On the recognition of the game type based on physiological signals and eye tracking
This work addresses cognitive activity recognition for applications like smart surveillance and quantified self, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of recognizing cognitive activities by classifying which of three video games a participant is playing, using physiological signals and eye tracking, achieving validation in both player-dependent and player-independent scenarios.
Automated interpretation of signals yields many impressive applications from the area of affective computing and human activity recognition (HAR). In this paper we ask the question about possibility of cognitive activity recognition on the base of particular set of signals. We use recognition of the game played by the participant as a playground for exploration of the problem. We build classifier of three different games (Space Invaders, Tetris, Tower Defence) and inter-game pause. We validate classifier in the player-independent and player-dependent scenario. We discuss the improvement in the player-dependent scenario in the context of biometric person recognition. On the base of the results obtained in game classification, we consider potential applications in smart surveillance and quantified self.