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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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.

Original languageEnglish
JournalMultimedia Tools and Applications
Issue number13
Pages (from-to)19189-19220
Publication statusPublished - May 2023
Externally publishedYes


  • Biometric sensors
  • Emotion classification
  • Games
  • Machine learning
  • Player experience
  • Psychophysiological data


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