This paper discusses an approach to monitor the level of engagement of video game players based on the theory of flow in the gaming experience. Starting from the flow framework, we developed a non-obtrusive system that estimates the player’s state of engagement by analysing non-verbal behavioral cues that are easily detected with simple hardware, such as a webcam and a traditional keyboard and mouse setup. We present the design and the results of an empirical study aimed at gathering data and model the player’s engagement. Facial expressions, head movements, keyboard and mouse activities were recorded while participants played a first-person shooter video game. We used an adapted version of the Experience Sampling Methodology to gather the ground truth and trained a Support Vector Machine classifier that recognizes the affective states, reaching an accuracy of 73%. The results showed that the level of engagement is reasonably predicted by considering the head movements and facial expressions only. The findings could aid in developing digital games able to use the information about the player’s affective state to adapt their content and support the game experience.

Engagement Recognition using Easily Detectable Behavioural Cues

Schiavo, Gianluca;Cappelletti, Alessandro;Zancanaro, Massimo
2014

Abstract

This paper discusses an approach to monitor the level of engagement of video game players based on the theory of flow in the gaming experience. Starting from the flow framework, we developed a non-obtrusive system that estimates the player’s state of engagement by analysing non-verbal behavioral cues that are easily detected with simple hardware, such as a webcam and a traditional keyboard and mouse setup. We present the design and the results of an empirical study aimed at gathering data and model the player’s engagement. Facial expressions, head movements, keyboard and mouse activities were recorded while participants played a first-person shooter video game. We used an adapted version of the Experience Sampling Methodology to gather the ground truth and trained a Support Vector Machine classifier that recognizes the affective states, reaching an accuracy of 73%. The results showed that the level of engagement is reasonably predicted by considering the head movements and facial expressions only. The findings could aid in developing digital games able to use the information about the player’s affective state to adapt their content and support the game experience.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/251227
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