Next Article in Journal
Shifting from Conventional to Organic Filter Media in Wastewater Biofiltration Treatment: A Review
Next Article in Special Issue
Bibliometric Analysis of Dental Erosion (2011–2020)
Previous Article in Journal
An Investigation of Work-Based Education and Training Needs for Effective BIM Adoption and Implementation: An Organisational Upskilling Model
Previous Article in Special Issue
Evaluation of Patient’s Perception and Acceptance of Tele(oral)Medicine for Care during the COVID-19 Pandemic: A Cross-Sectional Pilot Survey
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 11
Issue 18
10.3390/app11188648
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

by
Katarzyna Mocny-Pachońska
1,*,
Rafał J. Doniec
2,
Szymon Sieciński
2,*,
Natalia J. Piaseczna
2,
Marek Pachoński
3 and
Ewaryst J. Tkacz
2
1
Department of Conservative Dentistry with Endodontics, Faculty of Medical Science, Medical University of Silesia, Pl. Akademicki 17, 41902 Bytom, Poland
2
Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41800 Zabrze, Poland
3
Pachońscy Dental Clinic, Lubliniecka 38, 42288 Strzebin, Poland
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2021, 11(18), 8648; https://doi.org/10.3390/app11188648
Submission received: 2 August 2021 / Revised: 12 September 2021 / Accepted: 15 September 2021 / Published: 17 September 2021
(This article belongs to the Special Issue Oral Medicine, Theory, Methods and Applications)
Browse Figures
Versions Notes

Abstract

:
Stress is a physical, mental, or emotional response to a change and is a significant problem in modern society. In addition to questionnaires, levels of stress may be assessed by monitoring physiological signals, such as via photoplethysmogram (PPG), electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), facial expressions, and head and body movements. In our study, we attempted to find the relationship between the perceived stress level and physiological signals, such as heart rate (HR), head movements, and electrooculographic (EOG) signals. The perceived stress level was acquired by self-assessment questionnaires in which the participants marked their stress level before, during, and after performing a task. The heart rate was acquired with a finger pulse oximeter and the head movements (linear acceleration and angular velocity) and electrooculographic signals were recorded with JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan). We observed significant differences between the perceived stress level, heart rate, the power of linear acceleration, angular velocity, and EOG signals before performing the task and during the task. However, except for HR, these signals were poorly correlated with the perceived stress level acquired during the task.

1. Introduction

Stress is the physical, mental, or emotional response to a change that is caused by an imbalance between the demands of an individual and the individual’s ability to cope with them [1]. Despite the knowledge of the significant role of stress in the etiology of certain diseases (cardiac, mental, and many others) it is not classified as a separate ICD-10 or DSM V unit, but only considered a risk factor [2].
Stress has become a significant problem in modern societies and can lead to cognitive impairment, depression, and even cardiovascular diseases [3]. Stress affects not only mental health but also involvement in work and general attitude in everyday life [4,5,6].
The response to stress is related to the initiation of changes in its functioning. When a threat occurs, the autonomic nervous system (ANS) is stimulated and inhibits the activity of the parasympathetic system and activates the sympathetic nervous system. This reaction results in increased secretion of stress-related hormones, causing vasoconstriction, increased blood pressure, increased respiratory rate, increased muscle tone, heart rate (HR), and decreased heart rate variability (HRV). Among many different physiological stress indicators, such as blood pressure, cortisol level, increased skin conductivity, and head and body movements, HRV is considered one of the reliable methods for assessing the physiological response of the body in response to stress. HRV is caused by the interaction of the sympathetic and parasympathetic parts of the autonomic nervous system and can be analyzed in time and frequency domains [6,7,8,9,10,11].
Head movement behavior is considered a part of non-verbal communication that expresses itself in various aspects of everyday life. There are several actions, such as lowering, lifting, tilting, nodding, or shaking, which have specific meanings and are recognizable in intercultural communication. Head movement features have been used in a few studies assessing whether a patient is under stress–they are usually more frequent, faster, and more significant [10]. The application of electrooculography (EOG) in the design of brain-computer interfaces and activity recognition has been described in several studies [12,13,14,15,16]. Electrooculography (EOG) is a technique for measuring the resting electrical potential between the cornea and retina of the human eye by registering the electrical signal via electrodes placed around the eyes [12,17]. Based on the fact that electrooculography is often used to detect eye blinks [12,13], we can assess the level of stress using EOG and analyzing the dynamics of eye blinks [18].
There are indications that EOG-measured eye movements are consistent with brain activation in both the parietal and frontal cortex during attention-shifting tasks. As a consequence, stress can cause visual distractions which have a negative influence on task performance [19]. Much research has focused on cognitive and perceptual processing based on eye movements and fixations [20]. In [21] Rayner showed that the duration of eye fixation depends on cognitive processes; these data on eye movement may provide important and interesting knowledge about human information processing.
Many studies have shown that dental students are at higher risk of stress than students of other faculties and study programs because of unique stressors, such as the need to acquire an extensive body of theoretical knowledge combined with manual training, practiced in preclinical simulation centers and then in clinical practice [22,23,24,25,26,27,28,29,30,31]. These circumstances are related to high levels of stress in dental students worldwide in comparison with the general population [27,29,30,32,33].

2. Related Works

A popular approach to the assessment of stress level is using questionnaires due to their simple setup. A widely used instrument to measure stress is the PSS-10 scale. The PSS-10 assesses the extent to which a person perceived life as unpredictable, uncontrollable, and overloaded in the previous month [34]. One common tool used in stress studies in academia is the Dental Environment Stress (DES) questionnaire [35]. In addition to demographics, the questionnaire contains 41 items grouped into seven stress-inducing domains as follows: self-efficacy beliefs, faculty and administration, workload, patient treatment, clinical training, performance pressure, and social stressors.
While DES is useful for investigating the source of stress, it was not designed to measure stress levels among students. Another well-proven and reliable tool for measuring experiences related to depression, anxiety, and stress is the DASS-21 scale. In this context, depression is characterized by the absence of positive feelings, a sense of hopelessness, and loss of self-esteem, while anxiety is characterized by autonomic agitation and fearfulness [36,37,38].
The DASS scale was initially developed to measure the signs of depression and anxiety. The development of DASS scale resulted in developing the third part of the scale which measures the physiological stress. The basic version of the DASS scale is a 42-point scale (also known as DASS-42), which consists of three 14-point sub-scales which measure the level of depression, anxiety, and physiological stress [37]. This structure is in line with the tripartite model of anxiety and depression proposed by Clark and Watson [39,40,41]. The shorter version of DASS-42, known as DASS-21, has slightly better psychometric properties compared to the full DASS scale (DASS-42) [42].
Another tool frequently used for assessing anxiety is the Immediate Anxiety Measures Scale, which is used for assessing anxiety associated with a task [43,44]. It evaluates the cognitive and somatic symptoms of anxiety.
In addition to questionnaires [2,45,46,47,48], the level of stress may be assessed by monitoring physiological signals, such as photoplethysmogram (PPG), electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), facial expressions, and head and body movements. The most common set of physiological signals involves heart rate variability (HRV) in combination with electrodermal activity [10,11,49,50,51,52].
Thanks to the technological improvements, monitoring of stress level could be performed with various wearable devices, such as smart watches and smart glasses, e.g., Google Glass or JINS MEME ES_R (JINS Holdings, Inc., Tokyo, Japan) [53,54,55,56,57,58,59,60,61], or smartphones [62]. These devices may be used to estimate the level of one’s concentration; for instance, Ishimaru et al., conducted a study [63] which involved the use of JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan), further described in [61], to estimate the user’s concentration level by processing the EOG signal and head position.
The development of wearable smart sensors for measuring basic physiological parameters enables data collection in everyday activities and opens new research areas in signal processing and data classification [64].

3. Study Objective

The purpose of the study was to find the relationship of the perceived stress level reported in the questionnaires and the changes of heart rate, electrooculographic signals, and the linear acceleration and angular velocity of the head measured by a finger pulse oximeter and JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan).
The study was based on the hypothesis that the level of stress experienced by participants of the study is reflected in physiological signals (such as HR, EOG, and head movements). If the hypothesis was confirmed, it would be possible to use the aforementioned signals as markers of stress.

4. Materials and Methods

4.1. Experiment Setup

The study was conducted on twenty 3rd year students (18 females and 2 males, aged 22.19 ± 1.50 years) of the Medical University of Silesia, Faculty of Medical Sciences in Zabrze (Zabrze, Poland) during seminar classes between 1 October 2020 and 15 October 2020. At that time, seminar classes could be conducted because of the state and university regulations on teaching during the COVID-19 pandemic in Poland [65]. The imbalance between male and female participants, expressed as the percentage of women in the examined population (90%) was higher than the gender distribution at the university (73.51%) ([66], p. 45), academic programs in medical sciences (78.48%) ([66], p. 24) and medical universities (72.4%) in Poland ([66], p. 15).
The experiment consisted of two phases: The first phase was the relaxation phase in which the subjects watched a 4 min video of landscapes with relaxing music. After one minute of the video, we started acquiring the data via JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan). After another minute, the video was interrupted by a sound signal (“Space” timer ringtone in iPhone 7 set with the maximum volume 40–50 dB and frequency 0–296 Hz). For each subject, the exact moment of the sound signal occurrence was documented in the experiment sheet. The setup for the first phase was similar to the example shown in Figure 1.
The second phase of the experiment was to complete a task which served as a stress-inducing factor: to match the endodontic instruments and dental burs with their names. The endodontic instruments are shown in Figure 2 and the dental burs are presented in Figure 3.
There was not a strict time limit to finish the task; however, the EOG signal, linear acceleration, and angular velocity of the subject’s head were acquired for approximately one minute. The heart rate was recorded during the entire experiment.
The participants (students) were informed about the experiment outline but they did not know when the signal would occur and how long the data acquisition would last. All participants were provided with the same conditions: a noise-insulated room, adequately lit and ventilated, with sufficient space to complete the answers, and a comfortable seat.
The students were also asked to complete the questionnaire after completing the task. The questionnaire consisted of the following questions in which the participants marked their perceived stress level during each phase (before, during, and after performing the task):
  • How performing a task affects your perceived stress level:
    • While watching the film (before the sound signal)?
    • During the performed task (after the sound signal)?
    • After completing the task?
  • How stressful was waiting for the signal to occur and to start performing the task?
The questionnaire design was based on a seven-point Likert scale (0, no stress; 6, high stress) [67]. The scale included values from 0 to 6, where 0 means no stress, 1 and 2—low level of stress, 3 and 4—medium level of stress, and 5 and 6—high level of stress. The answers to the last question were not considered in further analyses.
The study design was approved by the Ethical Commission of Medical University of Silesia under the resolution number KNW/0022/KB1/79/18 taken on 16 October 2018. All participants gave informed consent before the experiment.

4.2. Technology Used

Electrooculographic (EOG) signals and head movement (linear acceleration—ACC, and angular velocity—GYRO) signals were acquired with JINS MEME ES_R (JINS Holdings, Inc., Tokyo, Japan) smart glasses. The glasses are equipped with three-point EOG sensors and a six-axis inertial measurement unit (IMU) with a three-axis accelerometer and a three-axis gyroscope. The six-axis sensor detects speed, motion, and rotation and may be used to measure movements of the head and whole body. The EOG sensor registers the electrical potential of an eye and can be used to detect eye blinks and eyeball movements [61,63,68]. We chose such smart glasses because of their minimal impact on the subject’s physical and psychological comfort, and the access to raw signal data [61]. The examples of registered raw signals are shown in Figure 4, Figure 5 and Figure 6.
Figure 4 shows linear acceleration (signals from accelerometer) of the subject’s head during the experiment in X (red), Y (blue), and Z (black) axes.
Figure 5 presents the angular velocity (signals from the gyroscope) of the subject’s head during the experiment in X (red line), Y (blue dots), and Z (green dashed line) axes.
Figure 6 shows sample signals from the EOG sensor (electric potentials) acquired during the experiment on the left electrode (orange) and right electrode (violet), as well as horizontal (blue) and vertical (red) differences between them.
To measure the heart rate, we used a TM-PX30 finger pulse oximeter (Tech-Med, Warsaw, Poland), which was placed on the index finger of the non-dominant hand. The subjects did not have their nails polished during the study to provide the optimal conditions for measurement. The heart rate was registered during each phase of the experiment (before, during, and after the task) for each subject, but only one measurement was considered in the analyses.

4.3. Signal Processing

The EOG and linear acceleration and angular velocity of the subject’s head registered by smart glasses were divided into two parts, before the task and during the task, because the data had not been acquired after the task. The linear acceleration in each axis (X, Y, and Z) was combined into the total linear acceleration (ACC) as:
A C C = A C C x 2 + A C C y 2 + A C C z 2
The ACC signal is shown in Figure 7.
The angular velocity in each axis (X, Y, and Z) was also combined into the total angular velocity (GYRO) as:
G Y R O = G Y R O x 2 + G Y R O y 2 + G Y R O z 2
and the result is shown in Figure 8.
Based on the approach presented in [69], the electrooculographic signals in four channels (H, V, L, and R) were combined into the total EOG signal as:
E O G = E O G H 2 + E O G V 2 + E O G L 2 + E O G R 2
and the result is shown in Figure 9.
The next step was calculating the power of the combined signal as:
1 N ∑ n = 1 N x n 2
where xn is the n-th sample of the signal x and N is the signal length.
Figure 7, Figure 8 and Figure 9 show that it is possible to determine the difference in the envelope of the ACC, GYRO, and EOG signals.

4.4. Statistical Analysis

The perceived stress level between the three related samples (before, during, and after the task) is expressed in an ordinal scale (0–6), so we chose Friedman’s test described in [70,71,72] to evaluate the differences between the analyzed phases of the experiment.
The heart rate in the three analyzed phases is expressed in the interval scale. In this case, we performed an all-sample normality test (Shapiro-Wilk test defined in [73]), and then we checked the compound symmetry of the data by performing the sphericity test further described in [74], and finally, we chose the Friedman’s test. The statistical significance of differences between analyzed phases was evaluated by Conover and Dunn-Bonferroni post-hoc tests which are typically used after performing Friedman’s test.
To analyze EOG, linear acceleration, and angular velocity, we chose the χ2 test. The relationship between the measured heart rate, EOG signal, head movement signals, and the reported level of perceived stress was evaluated with the Spearman’s rank correlation because of the use of the ordinal scale [75]. The level of statistical significance α was set to 0.05.

5. Results

Before selecting the parametric test for the HR data, we performed the Shapiro-Wilk normality of distribution test for all phases (before, during, and after the task). The obtained results (before—p = 0.0036, during—p = 0.7600, and after—p = 0.7530) led to the rejection of the normality hypothesis in the before case. Thus, the statistical analysis was completed by performing the Mauchly’s and JNS sphericity test performed to evaluate the differences of variances between the phases. The results (JNS = 44.06, p < 0.001) indicated that the use of parametric tests was not applicable. Therefore, we decided to use Friedman’s test in further analyses.
The Friedman’s test results shown in Figure 10 confirm significant differences between the three phases of the experiment (Fr = 23.68; p < 0.001). The results of Friedman’s test were supported with Dunn-Bonferroni and Conover post-hoc tests (see Table 1) which proved that the heart rate before performing the task was significantly lower than the heart rate during the task (p < 0.001) and after the task (p < 0.001).
Figure 11 shows differences between the perceived stress levels in the subsequent phases of the task. The median of perceived stress level before the task was 2, during the task was 4, and after the task was 1.5. The highest perceived level of stress was 6 for participants in the “during the task” phase.
The Friedman’s test results shown in Figure 11 confirm significant differences between the three analyzed phases (Fr = 18.35, p < 0.001) and they were supported by Dunn-Bonferroni and Conover post-hoc tests (see Table 2) which proved that the perceived stress level during performing the task was significantly higher than the stress level before the task (p < 0.001) and after the task (p < 0.001).
The results of Spearman’s rank correlation analysis presented in Table 3 proved that the relationship between the heart rate and perceived stress level during the task was statistically significant (p = 0.005).
The median power of total linear acceleration, angular velocity, and EOG signal before the task was 5.362 × 105 s2, 4.260 × 106 s2, and 7.533 × 103 s2, and the median power of total linear acceleration during the task was 5.483 × 105 s2, 4.330 × 106 s2, and 1.644 × 105 s2 (see Figure 12, Figure 13 and Figure 14).
The test χ2 showed differences between all signals in the phases before and during the task, but only for linear acceleration and angular velocity were the differences statistically significant (p < 0.001). To check the correlation between signals and perceived stress level, we conducted Spearman’s test.
The results of the Spearman’s rank correlation analysis presented in Table 4, Table 5 and Table 6 proved that this correlation was not statistically significant.

6. Discussion

In this study, we attempted to find the relationship between the perceived stress level declared in the questionnaires and the linear acceleration, angular velocity of head, electrooculographic signals, and heart rate registered by smart glasses and a finger pulse oximeter. The role of the finger pulse oximeter was providing the heart rate, and the smart glasses were used to acquire the linear acceleration and angular velocity of the subject’s head and EOG signals, which may be used to capture eyeball movements and other activities [63].
The acquisition of heart rate and other signals to enhance the stress recognition accuracy was described by Ahn et al. in [3] and Meina et al., in [6]. Ahn et al. in [3] achieved the stress level measurement accuracy of 87.5% by processing the EEG and ECG signals registered simultaneously. Meina et al. in [6] presented an approach to recognize the stress in firefighters based on 24 h registrations of ECG and acceleration in combination with the results of self-assessment of stress level in questionnaires. The performance of their approach expressed by the area under the curve (AUC) index was between 0.57 and 0.7.
Heart rate is the frequency of cardiac contractions and is susceptible to changes of the physical and mental condition. Therefore, heart rate is considered as one of the most important health indicators [58]. Many studies confirm the relationship between the HR and stress or anxiety level; Trotman et al. in [8] proved that the perceived heart rate is more significantly related to the level of anxiety than the measured heart rate. These findings play an important role in stress and anxiety coping strategies.
In [9] Szyjkowska et al. confirmed that work-related stress is strongly correlated with the heart rate and blood pressure; work-related stress in men was usually in association with higher blood pressure, whereas more prominent heart rate variability was usually observed in women. Moreover, stress also affects cognitive abilities; people suffering from stress make more mistakes and perform tasks less accurately [1].
In our study, the results of the Friedman’s test and post-hoc tests prove that the heart rate before and during the task was significantly higher than after the task (p < 0.001), whereas the perceived stress level during performing the task was significantly higher than the stress level before and after the task (p < 0.001). The Spearman’s rank correlation between the perceived stress level and heart rate was 0.561 (p = 0.005), which could be considered as a significant correlation. These results prove the hypothesis that the stress level and heart rate are correlated.
The differences between the power of total linear acceleration and total angular velocity before the task and during the task were statistically significant; however, we did not observe such differences for the power of the total EOG signal. This finding proves that the eyeball movements may not be correlated with the fact of performing the task, contrary to the findings of studies conducted by Doniec et al. [14,76], Li et al. [77], Shirahama et al. [78], and Deng et al. [79].
The study conducted by Dumitrescu et al. on a group of 50 students of physiology showed, as in our study, no statistically significant differences in the horizontal and vertical EOG values between the control group and the study group, classified in terms of visual dispersion. The authors emphasize that the visual distraction used in this study may not have been significant enough to deteriorate student performance in exams [19].
The literature presents a wide range of developed approaches to the selection of analyses for multidimensional data and various application purposes. Many existing methods, such as mean and variance values in sequence or first-order derivatives, are based on prior knowledge and manual examination, causing difficulties in providing detailed information and problems with statistical validation. There are also several methods of EOG data based on a heuristic approach using information on the basic types of eye movement, such vibrations, fixations, or batting [80,81,82,83]. The problem of extraction and selection of appropriate features from EOG signals in terms of the recognition of actions has not been thoroughly investigated. EOG signals acquired with smart glasses contain lots of data, but the recognition of several cognitive activities (e.g., learning, relaxing, or stress) is challenging [14,76,78].
Meina et al., in [6] proved that the perceived stress level and physiological signals are strongly correlated based on the results of Welch’s t-test. In our study, the Spearman’s rank correlation between the power of total linear acceleration, angular velocity, and EOG signals show that the head movements and electrooculographic signals (electrooculograms) are poorly correlated with the perceived stress level.
The use of physiological observations for stress recognition began much earlier than the attempts to construct a polygraph–in ancient China, the testimony of an interrogated person was verified by testing for dry mouth with rice. Galvanometric methods use the fact of increased activity of sweat glands under stress resulting in changes of the resistance between the skin and the electrode [84]. In our study, we confirmed the relationship between increased HR and stress perception. Therefore, we recognize the necessity of using mixed methods to objectify the stress measurement.

6.1. Limitations of the Study

The limitations of the study are the lack of EOG and head movement data acquired after the performed task, a small study group from one university (20 dental students at the Medical University of Silesia, Zabrze, Poland), gender bias (the vast majority of the study group consisted of female dental students), and the fact that the heart rate was registered only once during each phase of the experiment. The number of subjects was limited due to the involvement of students of medical programs in combating the COVID-19 pandemic and the access to the university’s facilities for students.
To address them, we consider including more subjects in the study group with more diverse characteristics, acquiring signals with smart glasses in all three phases of the experiment and including more signal features in future studies. Another part of this study worth further research is the fact of no statistically significant correlations between the perceived stress level were reported for questionnaires, head movements, and EOG signals.

6.2. Practical Application

This study shows some important problems in categorizing stress. First, sensors are highly dependent on the level of motion and conductivity of the electrodes. The glasses, although non-invasive, do not completely adapt to the patient. Due to the individual characteristics, the glasses can slip off and the nasal electrodes can stick out. For this reason, a better method for detecting and removing artifacts and conduction errors should be implemented.
Session data captured with glasses during activities performed in real conditions are a valuable carrier of information, but also errors and noises caused by reflex gestures (e.g., fixing glasses that fall off). They are definitely different from data collected under laboratory conditions and as such require special algorithms and techniques for signal processing. This issue should be addressed in further research.

7. Conclusions

A positive and statistically significant correlation between HR and the level of stress perceived in the “during the task” phase confirms the possibility of using HR as a stress marker. The HR signal can be used to objectify the stress level measurement.
No statistically significant correlation was found between the EOG, ACC, or GYRO signals and the level of stress perceived in the study group. Although the aforementioned signals acquired with JINS MEME_R smart glasses failed to indicate stress level in our study, further research needs to be done.
However, based on the test results of the head movement signals and the perceived stress level for the analyzed phases (before, during the task), we can distinguish the phases of the experiment.

Author Contributions

Conceptualization, K.M.-P., M.P., R.J.D., and S.S.; methodology, K.M.-P., R.J.D., S.S.; validation, K.M.-P., R.J.D., S.S.; formal analysis, K.M.-P., and R.J.D.; investigation, K.M.-P.; resources, K.M.-P., M.P., R.J.D.; writing–original draft preparation, K.M.-P., N.J.P., S.S., and M.P.; writing–review and editing, K.M.-P., R.J.D., S.S., N.J.P., E.J.T.; visualization, K.M.-P., S.S., and N.J.P.; project administration, K.M.-P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Medical University of Silesia under the resolution number KNW/0022/KB1/79/18 taken on 16 October 2018.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available in https://www.mdpi.com/1424-8220/xx/1/5/s2 (accessed on 13 September 2021).

Acknowledgments

We would like to thank the students who agreed to participate in the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rodrigues, S.; Paiva, J.S.; Dias, D.; Aleixo, M.; Filipe, R.M.; Cunha, J.P.S. Cognitive impact and psychophysiological effects of stress using a biomonitoring platform. Int. J. Environ. Res. Public Health 2018, 15, 1080. [Google Scholar] [CrossRef] [Green Version]
  2. Nielsen, M.G.; Ørnbøl, E.; Vestergaard, M.; Bech, P.; Larsen, F.B.; Lasgaard, M.; Christensen, K.S. The construct validity of the Perceived Stress Scale. J. Psychosom. Res. 2016, 84, 22–30. [Google Scholar] [CrossRef] [PubMed]
  3. Ahn, J.W.; Ku, Y.; Kim, H.C. A novel wearable EEG and ECG recording system for stress assessment. Sensors 2019, 19, 1991. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Júnior, I.G.O.; Camelo, L.V.; Mill, J.G.; Ribeiro, A.L.; de Jesus Mendes da Fonseca, M.; Griep, R.H.; Bensenor, I.J.; Santos, I.S.; Barreto, S.M.; Giatti, L. Job stress and heart rate variability: Findings fromthe ELSA-Brasil Cohort study. Psychosom. Med. 2019, 81, 536–544. [Google Scholar] [CrossRef] [PubMed]
  5. Parent, M.; Peysakhovich, V.; Mandrick, K.; Tremblay, S.; Causse, M. The diagnosticity of psychophysiological signatures: Can we disentangle mental workload from acute stress with ECG and fNIRS? Int. J. Psychophysiol. 2019, 146, 139–147. [Google Scholar] [CrossRef]
  6. Meina, M.; Ratajczak, E.; Sadowska, M.; Rykaczewski, K.; Dreszer, J.; Bałaj, B.; Biedugnis, S.; Węgrzyński, W.; Krasuski, A. Heart rate variability and accelerometry as classification tools for monitoring perceived stress levels—A pilot study on firefighters. Sensors 2020, 20, 2834. [Google Scholar] [CrossRef] [PubMed]
  7. Castaldo, R.; Melillo, P.; Bracale, U.; Caserta, M.; Triassi, M.; Pecchia, L. Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis. Biomed. Signal Process. Control 2015, 18, 370–377. [Google Scholar] [CrossRef] [Green Version]
  8. Trotman, G.P.; Veldhuijzen van Zanten, J.J.C.S.; Davies, J.; Möller, C.; Ginty, A.T.; Williams, S.E. Associations between heart rate, perceived heart rate, and anxiety during acute psychological stress. Anxiety Stress Coping 2019, 32, 711–727. [Google Scholar] [CrossRef] [Green Version]
  9. Szyjkowska, A.; Gadzicka, E.; Szymczak, W.; Bortkiewicz, A. The reaction of the circulatory system to stress and electromagnetic fields emitted by mobile phones—24-h monitoring of ECG and blood pressure. Med. Pract. 2019, 70, 411–424. [Google Scholar] [CrossRef] [PubMed]
  10. Giannakakis, G.; Manousos, D.; Simos, P.; Tsiknakis, M. Head movements in context of speech during stress induction. In Proceedings of the 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), Xi’an, China, 15–19 May 2018; pp. 710–714. [Google Scholar] [CrossRef]
  11. Hadar, U.; Steiner, T.; Grant, E.; Rose, F.C. Head movement correlates of juncture and stress at sentence level. Lang. Speech 1983, 26, 117–129. [Google Scholar] [CrossRef] [PubMed]
  12. Pander, T.; Przybyla, T.; Czabanski, R. An application of detection function for the eye blinking detection. In Proceedings of the Conference on Human System Interactions, Krakow, Poland, 25–27 May 2008; pp. 287–291. [Google Scholar] [CrossRef]
  13. Käthner, I.; Kübler, A.; Halder, S. Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: A case study with a participant in the locked-in state. J. Neuroeng. Rehabil. 2015, 12, 76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Doniec, R.; Sieciński, S.; Piaseczna, N.; Mocny-Pachońska, K.; Lang, M.; Szymczyk, J. The classifier algorithm for recognition of basic driving scenarios. In Information Technology in Biomedicine; Piętka, E., Badura, P., Kawa, J., Więcławek, W., Eds.; Springer Nature AG: Cham, Switzerland, 2020; pp. 359–367. [Google Scholar] [CrossRef]
  15. Niwa, S.; Yuki, M.; Noro, T.; Shioya, S.; Inoue, K. A Wearable Device for Traffic Safety—A Study on Estimating Drowsiness with Eyewear, JINS MEME; SAE Technical Paper Series; SAE International: Detroit, MI, USA, 2016. [Google Scholar] [CrossRef]
  16. Stapel, J.; Hassnaoui, M.E.; Happee, R. Measuring driver perception: Combining eye-tracking and automated road scene perception. Hum. Factors Int. J. Hum. Factors Ergon. 2020. [Google Scholar] [CrossRef] [PubMed]
  17. Joseph, D.P.; Miller, S.S. Apical and basal membrane ion transport mechanisms in bovine retinal pigment epithelium. J. Physiol. 1991, 435, 439–463. [Google Scholar] [CrossRef] [PubMed]
  18. Korda, A.I.; Giannakakis, G.; Ventouras, E.; Asvestas, P.A.; Smyrnis, N.; Marias, K.; Matsopoulos, G.K. Recognition of blinks activity patterns during stress conditions using CNN and markovian analysis. Signals 2021, 2, 6. [Google Scholar] [CrossRef]
  19. Dumitrescu, C.; Stanley, M.; Treat, J.; Zacharsky, A.; Zaugg, A. The effect of a visual distraction on test-taking performance. J. Adv. Stud. Sci. JASS 2019, 2018. Available online: http://digital.library.wisc.edu/1793/81995 (accessed on 13 September 2021).
  20. Müller, J.A.; Wendt, D.; Kollmeier, B.; Brand, T. Comparing eye tracking with electrooculography for measuring individual sentence comprehension duration. PLoS ONE 2016, 11, e0164627. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Rayner, K. Visual attention in reading: Eye movements reflect cognitive processes. Mem. Cogn. 1977, 5, 443–448. [Google Scholar] [CrossRef]
  22. Piazza-Waggoner, C.A.; Cohen, L.L.; Kohli, K.; Taylor, B.K. Stress management for dental students performing their first pediatric restorative procedure. J. Dent. Educ. 2003, 67, 542–548. [Google Scholar] [CrossRef]
  23. Dyrbye, L.N.; Thomas, M.R.; Shanafelt, T.D. Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Acad. Med. 2006, 81, 354–373. [Google Scholar] [CrossRef]
  24. Divaris, K.; Mafla, A.C.; Villa-Torres, L.; Sánchez-Molina, M.; Gallego-Gómez, C.L.; Vélez-Jaramillo, L.F.; Tamayo-Cardona, J.A.; Pérez-Cepeda, D.; Vergara-Mercado, M.L.; Simancas-Pallares, M.Á.; et al. Psychological distress and its correlates among dental students: A survey of 17 Colombian dental schools. BMC Med. Educ. 2013, 13, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Albajjar, M.; Bakarman, M. Prevalence and correlates of depression among male medical students and interns in Albaha University, Saudi Arabia. J. Fam. Med. Prim. Care 2019, 8, 1889–1894. [Google Scholar] [CrossRef]
  26. Brazeau, C.M.; Shanafelt, T.; Durning, S.J.; Massie, F.S.; Eacker, A.; Moutier, C.; Satele, D.V.; Sloan, J.A.; Dyrbye, L.N. Distress among matriculating medical students relative to the general population. Acad. Med. 2014, 89, 1520–1525. [Google Scholar] [CrossRef]
  27. Kötter, T.; Wagner, J.; Brüheim, L.; Voltmer, E. Perceived Medical School stress of undergraduate medical students predicts academic performance: An observational study. BMC Med. Educ. 2017, 17, 256. [Google Scholar] [CrossRef] [Green Version]
  28. Ali, K.; Cockerill, J.; Zahra, D.; Tredwin, C.; Ferguson, C. Impact of Progress testing on the learning experiences of students in medicine, dentistry and dental therapy. BMC Med. Educ. 2018, 18, 253. [Google Scholar] [CrossRef] [Green Version]
  29. Halboub, E.; Alhajj, M.N.; AlKhairat, A.M.; Sahaqi, A.A.M.; Quadri, M.F.A. Perceived stress among undergraduate dental students in relation to gender, clinical training and academic performance. Acta Stomatol. Croat. 2018, 52, 37–45. [Google Scholar] [CrossRef]
  30. Mocny-Pachońska, K.; Doniec, R.; Trzcionka, A.; Pachoński, M.; Piaseczna, N.; Sieciński, S.; Osadcha, O.; Łanowy, P.; Tanasiewicz, M. Evaluating the stress-response of dental students to the dental school environment. PeerJ 2020, 8, e8981. [Google Scholar] [CrossRef]
  31. Mocny-Pachońska, K.; Doniec, R.J.; Wójcik, S.; Sieciński, S.; Piaseczna, N.J.; Duraj, K.M.; Tkacz, E.J. Evaluation of the most stressful dental treatment procedures of conservative dentistry among polish dental students. Int. J. Environ. Res. Public Health 2021, 18, 4448. [Google Scholar] [CrossRef]
  32. Basudan, S.; Binanzan, N.; Alhassan, A. Depression, anxiety and stress in dental students. Int. J. Med. Educ. 2017, 8, 179–186. [Google Scholar] [CrossRef] [Green Version]
  33. Mocny-Pachońska, K.; Doniec, R.; Trzcionka, A.; Lang, M.; Pachoński, M.; Piaseczna, N.; Sieciński, S.; Twardawa, H.; Tanasiewicz, M. The stress level assessment based on socio-demographic and gender factors among Polish and Taiwanese female and male junior dental students. In Information and Software Technologies, Proceedings of the 25th International Conference (ICIST 2019), Vilnius, Lithuania, 10–12 October 2019; Damaševičius, R., Vasiljeviene, G., Eds.; Springer International Publishing: Cham, Switzerland, 2019; Volume 1078, pp. 553–564. [Google Scholar] [CrossRef]
  34. Cohen, S.; Kamarck, T.; Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 1983, 24, 385. [Google Scholar] [CrossRef]
  35. Al-Sowygh, Z.H. Academic distress, perceived stress and coping strategies among dental students in Saudi Arabia. Saudi Dent. J. 2013, 25, 97–105. [Google Scholar] [CrossRef] [Green Version]
  36. Crawford, J.R.; Henry, J.D. The Depression Anxiety Stress Scales (DASS): Normative data and latent structure in a large non-clinical sample. Br. J. Clin. Psychol. 2003, 42, 111–131. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Lovibond, P.; Lovibond, S. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 1995, 33, 335–343. [Google Scholar] [CrossRef]
  38. Antony, M.M.; Bieling, P.J.; Cox, B.J.; Enns, M.W.; Swinson, R.P. Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychol. Assess. 1998, 10, 176–181. [Google Scholar] [CrossRef]
  39. Clark, L.A.; Watson, D. Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. J. Abnorm. Psychol. 1991, 100, 316–336. [Google Scholar] [CrossRef]
  40. Anderson, E.; Hope, D. A review of the tripartite model for understanding the link between anxiety and depression in youth. Clin. Psychol. Rev. 2008, 28, 275–287. [Google Scholar] [CrossRef] [Green Version]
  41. Brown, T.A.; Chorpita, B.F.; Barlow, D.H. Structural relationships among dimensions of the DSM-IV anxiety and mood disorders and dimensions of negative affect, positive affect, and autonomic arousal. J. Abnorm. Psychol. 1998, 107, 179–192. [Google Scholar] [CrossRef]
  42. Gloster, A.T.; Rhoades, H.M.; Novy, D.; Klotsche, J.; Senior, A.; Kunik, M.; Wilson, N.; Stanley, M.A. Psychometric properties of the Depression Anxiety and Stress Scale-21 in older primary care patients. J. Affect. Disord. 2008, 110, 248–259. [Google Scholar] [CrossRef] [Green Version]
  43. Thomas, O.; Hanton, S.; Jones, G. An alternative approach to short-form self-report assessment of competitive anxiety: A research note. Int. J. Sport Psychol. 2002, 33, 325–336. [Google Scholar]
  44. Trotman, G.P.; Williams, S.E.; Quinton, M.L.; van Zanten, J.J.V. Challenge and threat states: Examining cardiovascular, cognitive and affective responses to two distinct laboratory stress tasks. Int. J. Sport Psychol. 2018, 126, 42–51. [Google Scholar] [CrossRef]
  45. Wang, Y.; Wang, P. Perceived stress and psychological distress among chinese physicians. Medicine 2019, 98, e15950. [Google Scholar] [CrossRef]
  46. Lin, X.J.; Zhang, C.Y.; Yang, S.; Hsu, M.L.; Cheng, H.; Chen, J.; Yu, H. Stress and its association with academic performance among dental undergraduate students in Fujian, China: A cross-sectional online questionnaire survey. BMC Med. Educ. 2020, 20, 181. [Google Scholar] [CrossRef] [PubMed]
  47. Stormon, N.; Ford, P.J.; Kisely, S.; Bartle, E.; Eley, D.S. Depression, anxiety and stress in a cohort of Australian dentistry students. Eur. J. Dent. Educ. 2019, 23, 507–514. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Mocny-Pachońska, K.; Trzcionka, A.; Doniec, R.J.; Sieciński, S.; Tanasiewicz, M. The influence of gender and year of study on stress levels and coping strategies among Polish dental. Medicina 2020, 56, 531. [Google Scholar] [CrossRef]
  49. Liao, W.; Zhang, W.; Zhu, Z.; Ji, Q. A Real-time human stress monitoring system using dynamic bayesian network. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)—Workshops, San Diego, CA, USA, 20–25 June 2005; p. 70. [Google Scholar] [CrossRef]
  50. Sioni, R.; Chittaro, L. Stress detection using physiological sensors. IEEE Comput. 2015, 48, 26–33. [Google Scholar] [CrossRef]
  51. Schmidt, P.; Reiss, A.; Dürichen, R.; Laerhoven, K.V. Wearable-based affect recognition—A review. Sensors 2019, 19, 4079. [Google Scholar] [CrossRef] [Green Version]
  52. Affanni, A. Wireless sensors system for stress detection by means of ECG and EDA acquisition. Sensors 2020, 20, 2026. [Google Scholar] [CrossRef] [Green Version]
  53. Kocielnik, R.; Sidorova, N.; Maggi, F.M.; Ouwerkerk, M.; Westerink, J.H.D.M. Smart technologies for long-term stress monitoring at work. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, 20–22 June 2013; pp. 53–58. [Google Scholar] [CrossRef]
  54. Yoon, S.; Sim, J.K.; Cho, Y.H. A flexible and wearable human stress monitoring patch. Sci. Rep. 2016, 6, 23468. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Lee, S.W.; Lee, C.Y.; Kwak, D.H.; Ha, J.W.; Kim, J.; Zhang, B.T. Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors. Neural Netw. 2017, 92, 17–28. [Google Scholar] [CrossRef]
  56. Lee, J.H.; Kim, H.; Hwang, J.Y.; Chung, J.; Jang, T.M.; Seo, D.G.; Gao, Y.; Lee, J.; Park, H.; Lee, S.; et al. 3D printed, customizable, and multifunctional smart electronic eyeglasses for wearable healthcare systems and human-machine interfaces. ACS Appl. Mater. Interfaces 2020, 12, 21424–21432. [Google Scholar] [CrossRef] [PubMed]
  57. Khan, Y.; Ostfeld, A.E.; Lochner, C.M.; Pierre, A.; Arias, A.C. Monitoring of vital signs with flexible and wearable medical devices. Adv. Mater. 2016, 28, 4373–4395. [Google Scholar] [CrossRef]
  58. Menghini, L.; Gianfranchi, E.; Cellini, N.; Patron, E.; Tagliabue, M.; Sarlo, M. Stressing the accuracy: Wrist-worn wearable sensor validation over different conditions. Psychophysiology 2019, 56, e13441. [Google Scholar] [CrossRef]
  59. Servati, A.; Zou, L.; Wang, Z.; Ko, F.; Servati, P. Novel flexible wearable sensor materials and signal processing for vital sign and human activity monitoring. Sensors 2017, 17, 1622. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Nisar, M.A.; Shirahama, K.; Li, F.; Huang, X.; Grzegorzek, M. Rank pooling approach for wearable sensor-based ADLs recognition. Sensors 2020, 20, 3463. [Google Scholar] [CrossRef] [PubMed]
  61. JINS Holdings, Inc. JINS MEME Glasses Specifications. Available online: https://jins-meme.com/en/researchers/specifications/ (accessed on 27 March 2021).
  62. Casalino, G.; Castellano, G.; Zaza, G. A mHealth solution for contact-less self-monitoring of blood oxygen saturation. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Rennes, France, 7–10 July 2020; pp. 1–7. [Google Scholar] [CrossRef]
  63. Ishimaru, S.; Kunze, K.; Tanaka, K.; Uema, Y.; Kise, K.; Inami, M. Smart eyewear for interaction and activity recognition. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems CHI EA ’15, Seoul, Korea, 18–23 April 2015; Association for Computing Machinery: New York, NY, USA, 2015; pp. 307–310. [Google Scholar] [CrossRef]
  64. Hou, X.; Liu, Y.; Sourina, O.; Tan, Y.R.E.; Wang, L.; Mueller-Wittig, W. EEG based stress monitoring. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China, 9–12 October 2015; pp. 3110–3115. [Google Scholar] [CrossRef]
  65. Medical University of Silesia. Resolution No. 161/2020 of 08.09.2020 by the Rector of the Medical University of Silesia in Katowice. Available online: http://www3.sum.edu.pl/files/25406/res_161_2020.pdf (accessed on 12 May 2021).
  66. Auksztol, J. (Ed.) Higher Education and Its Finances in 2019; Statistics Poland: Warsaw, Poland, 2020. Available online: https://stat.gov.pl/en/topics/education/education/higher-education-and-its-finances-in-2019,2,13.html (accessed on 19 April 2021).
  67. Umoren, R.A.; Sawyer, T.L.; Ades, A.; DeMeo, S.; Foglia, E.E.; Glass, K.; Gray, M.M.; Barry, J.; Johnston, L.; Jung, P.; et al. Team stress and adverse events during neonatal tracheal intubations: A report from NEAR4NEOS. Am. J. Perinatol. 2019, 37, 1417–1424. [Google Scholar] [CrossRef] [PubMed]
  68. Uema, Y.; Inoue, K. JINS MEME algorithm for estimation and tracking of concentration of users. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2017 ACM International Symposium on Wearable Computers, Maui, HI, USA, 11–15 September 2017; ACM: New York, NY, USA, 2017. [Google Scholar] [CrossRef]
  69. Jia, Y.; Tyler, C.W. Measurement of saccadic eye movements by electrooculography for simultaneous EEG recording. Behav. Res. Methods 2019, 51, 2139–2151. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Friedman, M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 1937, 32, 675–701. [Google Scholar] [CrossRef]
  71. Friedman, M. A correction. J. Am. Stat. Assoc. 1939, 34, 109. [Google Scholar] [CrossRef]
  72. Friedman, M. A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 1940, 11, 86–92. [Google Scholar] [CrossRef]
  73. Shapiro, S.S.; Wilk, M.B. An analysis of variance test for normality (complete samples). Biometrika 1965, 52, 591–611. [Google Scholar] [CrossRef]
  74. Mauchly, J.W. Significance test for sphericity of a normal n-variate distribution. Ann. Math. Stat. 1940, 11, 204–209. [Google Scholar] [CrossRef]
  75. Al-jabery, K.K.; Obafemi-Ajayi, T.; Olbricht, G.R.; Wunsch, D.C., II. Data preprocessing. In Computational Learning Approaches to Data Analytics in Biomedical Applications; Al-jabery, K.K., Obafemi-Ajayi, T., Olbricht, G.R., Wunsch, D.C., II, Eds.; Academic Press: Cambridge, MA, USA, 2020; pp. 7–27. [Google Scholar] [CrossRef]
  76. Doniec, R.J.; Sieciński, S.; Duraj, K.M.; Piaseczna, N.J.; Mocny-Pachońska, K.; Tkacz, E.J. Recognition of drivers’ activity based on 1D convolutional neural network. Electronics 2020, 9, 2002. [Google Scholar] [CrossRef]
  77. Li, F.; Shirahama, K.; Nisar, M.; Köping, L.; Grzegorzek, M. Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors 2018, 18, 679. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Shirahama, K.; Grzegorzek, M. Emotion recognition based on physiological sensor data using codebook approach. In Information Technologies in Medicine; Piętka, E., Badura, P., Kawa, J., Wieclawek, W., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 27–39. [Google Scholar]
  79. Deng, L.Y.; Hsu, C.L.; Lin, T.C.; Tuan, J.S.; Chang, S.M. EOG-based Human–Computer Interface system development. Expert Syst. Appl. 2010, 37, 3337–3343. [Google Scholar] [CrossRef]
  80. García-Nieto, J.; Alba, E.; Apolloni, J. Hybrid DE-SVM approach for feature selection: Application to gene expression datasets. In Proceedings of the 2009 2nd International Symposium on Logistics and Industrial Informatics (LINDI), Linz, Austria, 10–11 September 2009; pp. 1–6. [Google Scholar] [CrossRef]
  81. Kuo, B.C.; Ho, H.H.; Li, C.H.; Hung, C.C.; Taur, J.S. A Kernel-based feature selection method for SVM with RBF Kernel for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 317–326. [Google Scholar] [CrossRef]
  82. Persello, C.; Bruzzone, L. Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning. IEEE Trans. Geosci. Remote Sens. 2016, 54, 2615–2626. [Google Scholar] [CrossRef]
  83. Xu, J.W.; Suzuki, K. Max-AUC feature selection in computer-aided detection of polyps in CT colonography. IEEE J. Biomed. Health Inform. 2014, 18, 585–593. [Google Scholar] [CrossRef] [Green Version]
  84. Fisher, J. The Polygraph and the Frye Case; Deptartment of Political Science & Criminal Justice, Edinboro University of Pennsylvania: Edinboro, PA, USA, 2015; Available online: http://jimfisher.edinboro.edu/forensics/frye.html (accessed on 13 September 2021).
Applsci 11 08648 g001 550
Figure 1. An example of subject setup for the experiment.
Figure 1. An example of subject setup for the experiment.
Applsci 11 08648 g001
Applsci 11 08648 g002 550
Figure 2. Endodontic instruments.
Figure 2. Endodontic instruments.
Applsci 11 08648 g002
Applsci 11 08648 g003 550
Figure 3. Dental burs.
Figure 3. Dental burs.
Applsci 11 08648 g003
Applsci 11 08648 g004 550
Figure 4. Linear acceleration of head during the experiment. Green vertical line denotes the occurrence of the sound signal.
Figure 4. Linear acceleration of head during the experiment. Green vertical line denotes the occurrence of the sound signal.
Applsci 11 08648 g004
Applsci 11 08648 g005 550
Figure 5. Angular velocity of head during the experiment. Black vertical line denotes the occurrence of the sound signal.
Figure 5. Angular velocity of head during the experiment. Black vertical line denotes the occurrence of the sound signal.
Applsci 11 08648 g005
Applsci 11 08648 g006 550
Figure 6. EOG signal during the experiment. Black vertical line denotes the occurrence of the sound signal.
Figure 6. EOG signal during the experiment. Black vertical line denotes the occurrence of the sound signal.
Applsci 11 08648 g006
Applsci 11 08648 g007 550
Figure 7. Total linear acceleration of head during the experiment. Red dashed vertical line denotes the occurrence of the sound signal.
Figure 7. Total linear acceleration of head during the experiment. Red dashed vertical line denotes the occurrence of the sound signal.
Applsci 11 08648 g007
Applsci 11 08648 g008 550
Figure 8. Total angular velocity of head during the experiment. Red dashed vertical line denotes the occurrence of the sound signal.
Figure 8. Total angular velocity of head during the experiment. Red dashed vertical line denotes the occurrence of the sound signal.
Applsci 11 08648 g008
Applsci 11 08648 g009 550
Figure 9. Total EOG signal during the experiment. Red dashed vertical line denotes the occurrence of the sound signal.
Figure 9. Total EOG signal during the experiment. Red dashed vertical line denotes the occurrence of the sound signal.
Applsci 11 08648 g009
Applsci 11 08648 g010 550
Figure 10. Results of Friedman Two-Way Analysis of Variance by Ranks of the differences between the heart rate measured in subsequent phases of the experiment. Q1-first quartile, Q3-third quartile.
Figure 10. Results of Friedman Two-Way Analysis of Variance by Ranks of the differences between the heart rate measured in subsequent phases of the experiment. Q1-first quartile, Q3-third quartile.
Applsci 11 08648 g010
Applsci 11 08648 g011 550
Figure 11. Results of Friedman Two-Way Analysis of Variance by Ranks of the differences between the perceived stress level in subsequent phases of the task. Q1-first quartile, Q3-third quartile.
Figure 11. Results of Friedman Two-Way Analysis of Variance by Ranks of the differences between the perceived stress level in subsequent phases of the task. Q1-first quartile, Q3-third quartile.
Applsci 11 08648 g011
Applsci 11 08648 g012 550
Figure 12. The differences between the power of EOG before the task and during the task.
Figure 12. The differences between the power of EOG before the task and during the task.
Applsci 11 08648 g012
Applsci 11 08648 g013 550
Figure 13. The differences between the power of linear acceleration before the task and during the task.
Figure 13. The differences between the power of linear acceleration before the task and during the task.
Applsci 11 08648 g013
Applsci 11 08648 g014 550
Figure 14. The differences between the power of angular velocity before the task and during the task.
Figure 14. The differences between the power of angular velocity before the task and during the task.
Applsci 11 08648 g014
Table
Table 1. Results of post-hoc tests of the differences between the heart rate measured in subsequent phases of the experiment.
Table 1. Results of post-hoc tests of the differences between the heart rate measured in subsequent phases of the experiment.
Dunn-Bonferroni
ConoverpBefore the taskDuring the taskAfter the task
Before the task <0.001<0.001
During the task<0.001 0.953
After the task<0.0010.030
Table
Table 2. Results of post-hoc tests of the differences between the heart rate measured in subsequent phases of the experiment.
Table 2. Results of post-hoc tests of the differences between the heart rate measured in subsequent phases of the experiment.
Dunn-Bonferroni
ConoverpBefore the taskDuring the taskAfter the task
Before the task <0.0011.000
During the task<0.001 <0.001
After the task0.134<0.001
Table
Table 3. The Spearman’s rank correlation between HR and perceived stress level in subsequent phases of the experiment.
Table 3. The Spearman’s rank correlation between HR and perceived stress level in subsequent phases of the experiment.
HR-Stress LevelBefore the TaskDuring the TaskAfter the Task
r 0.0700.5610.317
p0.3850.0050.087
Table
Table 4. The Spearman’s rank correlation between the power of total linear acceleration (ACC) and perceived stress level.
Table 4. The Spearman’s rank correlation between the power of total linear acceleration (ACC) and perceived stress level.
ACC-Stress LevelBefore the TaskDuring the Task
r−0.0470.179
p0.8440.450
Table
Table 5. The Spearman’s rank correlation between the power of angular velocity (GYRO) and perceived stress level.
Table 5. The Spearman’s rank correlation between the power of angular velocity (GYRO) and perceived stress level.
GYRO-Stress LevelBefore the TaskDuring the Task
r−0.1700.312
p0.4750.181
Table
Table 6. The Spearman’s rank correlation between the power of EOG and perceived stress level.
Table 6. The Spearman’s rank correlation between the power of EOG and perceived stress level.
EOG-Stress LevelBefore the TaskDuring the Task
r−0.3010.300
p0.1970.199
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Mocny-Pachońska, K.; Doniec, R.J.; Sieciński, S.; Piaseczna, N.J.; Pachoński, M.; Tkacz, E.J. The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students. Appl. Sci. 2021, 11, 8648. https://doi.org/10.3390/app11188648

AMA Style

Mocny-Pachońska K, Doniec RJ, Sieciński S, Piaseczna NJ, Pachoński M, Tkacz EJ. The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students. Applied Sciences. 2021; 11(18):8648. https://doi.org/10.3390/app11188648

Chicago/Turabian Style

Mocny-Pachońska, Katarzyna, Rafał J. Doniec, Szymon Sieciński, Natalia J. Piaseczna, Marek Pachoński, and Ewaryst J. Tkacz. 2021. "The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students" Applied Sciences 11, no. 18: 8648. https://doi.org/10.3390/app11188648

APA Style

Mocny-Pachońska, K., Doniec, R. J., Sieciński, S., Piaseczna, N. J., Pachoński, M., & Tkacz, E. J. (2021). The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students. Applied Sciences, 11(18), 8648. https://doi.org/10.3390/app11188648

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop