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Game-Calibrated and User-Tailored Remote Detection of Stress and Boredom in Games
Computer Science, Federal University of Fronteira Sul, Chapecó 89802 112, Brazil. (Interaction lab)ORCID-id: 0000-0001-6479-4856
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Interaction lab)ORCID-id: 0000-0002-9972-4716
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Interaction Lab)ORCID-id: 0000-0001-9287-9507
2019 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 13, s. 1-43, artikkel-id 2877Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Emotion detection based on computer vision and remote extraction of user signals commonly rely on stimuli where users have a passive role with limited possibilities for interaction or emotional involvement, e.g., images and videos. Predictive models are also trained on a group level, which potentially excludes or dilutes key individualities of users. We present a non-obtrusive, multifactorial, user-tailored emotion detection method based on remotely estimated psychophysiological signals. A neural network learns the emotional profile of a user during the interaction with calibration games, a novel game-based emotion elicitation material designed to induce emotions while accounting for particularities of individuals. We evaluate our method in two experiments (n = 20 and n = 62) with mean classification accuracy of 61.6%, which is statistically significantly better than chance-level classification. Our approach and its evaluation present unique circumstances: our model is trained on one dataset (calibration games) and tested on another (evaluation game), while preserving the natural behavior of subjects and using remote acquisition of signals. Results of this study suggest our method is feasible and an initiative to move away from questionnaires and physical sensors into a non-obtrusive, remote-based solution for detecting emotions in a context involving more naturalistic user behavior and games.

sted, utgiver, år, opplag, sider
MDPI, 2019. Vol. 19, nr 13, s. 1-43, artikkel-id 2877
Emneord [en]
human–computer interaction, games, affective computing, remote photoplethysmography
HSV kategori
Forskningsprogram
Interaction Lab (ILAB)
Identifikatorer
URN: urn:nbn:se:his:diva-17485DOI: 10.3390/s19132877ISI: 000477045000038PubMedID: 31261716Scopus ID: 2-s2.0-85069267193OAI: oai:DiVA.org:his-17485DiVA, id: diva2:1339292
Tilgjengelig fra: 2019-07-29 Laget: 2019-07-29 Sist oppdatert: 2019-11-08bibliografisk kontrollert

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