{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:08:19Z","timestamp":1776442099249,"version":"3.51.2"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T00:00:00Z","timestamp":1691193600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T00:00:00Z","timestamp":1691193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007157","name":"Instituto Polit\u00e9cnico do Porto","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007157","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Stress is a common feeling in people\u2019s day-to-day life, especially at work, being the cause of several health problems and absenteeism. Despite the difficulty in identifying it properly, several studies have established a correlation between stress and perceivable human features. The problem of detecting stress has attracted significant attention in the last decade. It has been mainly addressed through the analysis of physiological signals in the execution of specific tasks in controlled environments. Taking advantage of technological advances that allow to collect stress-related data in a non-invasive way, the goal of this work is to provide an alternative approach to detect stress in the workplace without requiring specific controlled conditions. To this end, a video-based plethysmography application that analyses the person\u2019s face and retrieves several physiological signals in a non-invasive way was used. Moreover, in an initial phase, additional information that complements and labels the physiological data was obtained through a brief questionnaire answered by the participants. The data collection pilot took place over a period of two months, having involved 28 volunteers. Several stress detection models were developed; the best trained model achieved an accuracy of 86.8% and a F1 score of 87% on a binary stress\/non-stress prediction.<\/jats:p>","DOI":"10.1007\/s10844-023-00806-z","type":"journal-article","created":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T11:01:36Z","timestamp":1691233296000},"page":"77-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Semi-supervised and ensemble learning to predict work-related stress"],"prefix":"10.1007","volume":"62","author":[{"given":"F\u00e1tima","family":"Rodrigues","sequence":"first","affiliation":[]},{"given":"Hugo","family":"Correia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,5]]},"reference":[{"key":"806_CR1","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.jbi.2015.11.007","volume":"59","author":"A Alberdi","year":"2016","unstructured":"Alberdi, A., Aztiria, A., & Basarab, A. (2016). Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. Journal of Biomedical Informatics, 59, 49\u201375. https:\/\/doi.org\/10.1016\/j.jbi.2015.11.007","journal-title":"Journal of Biomedical Informatics"},{"key":"806_CR2","doi-asserted-by":"crossref","unstructured":"Almeida, J., Rodrigues, F. (2021). Facial expression recognition system for stress detection with deep learning. In ICEIS (1), 256-263. https:\/\/www.scitepress.org\/Papers\/2021\/104742\/104742.pdf","DOI":"10.5220\/0010474202560263"},{"issue":"8","key":"806_CR3","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.3390\/s19081849","volume":"19","author":"YS Can","year":"2019","unstructured":"Can, Y. S., Chalabianloo, N., Ekiz, D., et al. (2019). Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors, 19(8), 1849. https:\/\/doi.org\/10.3390\/s19081849","journal-title":"Sensors"},{"key":"806_CR4","doi-asserted-by":"publisher","unstructured":"Carolan, S., Harris, P. R., Cavanagh, K. (2017). Improving employee well-being and effectiveness: systematic review and meta-analysis of web-based psychological interventions delivered in the workplace. Journal of Medical Internet Research, 19(7), Article e271. https:\/\/doi.org\/10.2196\/jmir.7583","DOI":"10.2196\/jmir.7583"},{"key":"806_CR5","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s10844-022-00704-w","volume":"59","author":"JC Cheng","year":"2022","unstructured":"Cheng, J. C., & Chen, A. L. P. (2022). Multimodal time-aware attention networks for depression detection. J Intell Inf Syst, 59, 319\u2013339. https:\/\/doi.org\/10.1007\/s10844-022-00704-w","journal-title":"J Intell Inf Syst"},{"issue":"8","key":"806_CR6","doi-asserted-by":"publisher","first-page":"2873","DOI":"10.3390\/s21082873","volume":"21","author":"KM Dalmeida","year":"2021","unstructured":"Dalmeida, K. M., & Masala, G. L. (2021). HRV features as viable physiological markers for stress detection using wearable devices. Sensors, 21(8), 2873. https:\/\/doi.org\/10.3390\/s21082873","journal-title":"Sensors"},{"key":"806_CR7","doi-asserted-by":"publisher","unstructured":"Dietterich, T. G. (2000). Ensemble methods in Machine Learning. In multiple classifier systems, First International Workshop, MCS. Cagliari, Italy, June 21\u201323, Proceedings 1. Springer, Berlin Heidelberg, 1\u201315 https:\/\/doi.org\/10.1007\/3-540-45014-9_1","DOI":"10.1007\/3-540-45014-9_1"},{"key":"806_CR8","doi-asserted-by":"publisher","first-page":"84045","DOI":"10.1109\/ACCESS.2021.3085502","volume":"9","author":"S Gedam","year":"2021","unstructured":"Gedam, S., & Paul, S. (2021). A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, 9, 84045\u201384066. https:\/\/doi.org\/10.1109\/ACCESS.2021.3085502","journal-title":"IEEE Access"},{"key":"806_CR9","unstructured":"Gomes, P., Margaritoff, P., Silva, H. (2019). pyHRV: Development and evaluation of an open-source python toolbox for heart rate variability (hrv), Proc. International Conference on Electrical, Electronic and Computing Engineering (icetran), 822-828."},{"key":"806_CR10","unstructured":"Hanula-Bobbitt, K., Bo\u010dkut\u0117, K. (2022). Stress management in the education sector. Master thesis, Tampere University of Applied Sciences, Finland 2022. https:\/\/urn.fi\/URN:NBN:fi:amk-2022121429695"},{"key":"806_CR11","doi-asserted-by":"publisher","unstructured":"Hilmy, M. S. H., Asnawi, A. L., Jusoh, A. Z. et\u00a0al. (2021). Stress classification based on speech analysis of MFCC feature via Machine Learning. In 8th International Conference on Computer and Communication Engineering (ICCCE) 339-343, IEEE. https:\/\/doi.org\/10.1109\/ICCCE50029.2021.9467176","DOI":"10.1109\/ICCCE50029.2021.9467176"},{"key":"806_CR12","doi-asserted-by":"publisher","unstructured":"Kim, H. G., Cheon, E. J., Bai, D. S. et al. (2018). Stress and heart rate variability: a meta-analysis and review of the literature. Psychiatry Investigation, 15(3), 235. https:\/\/doi.org\/10.30773\/pi.2017.08.17","DOI":"10.30773\/pi.2017.08.17"},{"key":"806_CR13","doi-asserted-by":"publisher","first-page":"129","DOI":"10.5555\/1867135.1867155","volume":"2","author":"K Kira","year":"1992","unstructured":"Kira, K., & Rendell, L. A. (1992). The feature selection problem: traditional methods and a new algorithm. AAAI, 2, 129\u2013134. https:\/\/doi.org\/10.5555\/1867135.1867155","journal-title":"AAAI"},{"key":"806_CR14","doi-asserted-by":"publisher","first-page":"2854","DOI":"10.1016\/j.promfg.2015.07.783","volume":"3","author":"G Marquart","year":"2015","unstructured":"Marquart, G., Cabrall, C., & de Winter, J. (2015). Review of eye-related measures of drivers\u2019 mental workload. Procedia Manufacturing, 3, 2854\u20132861. https:\/\/doi.org\/10.1016\/j.promfg.2015.07.783","journal-title":"Procedia Manufacturing"},{"key":"806_CR15","doi-asserted-by":"publisher","unstructured":"Maxhuni, A., Hernandez-Leal, P., Sucar, et al. (2016). Stress modelling and prediction in presence of scarce data. Journal of Biomedical Informatics, 63, 344\u2013356. https:\/\/doi.org\/10.1016\/j.jbi.2016.08.023","DOI":"10.1016\/j.jbi.2016.08.023"},{"key":"806_CR16","unstructured":"Ordem dos Psic\u00f3logos Portugueses, (2023). O Custo do stress e dos problemas de sa\u00fade psicol\u00f3gica no trabalho em Portugal, Contributo OPP. https:\/\/www.ordemdospsicologos.pt\/pt\/noticia\/4466, last accessed 27 Feb 2023"},{"issue":"2","key":"806_CR17","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.bbe.2019.01.004","volume":"39","author":"SS Panicker","year":"2019","unstructured":"Panicker, S. S., & Gayathri, P. (2019). A survey of machine learning techniques in physiology based mental stress detection systems. Biocybernetics and Biomedical Engineering, 39(2), 444\u2013469. https:\/\/doi.org\/10.1016\/j.bbe.2019.01.004","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"806_CR18","doi-asserted-by":"publisher","unstructured":"Park, J., Kim, J., Kim, S. P. (2018). Prediction of daily mental stress levels using a wearable photoplethysmography sensor. In TENCON IEEE Region 10 Conference, 1899-1902. https:\/\/doi.org\/10.1109\/TENCON.2018.8650109","DOI":"10.1109\/TENCON.2018.8650109"},{"key":"806_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00420-020-01525-6","volume":"25","author":"I Reijmerink","year":"2020","unstructured":"Reijmerink, I., van der Laan, M., & Cnossen, F. (2020). Heart rate variability as a measure of mental stress in surgery: A systematic review. Int. Arch. Occup. Environ. Health, 25, 1\u201317. https:\/\/doi.org\/10.1007\/s00420-020-01525-6","journal-title":"Int. Arch. Occup. Environ. Health"},{"key":"806_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2021.3056960","author":"RM Sabour","year":"2021","unstructured":"Sabour, R. M., Benezeth, Y., De Oliveira, P., et al. (2021). UBFC-Phys: A multimodal database for psychophysiological studies of social stress. IEEE Transactions on Affective Computing. https:\/\/doi.org\/10.1109\/TAFFC.2021.3056960","journal-title":"IEEE Transactions on Affective Computing"},{"key":"806_CR21","doi-asserted-by":"publisher","unstructured":"Seiler, A., Fagundes C.P., Christian L. M. (2020). The impact of everyday stressors on the immune system and health. In Stress challenges and immunity in space, 71-92. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-16996-1_6","DOI":"10.1007\/978-3-030-16996-1_6"},{"key":"806_CR22","doi-asserted-by":"publisher","unstructured":"Tran, C. T., Zhang, M., Andreae, P., et\u00a0al. (2017). Bagging and feature selection for classification with incomplete data. In Applications of Evolutionary Computation: 20th European Conference, Evo Applications, Amsterdam, Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-319-55849-3_31","DOI":"10.1007\/978-3-319-55849-3_31"},{"issue":"2","key":"806_CR23","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373\u2013440. https:\/\/doi.org\/10.1007\/s10994-019-05855-6","journal-title":"Machine Learning"},{"key":"806_CR24","unstructured":"World Health Organization (WHO), (2023). Occupational health: stress at the workplace, https:\/\/www.who.int\/news-room\/questions-and-answers\/item\/ccupational-health-stress-at-the-workplace, last accessed 5 Mar 2023"},{"key":"806_CR25","unstructured":"Wirth, R., & Hipp, J. (2000). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international Conference on the Practical Applications of Knowledge Discovery and Data Mining, Vol. 1, 29-39. http:\/\/www.cs.unibo.it\/~danilo.montesi\/CBD\/Beatriz\/10.1.1.198.5133.pdf"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-023-00806-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-023-00806-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-023-00806-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T18:05:18Z","timestamp":1710093918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-023-00806-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,5]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["806"],"URL":"https:\/\/doi.org\/10.1007\/s10844-023-00806-z","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,5]]},"assertion":[{"value":"20 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}