music on human behaviour. Some examples of them are shown in [24], where a relationship between the music tempo and the time that people required to eat or drink was studied. In [25], a faster tempo of the music incremented the speed and the accuracy of mathematical calculations in stock market environments, giving an additional hint that the music tempo also changes how the tasks are performed. While driving, music can influence the driving speed depending on the music tempo [26]. Various parameters of the music, like genre, instruments, volume, etc., can affect the driver's behaviour, making it more aggressive or more relaxed. During our experiment, the participants used noise-damping headphones to listen to music; they could adjust the volume according to their taste to avoid discomfort. As shown in Fig. 2, at the beginning of the experiment and after answering the questionnaires, the participants had to listen to “Sonata for Two Pianos in D major” by Mozart for relaxation. 2.4. Questionnaires There are two questionnaires that were used for the experiment. As shown in Fig.2, the first one is the EPQR-S questionnaire, which is followed by the PSQ-20 questionnaire. At the end of the experiment, the second part of the EPQR-S has to be answered. The objective of the first questionnaire is to obtain the driver’s characteristics and the emotional and physical state of the participant. The questionnaire used the standardised PSQ-20 Questionnaire to collect information about the perceived emotional state, as well as the levels of stress, fatigue and sadness that the driver feels [27]. The PSQ- 20 is one of the most widely used stress questionnaires. All questions in the questionnaires are formatted as a Likert scale, giving, in the case of the PQS-20, four options for answering. The questionnaire gives four different results for the emotional categories of worries, tension, joy, and demands, as well as an overall score. According to [27], a high result in the category of joy demonstrates that the emotional state of the person can be described as joy and Fig. 3 Driving simulator Wilhelm Daniel Scherz et al. / Procedia Computer Science 225 (2023) 4381–4388 4387 Wilhelm Daniel Scherz/ Procedia Computer Science 00 (2023) 000–000 happiness. For the category tensions, demands and worries, a high value is negative and demonstrates a subdued state of mind of the participant. The overall score tells if the person feels his/her more or less stressed. The Eysenck Personality Questionnaire (EPQR) is a very common and widely used questionnaire to determine the personality traits of a person. The complete questionnaire consists of 100 items and classifies the personality of the participant into one of the four categories: psychoticism, neuroticism and extraversion [28]. High results in the category extroversion imply a lively, sociable and carefreeness personality. A high score in the category of neuroticism is often related to anxiety, depression and tension. A high score in the category of psychoticism is often related to aggressiveness, egocentrism and lack of empathy. It is important to mention that the previous categories are not exclusive, and a participant can have a high score in several of the categories at the same time. 3.Results This experiment design will generate two datasets of each participant to compare a relaxation phase to a driving phase with higher stress levels. Each participant will be characterised into three personality traits: neuroticism, extroversion and psychoticism, using the EPQR-S questionnaire. A higher score in each personality trait means that this trait is more dominant and significant. In addition to the personality traits, the emotional state and the perceived stress will be measured using the PSQ-20 Questionary. These results will allow us to analyse the relationship between stress, personality traits and bio-vital parameters (HR). Additionally, the result of this study should help to create models that explain the relationship between personality, stress perception and stress exposure. 4. Conclusion In conclusion, stress is a complex bodily response that arises from the combination of external demands and internal concerns and is strongly associated with inattention, anxiety, memory, and motoric performance. The impact of driving-related stress on road safety has been extensively studied, with findings suggesting that this type of stress is linked to an increase in driving mistakes and traffic violations. Factors such as time pressure, road congestion, poor road conditions, and aversion to driving have been identified as contributors to driving-related stress. 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