The Body’s Automatic Stress Detection Process Relies on

Abstract

The negative effects of mental stress on human being wellness has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a pace outside the laboratory surroundings to the real-life. We adult an automated stress detection system using physiological signals obtained from unobtrusive smart wearable devices which tin can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for existent-life conditions. We farther tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for ix days. This event had lectures, contests too equally gratis time. Past using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cerebral load (lecture) and relaxed fourth dimension activities past using different machine learning methods.

Keywords:

stress recognition, machine learning, wearable sensors, smartwatch, photoplethysmography, electrodermal activeness, daily life psychophysiological data, heart charge per unit variability

1. Introduction

Daily life stress is an important problem of our modernistic society. It is a growing issue and it has become an unavoidable part of our daily lives. Psychological stress types can be listed every bit astute and chronic [1]. Acute stress is more prevalent than chronic stress. American Psychological Clan noted that the causes of acute stress are pressure from contempo past and nearly future [ii]. Able-bodied challenges, examination taking, or anxiety when meeting new people can induce acute stress. On the other hand, long-standing pressures and demands as a consequence of socioeconomic conditions, difficulties in interpersonal relationships, or an unsatisfying career tin trigger chronic stress [2]. If chronic stress is not handled properly, it could result in serious health issues [3]. Since symptoms of acute stress are more apparent than chronic stress symptoms, acute stress is more widely investigated.

After musculoskeletal illnesses, which also could exist stress-related in some cases [four], stress is one of the most significant wellness bug in the world. The effect of stress on man health depends on the stress type. Emotional distress, muscular ache and tension, back pain, headache, heartburn, digestive tract issues, and overarousal tin be named as the effects of acute stress [5]. Overarousal can cause eye attacks, arrhythmias, and even sudden death for people with eye conditions [6]. Effects of the chronic stress on human wellness are akin to those of acute stress even so it can damage physical conditions more. Possible causes of the chronic stress tin can be listed equally hypertension and coronary disease [six,vii], irritable bowel syndrome, gastroesophageal reflux affliction [eight], generalized anxiety disorder, and depression [ix]. The above-mentioned stress-related diseases also affect the economy by increasing absenteeism, staff turnover [x], presenteeism, and tardiness. These issues decrease the product and increment the piece of work-related costs. Public surveys [11] unveiled that at least half of the European workers are subjected to stress at work. Furthermore, at least half of the lost working days in the business sector are causeless to be acquired past piece of work-related stress and psycho-social risks [12].

Researchers plant out that stress should be handled when the symptoms first come out to avoid the long-term consequences. In other words, stress must be discovered in early stages to refrain from more damages and impede information technology from being chronic. The higher up-mentioned damages of stress on homo health and detriments to social life and economy have forced researchers to come up with an automated stress monitoring scheme which exploits smart article of clothing devices and advanced affective computing algorithms. This scheme can be applied in automobiles, airplanes, factories, and offices, at job interviews and daily life environments. This scheme tin can further compute social stress stages during meetings or mutual intercommunication. The platonic scheme should exist applicable to daily life, i.east., it should use unobtrusive sensors and devices which users tin article of clothing easily in their daily routines.

In this work, we adult an automatic stress level detection scheme that uses physiological signals from wrist-worn devices. Our scheme can also exist applied to daily life of individuals. In real-life settings, movements of individuals are unrestricted and artifacts occur because of that. In society for our organization to be applicable in these settings, we practical several novel artifact detection and removal strategies. These antiquity detection algorithms are developed for specific sensors and their performances are scientifically proven. We further extracted features from heart activity, skin conductance, and accelerometer signals with our tools. From these features, we classified the stress level of an individual by employing auto learning algorithms. To test our organisation in real-life settings, we collected physiological signals of participants in an algorithmic programming summer army camp via smart wrist-worn habiliment devices. This military camp was composed of lecture, contest, and complimentary time sessions. We nerveless data for nine days from 21 participants. After the data were collected, we obtained promising results for detecting stress with these habiliment devices in real life scenarios. Our work addresses five prominent enquiry issues:

• The comparison of stress detection model performances with dissimilar wearable devices;

• The influence of the interpolation, aggregation window sizes and artifact detection threshold percentages;

• Change in the functioning of the stress detection scheme with known context labels and the subjective reports as the ground truths;

• The discriminative effect of each sensor modality; and

• The performance of person-specific and full general models.

The structure of the rest of the paper is every bit follows: In Section 2, the related work for stress detection is provided. Real-life data collection bug are addressed in Section 3. In Section 4, our stress detection scheme is explained. Data drove result and our experiment design are presented in Section 5. In Section half-dozen, we present experimental results and discussion. The decision of the study and futurity work are given in Section 7.

ii. Related Piece of work

The early stress detection research was performed in the laboratory environments, while the current research continues on real-life environments (see
Table 1). Electrodermal action (EDA), centre activeness (60 minutes) and accelerometer are the most widely used physiological signals for the detection of stress levels. As shown in
Table one, EDA and HR combination has the best performances in the laboratory environments. Proposals with accuracies higher than 95% employ this combination every bit the physiological signals. Linear Discriminant Analysis (LDA), Support Vector Car (SVM), g Nearest Neighbors (kNN) and Fuzzy Logic classifiers are the best performing machine learning (ML) algorithms. An 89% accurateness was achieved in four-class stress classification by using EEG signals in [13]. However, current EEG (Electroencephalogram) measuring devices are obtrusive for individuals and they are non applicable to daily life routines.

Almost all of the studies in
Tabular array i
employed a ii-class stress level classification. However, the stress upward to a sure level might be harmless. After a sure limit, the stress level should be detected and precautions taken. To this end, stress detection resolution must be increased (precision of detected stress levels should be increased) and multi-level stress detection systems with high classification functioning must exist adult. These schemes should further take advantage of multimodality to increase accuracies as the laboratory enquiry suggest the benefits (see
Tabular array i).

It is recognized that the stress level that subjects endure in this environment is dissimilar from real life stress [1]. Information technology is as well demonstrated that subjects are reluctant to wear obtrusive instruments for measurement and they are not comfortable with these devices. For these reasons, stress measurement research has taken a step outside the lab with the aim of developing an unobtrusive multi-level stress detection system for daily life. Since smartphones and wearable devices have become an integral function of our lives in our modern society, they are chosen equally the instruments for stress detection in daily lives research.

Afterwards laboratory environments, stress level detection research has been conducted in restricted and semi-restricted environments such every bit office, automobile and university campus. Part and workplace are amidst the environments which increase the stress levels the most. The stress level in the office environs is monitored past using EDA, ECG (Electrocardiogram) and Accelerometer [36,37,38]. Peculiarly in crowded cities, the stress levels of individuals increase in traffic jams. At that place are a number of studies in the automobile environments in the literature. Most of the studies used DriveDB database [39]. This database consists of ECG, EDA, EMG (Electromyogram) and respiratory sensor information collected from 24 drivers in Boston. Researchers applied automobile learning algorithms to this database and EDA-ECG signal combination and SVM-kNN classifiers accomplished the best performance [forty] in this environment. Campus environments are semi-restricted environments and the well-nigh similar surround to unrestricted daily life environment. Therefore, nomenclature performances are lower when compared with restricted laboratory, function and automobile environments. ECG betoken and the conclusion tree classifier have achieved the highest classification accuracy in ii-form classification in a campus environment [41]. Most works have only used features extracted from the smartphones [42,43,44]. Smart habiliment devices are not used in the campus environment in well-nigh of the works.

The stress detection research has taken a step to the unrestricted existent life since the ultimate aim is to detect stress levels of individuals in their daily routines. However, researchers should come up up with solutions to new problems ascend when taking a pace exterior the laboratory (see Section three). The stress level recognition performances of existent-life schemes are lower than restricted environments and laboratory environments [32] (2016), ref. [45] (2015), ref. [34] (2017) and [35] (2018). The listed works have classification accuracies around 70% and lxxx%. Low reliability of self-report answers, the unknown context of participants and unrestricted movements of subjects could be the main reasons. Furthermore, the devices used in real-life studies are non-obtrusive but their information quality is not comparable with their laboratory counterparts. There are a lot of smart unobtrusive wearable devices for daily life usage. Even so, their data quality and effect on stress level detection performance are non investigated thoroughly. Another open enquiry question for real-life studies is the unknown context and low reliability of self-reports. Gjoreski et al. [34] employed activity recognition to increment the knowledge regarding context and improve their recognition performance. The effect of context and questionnaires to the performance of stress recognition systems should exist investigated comprehensively. Lastly, to eliminate the negative effects of unconstrained movements, antiquity detection and removal algorithms specific for each sensor must be adult and used.

3. Preprocessing and Characteristic Extraction: Problems and Possible Solutions in Real Life

Existent-life data collection brings new problems that are not encountered in laboratory data collection. In a lab experiment, methods for data collection are less error-prone and relatively easy. In real life scenarios, new parameters that can cause new research issues are added to the organization. For example, the maximum runtime of the devices is express due to their express battery. Wrong placement of devices, loosely worn equipment, charging of instruments, unconstrained movement of subjects and issues with the ground truth collection should exist taken into business relationship.

iv. Proposed Arrangement Description

In plethysmography, volumetric changes of organs are measured from the skin illuminated by the light emitted from a pulse oximeter PPG [47]. PPG sensors in our devices are used to measure the center activity past measuring blood menses during the middle’s pumping deportment. Heart activity betoken is composed of dissimilar peaks and valleys. R peak is the about prominent i, which is used to calculate heart rate variability. PPG provides the RR interval by measuring the elapsing between ii consecutive R peaks which tin can also be chosen as Interbeat Interval (IBI).

EDA also known as Galvanic Skin Response (GSR), is the change of electrical properties of peel. Under emotional arousal and stress, trunk sweats and skin conductance increases. EDA is 1 of the all-time and widely used discriminative point along with the eye charge per unit signal for measuring stress [48]. Mean aamplitude, standard departure, minimum and maximum values, RMS, the filibuster between applied stimuli and response, number of peaks, peak meridian, ascent time, recovery time, the position of maximum and minimum features were used in the literature to measure out the stress levels of the user [49].

In this study, we developed a multi-level stress detection system, which employed heart activity information from the PPG sensor, skin conductance data from the EDA sensor and accelerometer and temperature information. Our EDA preprocessing tool uses accelerometer and temperature signals to clean the artifacts in this signal. We further extracted features from the accelerometer sensor but temperature information were not used for characteristic extraction. The increment in the centre rate and electrodermal activeness levels can be seen in
Figure 1. Preprocessing and feature extraction tools for each modality were developed. For each sensor, modality-specific tools were applied to eliminate artifacts, cleaning signals and extracting features. After the characteristic extraction, the virtually successful machine learning algorithms in the literature were applied to the physiological information for the classification task. Our system is compatible with different smart wrist-worn wearables in spite of the fact that they have unlike platforms and sensors. System diagrams for Samsung Gear South family devices and Empatica E4 devices are shown in
Figure 2. Note that all parameters for artifact detection and preprocessing algorithms are universal and person contained.

Recorded physiological signals before and after the start of the stimuli. The increment in EDA point level and number of peaks and irregularities and sudden increases in HRV can be seen in this figure.

The cake diagram of the stress level detection system for Samsung Gear Due south and S2 and Empatica E4. Since the sensors and platforms are different, please notation that EDA and temperature signals are just bachelor for E4.

4.1. Electrodermal Activity Signal Preprocessing and Feature Extraction Tools

four.ane.ane. Preprocessing and Artifact Removal

Electrodermal Activeness bespeak is afflicted past increased physical activity and temperature changes. In these situations, obtained signal is contaminated and should be filtered. To this cease, we employed the EDA Explorer tool from Taylor et al. [l]. The artifacts in the EDA indicate is manually labeled by the experts to train a auto learning model. Past applying the SVM (Support Vector Motorcar) classifier with the accelerometer and temperature information, this tool achieves 95% accurateness on detecting artifacts in the EDA signals (encounter
Figure 3
and
Figure 4). We added batch processing feature to this tool. If a data segment is detected every bit an antiquity segment, it is excluded in the characteristic extraction process. By this style, we eliminated false peaks caused by increased temperature or physical activeness when extracting features.

The case filtered EDA indicate according to changes in the accelerometer indicate. Annotation that red components were deleted because of the high activity intensity.

Activity intensity is shown past using the accelerometer sensor X, Y, and Z components corresponding to the example EDA signal in
Figure 3
Note that this case was recorded during a highly intensive activity.

4.1.2. Characteristic Extraction

After cleaning artifacts from the signals, features were extracted. EDA indicate has two components: phasic and tonic. Nosotros decomposed the EDA indicate by applying the
cvxEDA
tool [51] on the EDA bespeak, which makes use of a convex optimization approach to decompose the EDA signal. Skin Conductance Level (Tonic) component includes more long-term ho-hum changes, whereas phasic components include faster (event-related) changes. When evaluating the hateful, standard deviation, and percentile features, researchers use the tonic component because they do non desire to overestimate these long-term changes with consequence-related fast changes. The phasic part is subtracted and features are calculated. On the other mitt, some peak related features such equally peaks per 100 south, superlative aamplitude, and potent peaks (peaks that are more 1

μ

Siemens) per 100 s are calculated from the phasic element. An example of a decomposed signal is shown in
Figure v. After that, we extracted seven features from the EDA signal: mean, standard deviation, superlative, strong height, 20th percentile, 80th percentile and quartile deviation (75th percentile–25th percentile).

Decomposed EDA Signal from Empatica E4 wristband by applying cvxEDA tool.

4.ii. Center Activity Point Preprocessing and Feature Extraction Tools

four.2.1. Preprocessing and Artifact Removal

The heart rate activity signal is too sensitive to the movement of the subjects and loosely worn wrist devices. To cope with these bug and make clean the artifacts from the indicate, our enquiry grouping adult a preprocessing tool in MATLAB. With this tool, we employed an artifact detection percent threshold between the information and the local average. In the literature, this threshold is by and large prepare every bit xx% [38] and nosotros as well used this threshold. After nosotros detected the artifacts in the center activity point, a user can cull to remove and apply some additional constraints or supersede them with shape preserving cubic spline interpolation after removal (see
Figure 6). If the artifact data points are removed and non-interpolated, new rules can be ready on the remaining healthy information. A minimum corporeality of sequent information samples and minimum consecutive fourth dimension rules can exist fix to evaluate the remaining segments. These rules are used to exclude interrupted (with holes of removed data) small amount of consecutive data in the feature extraction process. We applied removal with extra exclusion rules and removal and interpolation separately and observed their effect on the functioning of our system.

Gaps due to movement and loosely worn wristband from PPG (Photoplethysmography) data (Left) are filled with cubic interpolation function (Right).

The tool also has a batch processing feature. Length of the local hateful, the pct of artifact detection threshold, minimum consecutive time and data sample constraints can exist altered with parameters.

four.2.2. Characteristic Extraction

For feature extraction, nosotros used MATLAB born tools forth with Marcus Vollmer’s HRV toolbox [52] along with our preprocessing tool. The employed time domain features are the hateful value of the heart rate (Mean 60 minutes), the standard deviation of inter-beat interval (IBI), hateful value of the inter-beat (RR) intervals (Mean RR), root mean square of successive difference of the RR intervals (RMSSD), the percentage of the number of successive RR intervals varying more than than 50 ms from the previous interval (pNN50), the full number of RR intervals divided by the peak of the histogram of all RR intervals measured on a scale with bins of 1/128 s (HRV triangular index), and triangular interpolation of RR interval histogram (TINN).

We as well applied Fast Fourier Transform (FFT) and Lomb–Scargle periodogram [53] and the post-obit frequency domain features are calculated: low frequency ability (LF), loftier frequency power (HF), very depression frequency power (VLF), prevalent low frequency (pLF), prevalent loftier frequency (pHF), the ratio of LF to HF (LF/HF), (From Lomb–Scargle) LF, HF, and LF/HF. Definitions of these features are given in
Table 2.

Tabular array two

Heart charge per unit variability features and their definitions.

Feature	Description
Mean RR	Mean value of the inter-crush (RR) intervals
STD RR	Standard divergence of the inter-beat interval
RMSSD	Root mean square of successive difference of the RR intervals
pNN50	Percentage of the number of successive RR intervals varying more than 50 ms from the previous interval
HRV triangular alphabetize	Full number of RR intervals divided by the height of the histogram of all RR intervals measured on a calibration with bins of 1/128 southward
TINN	Triangular interpolation of RR interval histogram
LF	Power in low-frequency ring (0.04–0.15 Hz)
HF	Power in high-frequency ring (0.15–0.4 Hz)
LF/HF	Ratio of LF-to-HF
pLF	Prevalent low-frequency oscillation of heart rate
pHF	Prevalent high-frequency oscillation of heart rate
VLF	Power in very low-frequency ring (0.00–0.04 Hz)
SDSD	Related standard divergence of successive RR interval differences

5. Description of the Data Collection Event: Algorithmic Programming Summer Camp

To examination and evaluate our system in real-life settings, we conducted a information drove experiment in the INZVA algorithmic programming contest summer military camp, which is organized each year in Istanbul, Turkey [56]. This event is similar to the International Collegiate Programming Contest (ICPC) [57]. A photograph from the information collection setup is shown in
Figure 7. Algorithmic programming camp is designed for loftier-school and university students to ameliorate their programming skills and this contest will induce stress on the participating students.

A view of smartwatches and wristbands after data extraction, charged and ready to use.

The algorithmic programming contest is conducted in iii levels, expert, advanced and foundation. 80-4 students with unlike levels of expertise gathered to participate in this algorithmic programming contest. Algorithmic programming competition camp was held for nine days. The physiological indicate and questionnaire data were collected from the 21 participants at the foundation level. Of these 21 people, 18 were men and 3 female person and the average age of the attendees was 20.

There were three types of sessions such as the grooming, the contest and the gratis day. The program was scheduled to be held from 10:00 to 17:00 for the whole nine days. The data collection schedule presented in
Figure 8
was repeated for the get-go 8 days. In those days, attendees had training classes with professors from the field of computer science and computer engineering science from high-ranking universities in Turkey for two hours. The participants entered daily trouble-solving contests in which the questions were derived from the aforementioned days of training lectures. In all sessions, raw NASA-TLX questionnaires were collected from the users. The frustration question of this questionnaire was used to measure the perceived stress levels of the individuals. On the endmost day, the participants entered the final contest and they should solve challenging questions, which were asked from all the topics covered in the total 8 days of training. Participants had to solve challenging questions within a time limit and the scores of the participants were projected on a wall, which created extra stress for the participants. To collect more points, participants should solve more than questions in a shorter fourth dimension period than their opponents. As a result, the mental demand likewise as the temporal demand was increased, which encouraged the participants to gain more points in a shorter time and to achieve a higher position in the final ranking. When a participant solved a question, a balloon was fastened to his/her table.

The daily schedule and information drove process during the algorithmic programming contest.

vi. Discussion of Experimental Results

We adult a iii-grade stress detection arrangement. The system can differentiate the stress level of the free twenty-four hours, lecture and competition sessions. It can further differentiate three levels of perceived stress (come across Section 6.5). Besides from Department half dozen.v, nosotros used the context label as the ground truth and we chosen the measured stress as physiological stress. Free day is enumerated equally 0, lecture is assigned 1 and contest is assigned 2 labels. We causeless that the stress levels of most of the subjects would be college in contest, medium in lecture and lower in the free fourth dimension with this context labels. In Section 6.5, the frustration scale of raw NASA-TLX is further used equally a ground truth and performance of the arrangement is compared under these 2 conditions. With the latter ground truth, perceived stress level of individuals from cocky-reports was measured.

6.2. Event of Device Type

Nosotros compared the result of using Empatica E4 and Samsung Gear South-S2 devices (Combination of Samsung Gear S and S2 devices) relative to each other. Samsung Gear Southward-S2 devices are a commercial type, relatively cheaper smartwatches. On the other hand, Empatica E4 is a more precise, relatively more expensive research device. Nosotros compared the nomenclature accuracies and data quality on both of these devices.

In the literature, RR intervals that differ more than than 20% of the local average are removed [38]. This is called the antiquity detection with a percent threshold. We changed the value of this threshold from ten% to 25% and observed the amount of remaining clean data. As shown in
Figure ix, Empatica E4 devices have approximately 25% more remaining information for all of the dissimilar artifact detection percentage thresholds. We deduced that the quality of RR intervals of Empatica E4 devices is higher than those of the Samsung Gear Southward-S2 devices. We farther investigated the effect of data quality on stress level nomenclature accuracies. We observed that nomenclature accuracies obtained from the data collected with Empatica E4 were higher than those from Samsung devices with all classifiers, as shown in
Tabular array 7
and
Tabular array 8. From these results, we tin can observe that the information quality has a significant effect on the stress level classification accuracy.

Percentage of the remaining information (for both device types) later on the artifacts are removed versus different pct thresholds of artifact detection.

Table 7

Consequence of the used device to three-class stress with context nomenclature accuracy when heart activity and accelerometer data are used together.

Algorithm	Empatica E4	Samsung Gear S-S2	All Devices
PCA + LDA	88.88	72.sixty	59.12
PCA + SVM (rad)	92.06	78.60	76.91
kNN	87.30	85.30	87.30
Logistic Regression	90.47	83.30	65.25
Random Forest	90.40	88.threescore	88.30
Multilayer Perception	95.23	87.30	83.ten

Table viii

Effect of the device used to iii-course stress level classification accuracy when only heart activity signal is used (without context).

Algorithm	Empatica E4	Samsung Gear S-S2	All Devices
PCA + LDA	65.07	55.33	52.58
PCA + SVM (rad)	90.xl	73.33	62.60
kNN	88.88	82.00	82.15
Logistic Regression	84.90	66.66	66.66
Random Forest	87.30	84.67	82.62
Multilayer Perception	88.88	78.00	71.36

7. Conclusions

We developed a stress detection scheme to be used in real life. Since our system employs unobtrusive wearable devices, it can hands exist used in the daily life of individuals. It can track the stress levels in existent-time and intervene if an extreme level of stress is detected. After the detection, some stress management methods can besides be offered to convalesce the high level of stress. We nerveless data from participants of an algorithmic programming competition to evaluate the operation of our system. We obtained labeled sessions for 21 subjects for nine days. Nosotros described the difficulties of existent-life data collection, which do not occur in laboratory environments. After describing our algorithms, we presented the results. For three-grade stress level detection, we obtained 90.40% accuracy by using Empatica E4 devices with high information quality, whereas the accuracy with Samsung S devices was 84.67%. We tin deduce from the results that the data quality of the devices increases the stress level classification accuracies. When compared with other real-life studies in
Table 1, our system has higher accuracies fifty-fifty for the results obtained with the three-grade classification. Later examining the upshot of different preprocessing methods and parameters, we can infer that their effect depends on the chosen ML algorithm. Researchers should select these methods past considering their performance with selected ML algorithms. Furthermore, person-specific models have always higher classification accuracies than full general models. We accomplished maximum 97.92% accurateness for 3-level stress detection with our person-specific models. On the other hand, nosotros obtained a maximum 88.20% classification accuracy with our general models. When physiological data from each person are sufficient for developing person-specific models, they should be applied. Otherwise, people should be clustered co-ordinate to their stress behaviors and models can be developed for clusters to increase the operation of general models. The best performing classifiers were the Random Woods and the Multilayer Perceptron algorithms. These classifiers outperformed other algorithms in almost cases. Another significant finding is that the combination of modalities increases the performance of our organisation. When we combined center activity with electrodermal activeness, we obtained 92.15% maximum 3-level classification accuracy, whereas this was 86.27% when these modalities were used separately. Finally, we observed that the perceived stress level classification results in lower accuracies than physiological stress level nomenclature. At that place were up to fifteen% decrease when compared with physiological stress level nomenclature accuracies. The possible causes of this subtract could be listed equally subjectivity and fallibility of cocky-report answers and the possible difference of physiological and psychological responses of individuals to the same stressor. Every bit a futurity piece of work, we plan to record data with an increased number of loftier-quality Empatica E4 devices. Nosotros further plan to develop personalized perceived stress level models from basis truth surveys and remove outlier answers to increase the perceived stress level classification accuracies.

Acknowledgments

We would like to show our gratitude to INZVA for providing us the opportunity for the data collection in their summertime camp.

Writer Contributions

Y.South.C., N.C. and D.E. contributed equally to this work in design, implementation, field study, information analysis and writing the manuscript. C.Due east. provided invaluable feedback and technical guidance to translate the design and details of the field study. C.E. also performed comprehensive disquisitional editing to increase the overall quality of the manuscript.

Funding

This work was supported by AffecTech: Personal Technologies for Affective Wellness, Innovative Training Network funded by the H2020 People Programme nether Marie Skłodowska-Curie grant agreement No. 722022. This work was supported by the Turkish Directorate of Strategy and Upkeep under the TAM Project number DPT2007K120610.

Conflicts of Involvement

The authors declare no conflict of involvement.

References