Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives

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dc.contributor.authorOmetov, Aleksandrcs
dc.contributor.authorMezina, Anzhelikacs
dc.contributor.authorLin, Hsiao-Chuncs
dc.contributor.authorArponen, Otsocs
dc.contributor.authorBurget, Radimcs
dc.contributor.authorNurmi, Jarics
dc.coverage.issue6cs
dc.coverage.volume9cs
dc.date.accessioned2025-08-07T05:57:02Z
dc.date.available2025-08-07T05:57:02Z
dc.date.issued2025-06-18cs
dc.description.abstractRemote continuous patient monitoring is an essential feature of eHealth systems, offering opportunities for personalized care. Among its emerging applications, emotion and stress recognition hold significant promise, but face major challenges due to the subjective nature of emotions and the complexity of collecting and interpreting related data. This paper presents a review of open access multimodal datasets used in emotion and stress detection. It focuses on dataset characteristics, acquisition methods, and classification challenges, with attention to physiological signals captured by wearable devices, as well as advanced processing methods of these data. The findings show notable advances in data collection and algorithm development, but limitations remain, e.g., variability in real-world conditions, individual differences in emotional responses, and difficulties in objectively validating emotional states. The inclusion of self-reported and contextual data can enhance model performance, yet lacks consistency and reliability. Further barriers include privacy concerns, annotation of long-term data, and ensuring robustness in uncontrolled environments. By analyzing the current landscape and highlighting key gaps, this study contributes a foundation for future work in emotion recognition. Progress in the field will require privacy-preserving data strategies and interdisciplinary collaboration to develop reliable, scalable systems. These advances can enable broader adoption of emotion-aware technologies in eHealth and beyond.en
dc.formattextcs
dc.format.extent247-279cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Healthcare Informatics Research. 2025, vol. 9, issue 6, p. 247-279.en
dc.identifier.doi10.1007/s41666-025-00200-0cs
dc.identifier.issn2509-4971cs
dc.identifier.orcid0000-0003-1849-5390cs
dc.identifier.other198172cs
dc.identifier.scopus23011250200cs
dc.identifier.urihttps://hdl.handle.net/11012/255407
dc.language.isoencs
dc.publisherSpringer Naturecs
dc.relation.ispartofJournal of Healthcare Informatics Researchcs
dc.relation.urihttps://link.springer.com/article/10.1007/s41666-025-00200-0cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2509-4971/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectEmotionen
dc.subjectStressen
dc.subjectRecognitionen
dc.subjectDetectionen
dc.subjectDataseten
dc.subjecteHealthen
dc.subjectWearableen
dc.subjectOpen accessen
dc.subjectReviewen
dc.titleStress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectivesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-198172en
sync.item.dbtypeVAVen
sync.item.insts2025.08.07 07:57:02en
sync.item.modts2025.08.07 07:34:08en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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