{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T17:11:20Z","timestamp":1786813880844,"version":"3.56.0"},"reference-count":58,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T00:00:00Z","timestamp":1555545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["Marie Sk\u0142odowska-Curie grant agreement No 722022."],"award-info":[{"award-number":["Marie Sk\u0142odowska-Curie grant agreement No 722022."]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Turkish Ministry of Development","award":["TAM Project number DPT2007K120610."],"award-info":[{"award-number":["TAM Project number DPT2007K120610."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The negative effects of mental stress on human health 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 step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.<\/jats:p>","DOI":"10.3390\/s19081849","type":"journal-article","created":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T11:58:21Z","timestamp":1555588701000},"page":"1849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":318,"title":["Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study"],"prefix":"10.3390","volume":"19","author":[{"given":"Yekta Said","family":"Can","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Bo\u011fazi\u00e7i University, Bebek, Istanbul 34342, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7228-4725","authenticated-orcid":false,"given":"Niaz","family":"Chalabianloo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bo\u011fazi\u00e7i University, Bebek, Istanbul 34342, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8130-3841","authenticated-orcid":false,"given":"Deniz","family":"Ekiz","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bo\u011fazi\u00e7i University, Bebek, Istanbul 34342, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7632-7067","authenticated-orcid":false,"given":"Cem","family":"Ersoy","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bo\u011fazi\u00e7i University, Bebek, Istanbul 34342, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/MMUL.2016.38","article-title":"Automating the Recognition of Stress and Emotion: From Lab to Real-World Impact","volume":"23","author":"Picard","year":"2016","journal-title":"IEEE MultiMedia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.yebeh.2012.06.016","article-title":"Epilepsy across the spectrum: Promoting health and understanding: A summary of the Institute of Medicine report","volume":"25","author":"England","year":"2012","journal-title":"Epilepsy Behav."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1016\/S1474-4422(13)70214-X","article-title":"Incidence and mechanisms of cardiorespiratory arrests in epilepsy monitoring units (MORTEMUS): A retrospective study","volume":"12","author":"Ryvlin","year":"2013","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1300\/J490v21n02_07","article-title":"Workplace stress: Etiology and consequences","volume":"21","author":"Colligan","year":"2006","journal-title":"J. Workplace Behav. Health"},{"key":"ref_5","unstructured":"American Psychology Association (2019). Stress: The Different Kinds of Stress, American Psychology Association."},{"key":"ref_6","unstructured":"Krantz, D.S., Whittaker, K.S., and Sheps, D.S. (2011). Psychosocial risk factors for coronary heart disease: Pathophysiologic mechanisms. Heart and Mind: Evolution of Cardiac Psychology, American Psychological Association."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11906-001-0047-1","article-title":"Mental stress as a causal factor in the development of hypertension and cardiovascular disease","volume":"3","author":"Pickering","year":"2001","journal-title":"Curr. Hypertens. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1159\/000050681","article-title":"Role of stress in functional gastrointestinal disorders","volume":"19","author":"Tebbe","year":"2001","journal-title":"Dig. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1136\/bmj.315.7107.530","article-title":"Fortnightly review: Stress, the brain, and mental illness","volume":"315","author":"Herbert","year":"1997","journal-title":"BMJ"},{"key":"ref_10","unstructured":"Milczarek, M., and Elke Schneider, E.G. (2009). OSH in Figures, Stress at Work, Fact and Figures, European Agency for Safety and Health at Work."},{"key":"ref_11","unstructured":"European Agency for Safety and Health at Work (2013). European Opinion Poll on Occupational Safety and Health, European Agency for Safety and Health at Work."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jbi.2015.11.007","article-title":"Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review","volume":"59","author":"Alberdi","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_13","unstructured":"Vanitha, V., and Krishnan, P. (2016). Real Time Stress Detection System Based on EEG Signals. Biomed. Res., S271\u2013S275. Available online: http:\/\/www.biomedres.info\/biomedical-research\/real-time-stress-detection-system-based-on-eeg-signals.html."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Costin, R., Rotariu, C., and Pasarica, A. (2012, January 25\u201327). Mental stress detection using heart rate variability and morphologic variability of EeG signals. Proceedings of the 2012 International Conference and Exposition on Electrical and Power Engineering, Ia\u015fi, Romania.","DOI":"10.1109\/ICEPE.2012.6463870"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4857","DOI":"10.1109\/TIE.2010.2103538","article-title":"A Stress-Detection System Based on Physiological Signals and Fuzzy Logic","volume":"58","author":"Avila","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Soury, M., and Devillers, L. (2013, January 2\u20135). Stress Detection from Audio on Multiple Window Analysis Size in a Public Speaking Task. Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland.","DOI":"10.1109\/ACII.2013.93"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wijsman, J., Grundlehner, B., Liu, H., Hermens, H., and Penders, J. (September, January 30). Towards mental stress detection using wearable physiological sensors. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6090512"},{"key":"ref_18","unstructured":"Abouelenien, M., Burzo, M., and Mihalcea, R. (July, January 29). 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."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Aigrain, J., Dubuisson, S., Detyniecki, M., and Chetouani, M. (2015, January 4\u20138). 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.","DOI":"10.1109\/FG.2015.7284844"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TAFFC.2016.2631594","article-title":"Multimodal stress detection from multiple assessments","volume":"9","author":"Aigrain","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.bspc.2016.06.020","article-title":"Stress and anxiety detection using facial cues from videos","volume":"31","author":"Giannakakis","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Baltaci, S., and Gokcay, D. (2014, January 23\u201325). Role of pupil dilation and facial temperature features in stress detection. Proceedings of the 2014 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey.","DOI":"10.1109\/SIU.2014.6830465"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1650041","DOI":"10.1142\/S0129065716500416","article-title":"Stress Detection Using Wearable Physiological and Sociometric Sensors","volume":"27","author":"Mozos","year":"2017","journal-title":"Int. J. Neural Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zubair, M., Yoon, C., Kim, H., Kim, J., and Kim, J. (2015, January 24\u201327). Smart Wearable Band for Stress Detection. Proceedings of the 2015 5th International Conference on IT Convergence and Security (ICITCS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICITCS.2015.7293017"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hong, J.H., Ramos, J., and Dey, A.K. (2012, January 5\u20138). Understanding Physiological Responses to Stressors During Physical Activity. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370260"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TMSCS.2017.2703613","article-title":"Keep the Stress Away with SoDA: Stress Detection and Alleviation System","volume":"3","author":"Akmandor","year":"2017","journal-title":"IEEE Trans. Multi-Scale Comput. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Akhonda, M.A.B.S., Islam, S.M.F., Khan, A.S., Ahmed, F., and Rahman, M.M. (2014, January 22\u201323). Stress detection of computer user in office like working environment using neural network. Proceedings of the 2014 17th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh.","DOI":"10.1109\/ICCITechn.2014.7073120"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5057\/ijae.14.9","article-title":"Mental stress recognition based on non-invasive and non-contact measurement from stereo thermal and visible sensors","volume":"14","author":"Mohd","year":"2015","journal-title":"Int. J. Affect. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liapis, A., Katsanos, C., Sotiropoulos, D., Xenos, M., and Karousos, N. (2015, January 1\u20133). 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.","DOI":"10.1145\/2801948.2801964"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, M.X., Li, J., Ngai, G., and Leong, H.V. (2016, January 15\u201319). StressClick: Sensing Stress from Gaze-Click Patterns. Proceedings of the 2016 ACM on Multimedia Conference, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2964318"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.bspc.2018.03.006","article-title":"Mental stress detection using bioradar respiratory signals","volume":"43","author":"Anishchenko","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TAFFC.2016.2592504","article-title":"Individuals\u2019 stress assessment using human-smartphone interaction analysis","volume":"9","author":"Ciman","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gjoreski, M., Gjoreski, H., Lu\u0161trek, M., and Gams, M. (2016, January 12\u201316). Continuous Stress Detection Using a Wrist Device: In Laboratory and Real Life. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2968306"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jbi.2017.08.006","article-title":"Monitoring stress with a wrist device using context","volume":"73","author":"Gjoreski","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s00779-017-1108-z","article-title":"Unobtrusive stress detection on the basis of smartphone usage data","volume":"22","author":"Vildjiounaite","year":"2018","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hernandez, J., Morris, R.R., and Picard, R.W. (2011). Call Center Stress Recognition with Person-specific Models. Affective Computing and Intelligent Interaction\u2014Volume Part I, Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, Memphis, TN, USA, 9\u201312 October 2011, Springer.","DOI":"10.1007\/978-3-642-24600-5_16"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/JBHI.2015.2446195","article-title":"Automatic Stress Detection in Working Environments From Smartphones\u2019 Accelerometer Data: A First Step","volume":"20","author":"Osmani","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s00779-011-0466-1","article-title":"Monitoring of Mental Workload Levels During an Everyday Life Office-work Scenario","volume":"17","author":"Cinaz","year":"2013","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting stress during real-world driving tasks using physiological sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.eswa.2017.01.040","article-title":"Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers","volume":"85","author":"Chen","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Castaldo, R., Xu, W., Melillo, P., Pecchia, L., Santamaria, L., and James, C. (2016, January 16\u201320). Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591557"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gjoreski, M., Gjoreski, H., Lutrek, M., and Gams, M. (2015, January 15\u201317). Automatic Detection of Perceived Stress in Campus Students Using Smartphones. Proceedings of the 2015 International Conference on Intelligent Environments, Prague, Czech Republic.","DOI":"10.1109\/IE.2015.27"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., and Pentland, A.S. (2014, January 24\u201328). Pervasive stress recognition for sustainable living. Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), Budapest, Hungary.","DOI":"10.1109\/PerComW.2014.6815230"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bauer, G., and Lukowicz, P. (2012, January 19\u201323). Can smartphones detect stress-related changes in the behaviour of individuals?. Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, Lugano, Switzerland.","DOI":"10.1109\/PerComW.2012.6197525"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s00779-015-0885-5","article-title":"Noninvasive Stress Recognition Considering the Current Activity","volume":"19","author":"Sysoev","year":"2015","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1136\/jech.56.11.808","article-title":"Basic concepts in medical informatics","volume":"56","author":"Wyatt","year":"2002","journal-title":"J. Epidemiol. Community Health"},{"key":"ref_47","unstructured":"Lake, C., Hines, R., and Blitt, C. (2001). Pulse oximeter waveform: photoelectric plethysmography. Clinical Monitoring, WB Saunders Company."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1016\/j.cmpb.2012.07.003","article-title":"Objective measures, sensors and computational techniques for stress recognition and classification: A survey","volume":"108","author":"Sharma","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103139","DOI":"10.1016\/j.jbi.2019.103139","article-title":"Stress detection in daily life scenarios using smart phones and wearable sensors: A survey","volume":"92","author":"Can","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., and Picard, R. (2015, January 25\u201329). Automatic identification of artifacts in electrodermal activity data. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318762"},{"key":"ref_51","first-page":"797","article-title":"cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing","volume":"63","author":"Greco","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_52","unstructured":"Vollmer, M. (2019). MarcusVollmer\/HRV Toolbox, GitHub."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/BF00648343","article-title":"Least-squares frequency analysis of unequally spaced data","volume":"39","author":"Lomb","year":"1976","journal-title":"Astrophys. Space Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1037\/h0022736","article-title":"Differential communication of affect by head and body cues","volume":"2","author":"Ekman","year":"1965","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_55","unstructured":"Eibe, F., Hall, M., and Witten, I. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann."},{"key":"ref_56","unstructured":"(2019, April 17). INZVA Algorithmic Summer Camp. Available online: https:\/\/inzva.com\/algorithmic-competition-summer-camp-2018-report."},{"key":"ref_57","unstructured":"(2019, January 06). International Collegiate Programming Contest. Available online: https:\/\/icpc.baylor.edu\/."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"World Medical Association (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bull. World Health Organ., 79, 373.","DOI":"10.4414\/smf.2001.04031"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/8\/1849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:23Z","timestamp":1760186783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/8\/1849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,18]]},"references-count":58,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["s19081849"],"URL":"https:\/\/doi.org\/10.3390\/s19081849","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,18]]}}}