An automated approach to estimate player experience in game events from psychophysiological data

Elton Sarmanho Siqueira*, Marcos Cordeiro Fleury, Marcus Vinicius Lamar, Anders Drachen, Carla Denise Castanho, Ricardo Pezzuol Jacobi

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstract

Games User Research (GUR) is a relevant field of research that exploits knowledge on human-computer interaction, game design, and psychology, with a focus on improving the player experience (PX) and the quality of the game. Games form an environment of rich interactions which can lead to a variety of experiences for the player. Researchers employ new ways to assess PX over time with some degree of precision, while avoiding the interruption of gameplay. A possible way of attaining great PX evaluation can be using psychophysiological data. It is a source that can provide relevant details about the emotional states and a potential information in the context of GUR. This paper presents a process for classifying PX in games based on psychophysiological data acquired from the user during the gameplay. Biosensors and a webcam were employed to capture three signals: Galvanic Skin Response (GSR), Blood Volume Pulse (BVP) and Facial Expression. Our artificial neural network was trained with a dataset formed by psychophysiological data and human-annotated emotional expressions derived from assessment and judgment of players’ face and behavior with the help of an emotion annotation tool. Four classes of emotions, derived from the most significant game events, are considered for classification: Anger, Calm, Happiness and Sadness. The experimental results indicate that the proposed method leads to good human emotion recognition, and an accuracy score of 64%. The automatic assessment of player experience was compared with a traditional evaluation based on self-report, corroborating the effectiveness of the method.

OriginalsprogEngelsk
TidsskriftMultimedia Tools and Applications
Vol/bind82
Udgave nummer13
Sider (fra-til)19189-19220
ISSN1380-7501
DOI
StatusUdgivet - maj 2023
Udgivet eksterntJa

Bibliografisk note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Fingeraftryk

Dyk ned i forskningsemnerne om 'An automated approach to estimate player experience in game events from psychophysiological data'. Sammen danner de et unikt fingeraftryk.

Citationsformater