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.
@article{arxiv.2310.17383,
title = {On the recognition of the game type based on physiological signals and eye tracking},
author = {Łukasz Czekaj and Łukasz Radzinski and Mateusz Kolimaga and Jakub Domaszewicz and Robert Kitłowski and Mariusz Szwoch and Włodzisław Duch},
journal= {arXiv preprint arXiv:2310.17383},
year = {2023}
}