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Ontological Model for Contextual Data Defining Time Series for Emotion Recognition and Analysis

Abstrakt

One of the major challenges facing the field of Affective Computing is the reusability of datasets. Existing affective-related datasets are not consistent with each other, they store a variety of information in different forms, different formats, and the terms used to describe them are not unified. This paper proposes a new ontology, ROAD, as a solution to this problem, by formally describing the datasets and unifying the terms used. The developed ontology allows information about the origin and meaning of the data to be modeled, i.e., time series, representing both emotional states and features derived from biosignals. Furthermore, the ROAD ontology is extensible and not application-oriented, thus it can be used to store data from a wide range of Affective Computing experiments. The ontology was validated by modeling data obtained from one experiment on the AMIGOS dataset. The approach proposed in the paper can be used both by researchers who create new datasets or want to reuse existing ones, and for those who want to process data from experiments in a more automated way.

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Wersja publikacji
Accepted albo Published Version
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/ACCESS.2021.3132728
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Creative Commons: CC-BY otwiera się w nowej karcie

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
IEEE Access nr 9, strony 166674 - 166694,
ISSN: 2169-3536
Język:
angielski
Rok wydania:
2021
Opis bibliograficzny:
Zawadzka T., Waloszek W., Karpus A., Zapalowska S., Wróbel M.: Ontological Model for Contextual Data Defining Time Series for Emotion Recognition and Analysis// IEEE Access -Vol. 9, (2021), s.166674-166694
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/access.2021.3132728
Weryfikacja:
Politechnika Gdańska

wyświetlono 87 razy

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