Fig. Correspondence to Recorded physiological signals before and after the start of the stimuli. Use Git or checkout with SVN using the web URL. North American Journal of Fisheries Management 13, 217222 (1993). Stress detection and classification from physiological data is a promising direction towards assessing general health of We achieved maximum 97.92% accuracy for three-level stress detection with our person-specific models. We are experimenting with display styles that make it easier to read articles in PMC. The frequency of these signals ranges from 1 Hz for the Heart Rate to 64Hz for the BVP. Hence, the fake news detection has been a crucial but difficult task so far, due to the lack of comprehensive labeled datasets and the diverse linguistics cues in the fake news statements. In order for our system to be applicable in these settings, we applied several novel artifact detection and removal strategies. Scarpina, F. & Tagini, S. The stroop color and word test. and K.C. volume9, Articlenumber:255 (2022) The average EDA is higher in stressful situations for some participants. Our EDA preprocessing tool uses accelerometer and temperature signals to clean the artifacts in this signal. In other words, stress must be discovered in early stages to refrain from more damages and impede it from being chronic. In these situations, obtained signal is contaminated and should be filtered. Furthermore, person-specific models have always higher classification accuracies than general models. 38053808. We also chose to spread the events across the nurses work shifts since this is more likely to reduce recall conflicts. No incentives were offered. WESAD is a publicly available dataset for wearable stress and affect detection. Liapis A., Katsanos C., Sotiropoulos D., Xenos M., Karousos N. Stress Recognition in Human-computer Interaction Using Physiological and Self-reported Data: A Study of Gender Differences; Proceedings of the 19th Panhellenic Conference on Informatics; Athens, Greece. Frontiers in psychology 8, 557 (2017). With the rapid digitalization leading to text-based forms of communication gaining dominance over spoken ones, there is now the chance to develop analytical studies for stress detection directly from textual inputs in social media. Every problem creates multiple options for researchers. Signal Processing 167, 107299 (2020). In real-life settings, movements of individuals are unrestricted and artifacts occur because of that. For the classification of the data, we employed the Weka toolkit [55]. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. While earlier studies indicated that Heart Rate and Heart Rate variability are good features to use for stress detection42,43, this is not evident in our analysis. The conclusion of the study and future work are given in Section 7. 345350. International journal of environmental research and public health 12, 652666 (2015). We deduced that the quality of RR intervals of Empatica E4 devices is higher than those of the Samsung Gear S-S2 devices. Pekka et al.41, have shown that the stress detection models are personalized signals and features, and their importance in the machine learning models has to be computed at the user level. We observed that as the window size increases, the accuracy of stress detection decreases. We also provide the unlabelled data because we suspect that it may contain predictive markers of stress that future analyses may reveal. A view of smartwatches and wristbands after data extraction, charged and ready to use. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. [. [34] (2017) and [35] (2018). This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. On the other hand, Empatica E4 is a more precise, relatively more expensive research device. 2) A universal stress model can be robustly adapted to specic individual users, thereby increasing the accuracy across population of users. In laboratory experiments, researchers know the ground truth such as relaxed, baseline, and stressed because they designed the experiment timeline. 2223 December 2014; pp. While the latency may have produced some degree of recall bias, it is still an improvement over traditional surveys, which do not specify the precise time of stress events and are conducted at monthly or quarterly intervals24. Each folder contains raw data signals in CSV format in a sub-folder. Neuropsychobiology 28, 7681 (1993). We observed that classification accuracies obtained from the data collected with Empatica E4 were higher than those from Samsung devices with all classifiers, as shown in Table 7 and Table 8. You may switch to Article in classic view. Emotional distress, muscular ache and tension, back pain, headache, heartburn, digestive tract issues, and overarousal can be named as the effects of acute stress [5]. Brown, S., Whichello, R. & Price, S. The impact of resiliency on nurse burnout: An integrative literature review. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. EDA data. We further extracted features from the accelerometer sensor but temperature data were not used for feature extraction. We developed a three-class stress detection system. ; Formal analysis: S.H. Earlier studies relied on simulated data in laboratory settings where the subjects were in stationary/sedentary positions. We decomposed the EDA signal by applying the cvxEDA tool [51] on the EDA signal, which makes use of a convex optimization approach to decompose the EDA signal. While the nurses were instructed to use the E4 buttons to indicate the situations where they were stressed, none of the nurses actually used them. In: Lake C., Hines R., Blitt C., editors. After months of search I found only three datasets for stress classification that contained EDA data from Empatica E4 wrist-band. The accelerometer sensor records three-axis acceleration with gravity. The participants subjective experience on task load, mental effort, emotion, and perceived stress was assessed with validated questionnaires as a ground truth. Short video clips were shown to elicit specific emotions: sad, neutral, and happy and a 20 seconds gray screen was shown to let the participants rest. The data collected here can be useful for nursing, machine learning, and hospital management communities. However, the stress up to a certain level might be harmless. This event had lectures, contests as well as free time. The researcher downloads data from the Empatica server to inspect the data for any losses to run the machine learning model. The stress level recognition performances of real-life schemes are lower than restricted environments and laboratory environments [32] (2016), ref. [(accessed on 18 April 2019)]; Costin R., Rotariu C., Pasarica A. Table2 shows the data description. International journal of nursing studies 43, 875889 (2006). PubMedGoogle Scholar. After the nursing department expressed interest, the research team obtained approval from hospital compliance. 2 shows screenshots of the data collection app. Provided by the Springer Nature SharedIt content-sharing initiative, Scientific Data (Sci Data) Official websites use .govA The following sensor modalities are included: blood volume pulse, electrocardiogram, electrodermal activity . and JavaScript. Further, this study was conducted during the COVID-19 outbreak. Hosseini, S., Gottumukkala, R., Katragadda, S. et al. Electrodermal activity (EDA), heart activity (HR) and accelerometer are the most widely used physiological signals for the detection of stress levels. European Opinion Poll on Occupational Safety and Health. Greco A., Valenza G., Lanata A., Scilingo E.P., Citi L. cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing. These schemes should further take advantage of multimodality to increase accuracies as the laboratory research suggest the benefits (see Table 1). In this work, we developed an automatic stress level detection scheme that uses physiological signals from wrist-worn devices. After examining the effect of different preprocessing methods and parameters, we can infer that their effect depends on the chosen ML algorithm. Empatica E4 was used to record the skin conductance and heart rate. When to integrate the data (before classification or during processing) and missing data from some modalities are other challenges. We applied artifact correction percentage thresholds from 10% to 25% and investigated the stress level classification accuracies, as shown in Table 9. Note that red components were deleted because of the high activity intensity. We consulted with the nurses ahead of the study to ensure that the stress labels were meaningful to them. Journal of nursing management 23, 346358 (2015). 1517 July 2015; pp. Better guidance and interventions towards mitigating the impact of stress can be provided if stress can be monitored continuously. Based on visual inspection of the plot, stress has a positive correlation with the EDA. Mirjafari, S. et al. The stress detection algorithm uses a Random Forest model to identify epochs of potential stress. & Fiksenbaum, L. Workload and burnout in nurses. On the right side, context information with accelerometer data is also added. stress-ml . This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Yekta Said Can, Niaz Chalabianloo, [], and Cem Ersoy. They also had the option to report additional time slots where they experienced stress, along with the stress level and contributors of stress. C.E. A public dataset is a dataset available to anyone who would like to access it without having to request permission to use it. To obtain The Heart Rate and Heart Rate variability signals can be derived from the BVP signal by computing the inverse of the time between two successive peaks. Private Datasets. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 16 (IEEE, 2020). Clustering using GMM and classifying using GMM classifier, These signals and any tags input by the nurses are transmitted to the machine learning based stress detection model. Get the most important science stories of the day, free in your inbox. Training Random forests for classification, Different modalities: Chest vs. Wrist Although, no one used the watch to report additional stress events. When we combined heart activity with electrodermal activity, we obtained 92.15% maximum three-level classification accuracy, whereas this was 86.27% when these modalities were used separately. Human Stress Detection in and through Sleep by monitoring physiological data. University of Louisiana at Lafayette, Lafayette, LA, USA, Seyedmajid Hosseini,Raju Gottumukkala,Satya Katragadda,Ravi Teja Bhupatiraju,Ziad Ashkar,Christoph W. Borst&Kenneth Cochran, Opelousas General Health System, Opelousas, LA, USA, You can also search for this author in We also seek to allow exploration of physical activity on artifact generation. Raju Gottumukkala. In the literature, this threshold is generally set as 20% [38] and we also used this threshold. Gjoreski, M., Gjoreski, H., Lutrek, M. & Gams, M. Continuous stress detection using a wrist device: in laboratory and real life. Benchmarks Add a Result. Vanitha V., Krishnan P. Real Time Stress Detection System Based on EEG Signals. This project is funded by NSF Grants 1650551, CNS-1429526, and by the Louisiana Board of Regents Support Fund contract LEQSF (201920)-ENH-DE-22. Figs. Journal of occupational health 170011 (2017). This dataset contains sample data collected from 15 persons which contain details acquired from devices on the wrist and chest. Effects of the chronic stress on human health are akin to those of acute stress however it can damage physical conditions more. Stress is detected using well-known physiological signal features and standard machine learning methods to create a baseline on the dataset. Journal of biomedical informatics 59, 4975 (2016). Methods for evaluating psychological stress detection include self-report questionnaires and interviews. The negative effects of mental stress on human health has been known for decades. [. In our case, since we knew the context for all times, adding of accelerometer features to the feature vector might be trivial and these features increased the performance of our system. Mohd M.H., Kashima M., Sato K., Watanabe M. Mental stress recognition based on non-invasive and non-contact measurement from stereo thermal and visible sensors. Three different classifications of stress are performed, low stress, normal stress, and high stress. In the event of a loss of network connectivity, data is buffered (stored temporarily) on the phone and uploaded when the connection is restored. In: 2015 22nd Iranian Conference on Biomedical Engineering (ICBME). If the nurses agreed that there was stress and it was not covered by any stress label, it would be used to describe the stress event. ISSN 2052-4463 (online). The installation of the mobile application on the phone proved to be inconvenient as the application was not available on the App store and needed to be sideloaded onto the subjects phone. HR +ACC for Empatica E4. Incorrect placement of devices, loosely worn equipment, charging of instruments, unconstrained movement of subjects and issues with the ground truth collection should be taken into account. found that the aggregation window lengths between 10 min and 17.5 min have better accuracy in general [34], which is similar to our results. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (, stress recognition, machine learning, wearable sensors, smartwatch, photoplethysmography, electrodermal activity, daily life psychophysiological data, heart rate variability, Activity intensity is shown by using the accelerometer sensor X, Y, and Z components corresponding to the example EDA signal in, Gaps due to movement and loosely worn wristband from PPG (Photoplethysmography) data (, Stress detection accuracies with different ML algorithms: three-class classification. Ryvlin P., Nashef L., Lhatoo S.D., Bateman L.M., Bird J., Bleasel A., Boon P., Crespel A., Dworetzky B.A., Hgenhaven H., et al. 1923 March 2012; pp. Gelsema, T. I., Van Der Doef, M., Maes, S., Akerboom, S. & Verhoeven, C. Job stress in the nursing profession: The influence of organizational and environmental conditions and job characteristics. These thresholds were adopted based on the AffectiveRoad datasets5 and were further validated using the survey feedback during the initial phase of the study. 11851193. Compared to the laboratory scenarios where the undivided attention of the subject is available, in high-impact real-world scenarios, the subjects may not be distracted or interrupted frequently from their professional tasks for labeling. Public surveys [11] unveiled that at least half of the European workers are subjected to stress at work. Role of stress in functional gastrointestinal disorders. The facial expressions of the participants were captured using a webca, at a resolution of 640 X 480 at 30 frames per second. We employed the accelerometer modality in two ways. Job satisfaction among hospital nurses: A literature review. We further extracted features from heart activity, skin conductance, and accelerometer signals with our tools. Data is transmitted from the wearable to the phone using Bluetooth, and the phone transmits the data to the analytics server using Wi-Fi in the background without the involvement of the participant. Scientific Data 7, 126 (2020). Tsutsumi, A., Inoue, A. We present the accuracy results in Table 12. 3 shows the distribution of stress events in the nurses. In our case, the contest context was assumed to induce stress, the lecture context was assumed to gives some cognitive load and a lower amount of stress and free time was assumed to be relaxed sessions. ; Visualization: R.B., S.K. It can further differentiate three levels of perceived stress (see Section 6.5). For example, the maximum runtime of the devices is limited due to their limited battery. Data collection event and our experiment design are presented in Section 5. Details Frost Stress Dataset Description The dataset includes multispectral images and hyperspectral hand-held data captured before and after frost. A unique number was assigned to each participant and to each device during the study. The data was anonymized to remove publicly identifiable information and is available on Dryad. Melillo, P., Bracale, M. & Pecchia, L. Nonlinear heart rate variability features for real-life stress detection. Real-life data collection problems are addressed in Section 3. Each session is represented by a start time, end time, and stress label, where the label is determined by the average stress value S between the start time and end time. On the left side, stress recognition results that only used HR and EDA signals are presented. Heart and Mind: Evolution of Cardiac Psychology. There were three types of sessions such as the training, the contest and the free day. Basic concepts in medical informatics. [(accessed on 17 April 2019)]; International Collegiate Programming Contest. Keywords Stress detection Physiological signals Facial videos Affective computing In Proceedings of the 20th ACM international conference on multimodal interaction, 400408 (2018). This decision depends on the applied ML method. England M.J., Liverman C.T., Schultz A.M., Strawbridge L.M. In plethysmography, volumetric changes of organs are measured from the skin illuminated by the light emitted from a pulse oximeter PPG [47]. More detail about the dataset and machine learning model trained on the dataset can be found here. Lo, W.-Y., Chien, L.-Y., Hwang, F.-M., Huang, N. & Chiou, S.-T. From job stress to intention to leave among hospital nurses: A structural equation modelling approach. Aigrain J., Dubuisson S., Detyniecki M., Chetouani M. Person-specific behavioural features for automatic stress detection; Proceedings of the 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG); Ljubljana, Slovenia. Mental Stress Detection. Based on the visual inspection of the plot, stress does not appear to have a strong correlation with the Heart Rate. The responses are coarse designations of stress and are unable to detect subtler shifts in stress over time40. This box gives you information about the stress detection results: Your overall % of stress. When physiological data from each person are sufficient for developing person-specific models, they should be applied. The description field was used in place of None of the Above. We added batch processing feature to this tool. Google Scholar. We evaluated different scenarios with various window sizes and frequencies for stress detection using the AffectiveRoad dataset. However, observing several markers for identifying stress requires an increasing number of input sensors which in turn increases the overall price and lowers applicability. However, this can vary quite widely based on the type and length of activity and room temperature. If chronic stress is not handled properly, it could result in serious health issues [3]. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Our analysis shows that the computation time increases as the sampling frequency of the signals increase. Gjoreski et al. BAUM-1: BAUM-1 dataset contains 1184 multimodal facial video clips collected from 31 subjects. text message, phone call, app usage). The second model is the person dependent model. Based on the human's physical activity, the stress levels of the human being are detected and analyzed here. Hong J.H., Ramos J., Dey A.K. The stress detection algorithm has three parts. In this work, a combination of CNN with LSTM model applies to EEG signal to find out . There were up to 15% decrease when compared with physiological stress level classification accuracies. Tagged. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 800803 (2018). The four signals: Heart Rate, skin temperature, EDA, and BVP, have different sampling rates. An Empatica E4 was worn on the wrist of the dominant arm. 7 shows the overall distribution of the BVP signal of different participants. Thank you for visiting nature.com. Each biometric signal data has the following information: Start time (epoch): The DateTime floating point value that contains the time that signal was generated using the internal clock of the wristband. 83 hours of data are labeled with stress descriptors based on the validated stressful events by nurses. Furthermore, the devices used in real-life studies are non-obtrusive but their data quality is not comparable with their laboratory counterparts. Didn't find what you're looking for? The tool also has a batch processing feature. The data points for the three signals are interpolated to 4 Hz linearly. Stress Detection Dataset. First, researchers in signal processing and machine learning might be able to use the dataset to develop new machine learning models that improve stress detection performance. Incidence and mechanisms of cardiorespiratory arrests in epilepsy monitoring units (MORTEMUS): A retrospective study. Frequency: The second cell of each column shows the data collection frequency (32). 13951404. The system can differentiate the stress level of the free day, lecture and contest sessions. Gjoreski M., Gjoreski H., Lutrek M., Gams M. Automatic Detection of Perceived Stress in Campus Students Using Smartphones; Proceedings of the 2015 International Conference on Intelligent Environments; Prague, Czech Republic. The nurses had two ways to enter the labels. As the dataset is limited to only 32 participants, we developed a synthetic dataset to impose a prior on the DNN weights. If nothing happens, download Xcode and try again. Hersch, R. K. et al. Google Scholar. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), 178184 (IEEE, 2019). Proposals with accuracies higher than 95% use this combination as the physiological signals. From these features, we classified the stress level of an individual by employing machine learning algorithms. A Stress-Detection System Based on Physiological Signals and Fuzzy Logic. Wyatt J., Liu J. We offered nurses two ways of completing the surveys after stress detection: a custom mobile application and a web application (both offered the same functionality). Human Stress Detection in and through Sleep Data Code (31) Discussion (4) About Dataset Considering today's lifestyle, people just sleep forgetting the benefits sleep provides to the human body. By employing 10-fold cross-validation, the accuracy of the system is determined independently from any individuals data. https://doi.org/10.1038/s41597-022-01361-y, DOI: https://doi.org/10.1038/s41597-022-01361-y. Fig. All authors reviewed the manuscript. The result of this project through Phase II will be a system that can be deployed in space analog environments for validation testing and ultimately deployed on ISS to assist astronauts and mission support personnel in the detection of astronaut chronic stress, hyperarousal, and insomnia. Garcia-Ceja E., Osmani V., Mayora O. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k Nearest Neighbors (kNN) and Fuzzy Logic classifiers are the best performing machine learning (ML) algorithms. With all algorithms, HR, EDA and ACC signal combination with Empatica E4 devices had higher accuracy than with all devices in person-specific models. However, researchers should come up with solutions to new problems arise when taking a step outside the laboratory (see Section 3). These signals are then transmitted to the Empatica server through the mobile phones Wi-Fi connection. To increase the success of stress measurement systems, researchers tend to collect multimodal data. Mean amplitude, standard deviation, minimum and maximum values, RMS, the delay between applied stimuli and response, number of peaks, peak height, rising time, recovery time, the position of maximum and minimum features were used in the literature to measure the stress levels of the user [49]. Free day is enumerated as 0, lecture is assigned 1 and contest is assigned 2 labels. However, the IDs are generated in an ID column for all files for more convenience. 48 May 2015; pp. However, traditional methods are actually reactive, which are usually labor-consuming, time-costing and hysteretic. Description: Caltech-UCSD Birds 200 (CUB-200) is. We assumed that the stress levels of most of the subjects would be higher in contest, medium in lecture and lower in the free time with this context labels. We would like to show our gratitude to INZVA for providing us the opportunity for the data collection in their summer camp. The trier social stress test protocol for inducing psychological stress. In that light, I read many papers on stress detection and classification that uses We further plan to develop personalized perceived stress level models from ground truth surveys and remove outlier answers to increase the perceived stress level classification accuracies. There was no event where the nurses are worried about contracting COVID. A minimum amount of consecutive data samples and minimum consecutive time rules can be set to evaluate the remaining segments. Not all COVID-related stress classes ranked high, however. People should wear these devices without being uncomfortable in their daily routines, during sleeping, meetings and everyday activities. 2 papers with code 0 benchmarks 0 datasets This task has no description! Giannakakis G., Pediaditis M., Manousos D., Kazantzaki E., Chiarugi F., Simos P.G., Marias K., Tsiknakis M. Stress and anxiety detection using facial cues from videos. Random forests to classify Stress vs. Non-stress and also Stress vs. Prices for heart rate meters range from $70 to $500 USD; GSR devices range from $100 to $500 USD, while EMG devices have price ranges from $450 USD up to $1750 USD. https://github.com/CPHSLab/Stress-Detection-in-Nurses (2021). The exclusion criteria were pregnancy, heavy smoking, mental disorders, and chronic or cardiovascular diseases. Tesserae13,14,15,16,17,18,19, is a large multi-university project that studied various aspects of the workplace performance of information workers using wearables. Eighty-four students with different levels of expertise gathered to participate in this algorithmic programming contest. We monitored specific physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. Ciman M., Wac K. Individuals stress assessment using human-smartphone interaction analysis. This task can be automated by developing a mobile survey app and collecting answers periodically through pop-up surveys. Abouelenien M., Burzo M., Mihalcea R. Human Acute Stress Detection via Integration of Physiological Signals and Thermal Imaging; Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments; Corfu Island, Greece. Furthermore, at least half of the lost working days in the business sector are assumed to be caused by work-related stress and psycho-social risks [12]. The literature does not, however, validate this claim. OSH in Figures, Stress at Work, Fact and Figures. Physiological signals are sensitive to the movements of the subjects. Understanding Physiological Responses to Stressors During Physical Activity; Proceedings of the 2012 ACM Conference on Ubiquitous Computing; Pittsburgh, PA, USA.
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