{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T04:34:13Z","timestamp":1778560453018,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition.<\/jats:p>","DOI":"10.3390\/bioengineering10111308","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T02:02:42Z","timestamp":1699840962000},"page":"1308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4112-0577","authenticated-orcid":false,"given":"Enni","family":"Mattern","sequence":"first","affiliation":[{"name":"Chair of Control Engineering, Technische Universit\u00e4t Berlin, Stra\u00dfe des 17. Juni 135, 10623 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8663-2070","authenticated-orcid":false,"given":"Roxanne R.","family":"Jackson","sequence":"additional","affiliation":[{"name":"Chair of Control Engineering, Technische Universit\u00e4t Berlin, Stra\u00dfe des 17. Juni 135, 10623 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2569-1245","authenticated-orcid":false,"given":"Roya","family":"Doshmanziari","sequence":"additional","affiliation":[{"name":"Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), H\u00f8gskoleringen 1, 7034 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7265-8715","authenticated-orcid":false,"given":"Marieke","family":"Dewitte","sequence":"additional","affiliation":[{"name":"Department of Clinical Psychological Science, Maastricht University, Minderbroedersberg 4\u20136, 6211 LK Maastricht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4310-7938","authenticated-orcid":false,"given":"Damiano","family":"Varagnolo","sequence":"additional","affiliation":[{"name":"Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), H\u00f8gskoleringen 1, 7034 Trondheim, Norway"},{"name":"Department of Information Engineering, University of Padova, Via VIII Febbraio, 2, 35122 Padova, PD, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4413-4225","authenticated-orcid":false,"given":"Steffi","family":"Knorn","sequence":"additional","affiliation":[{"name":"Chair of Control Engineering, Technische Universit\u00e4t Berlin, Stra\u00dfe des 17. Juni 135, 10623 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1016\/j.ifacol.2017.08.207","article-title":"Qualitative modeling of pressure vs. pain relations in women suffering from dyspareunia","volume":"50","author":"Varagnolo","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1007\/s10845-015-1145-2","article-title":"Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices","volume":"28","author":"Balters","year":"2017","journal-title":"J. Intell. 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