{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T03:41:46Z","timestamp":1784691706557,"version":"3.55.0"},"reference-count":22,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this article, regression and classification models are compared for stress detection. Both personal and user-independent models are experimented. The article is based on publicly open dataset called AffectiveROAD, which contains data gathered using Empatica E4 sensor and unlike most of the other stress detection datasets, it contains continuous target variables. The used classification model is Random Forest and the regression model is Bagged tree based ensemble. Based on experiments, regression models outperform classification models, when classifying observations as stressed or not-stressed. The best user-independent results are obtained using a combination of blood volume pulse and skin temperature features, and using these the average balanced accuracy was 74.1% with classification model and 82.3% using regression model. In addition, regression models can be used to estimate the level of the stress. Moreover, the results based on models trained using personal data are not encouraging showing that biosignals have a lot of variation not only between the study subjects but also between the session gathered from the same person. On the other hand, it is shown that with subject-wise feature selection for user-independent model, it is possible to improve recognition models more than by using personal training data to build personal models. In fact, it is shown that with subject-wise feature selection, the average detection rate can be improved as much as 4%-units, and it is especially useful to reduce the variance in the recognition rates between the study subjects.<\/jats:p>","DOI":"10.3390\/s20164402","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T09:30:54Z","timestamp":1596792654000},"page":"4402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5995-5421","authenticated-orcid":false,"given":"Pekka","family":"Siirtola","sequence":"first","affiliation":[{"name":"Biomimetics and Intelligent Systems Group, University of Oulu, P.O. BOX 4500, FI-90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juha","family":"R\u00f6ning","sequence":"additional","affiliation":[{"name":"Biomimetics and Intelligent Systems Group, University of Oulu, P.O. BOX 4500, FI-90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rathod, P., George, K., and Shinde, N. (2016, January 14\u201317). Bio-signal based emotion detection device. Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA.","DOI":"10.1109\/BSN.2016.7516241"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zenonos, A., Khan, A., Kalogridis, G., Vatsikas, S., Lewis, T., and Sooriyabandara, M. (2016, January 14\u201318). HealthyOffice: Mood recognition at work using smartphones and wearable sensors. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia.","DOI":"10.1109\/PERCOMW.2016.7457166"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1108\/00251740810854168","article-title":"Tightening the link between employee wellbeing at work and performance: A new dimension for HRM","volume":"46","year":"2008","journal-title":"Manag. Decis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/nrn2639","article-title":"Effects of stress throughout the lifespan on the brain, behaviour and cognition","volume":"10","author":"Lupien","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.cmet.2016.01.015","article-title":"SnapShot: Stress and disease","volume":"23","author":"Nagaraja","year":"2016","journal-title":"Cell Metab."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Siirtola, P. (2019, January 9\u201313). Continuous stress detection using the sensors of commercial smartwatch. Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, London, UK.","DOI":"10.1145\/3341162.3344831"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., D\u00fcrichen, R., and Laerhoven, K.V. (2019). Wearable-Based Affect Recognition\u2014A Review. Sensors, 19.","DOI":"10.3390\/s19194079"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Hernandez, J., Morris, R.R., and Picard, R.W. (2011, January 9\u201312). Call center stress recognition with person-specific models. Proceedings of the International Conference on Affective Computing and Intelligent Interaction, Memphis, TN, USA.","DOI":"10.1007\/978-3-642-24600-5_16"},{"key":"ref_11","unstructured":"Stewart, C.L., Folarin, A., and Dobson, R. (2020). Personalized acute stress classification from physiological signals with neural processes. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mishra, V., Hao, T., Sun, S., Walter, K.N., Ball, M.J., Chen, C.H., and Zhu, X. (2018, January 8\u201312). Investigating the role of context in perceived stress detection in the wild. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore.","DOI":"10.1145\/3267305.3267537"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Haouij, N.E., Poggi, J.M., Sevestre-Ghalila, S., Ghozi, R., and Ja\u00efdane, M. (2018, January 9\u201313). AffectiveROAD System and Database to Assess Driver\u2019s Attention. Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France.","DOI":"10.1145\/3167132.3167395"},{"key":"ref_15","unstructured":"El Haouij, N. (2018). Biosignals for Driver\u2019s Stress Level Assessment: Functional Variable Selection and Fractal Characterization. [Ph.D. Thesis, Paris Saclay]."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.intcom.2012.04.003","article-title":"A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing","volume":"24","author":"Novak","year":"2012","journal-title":"Interact. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23384","DOI":"10.1038\/srep23384","article-title":"Higher-order multivariable polynomial regression to estimate human affective states","volume":"6","author":"Wei","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_18","unstructured":"(2019, May 27). Empatica E4. Available online: https:\/\/www.empatica.com\/e4-wristband."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018, January 16\u201320). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 2018 on International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.9781\/ijimai.2012.155","article-title":"Recognizing human activities user-independently on smartphones based on accelerometer data","volume":"1","author":"Siirtola","year":"2012","journal-title":"IJIMAI"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Siirtola, P., and R\u00f6ning, J. (2019). Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration. Sensors, 19.","DOI":"10.3390\/s19235151"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Schmidt, P., D\u00fcrichen, R., Reiss, A., Van Laerhoven, K., and Pl\u00f6tz, T. (2019, January 9\u201313). Multi-target affect detection in the wild: An exploratory study. Proceedings of the 23rd International Symposium on Wearable Computers, London, UK.","DOI":"10.1145\/3341163.3347741"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:57:31Z","timestamp":1760176651000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,7]]},"references-count":22,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164402"],"URL":"https:\/\/doi.org\/10.3390\/s20164402","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,7]]}}}