Expert-novice classification of mobile game player using smartphone inertial sensors

Abstract

The gaming industry has seen a tremendous growth in the last decade due to an exponential increase in the number of smartphone users. Embedded smartphone sensors provide solutions for automatic game controls during game-play. In this paper, we present an experimental study for the expertise classification of a game (mobile-based) player using smartphone inertial sensors, while they are simultaneously used for game controls. The game expertise level of participants is either labeled as expert or novice using game scores. Towards this end, data from 38 participants are curated during Traffic Racer game-play (in three different trials) using the embedded gyroscope and accelerometer sensors of the smartphone. These signals are pre-processed using Savitzky-Golay smoothing filter to remove noise. Twenty time domain features are extracted from the pre-processed data and are subjected to the wrapper-based feature selection method to select an optimum subset of features. Three classifiers, including k-nearest neighbor (k-NN), random forest, and the Naive Bayes, are evaluated towards the classification of player’s expertise level, i.e., expert and novice. The best average accuracy of 92.1% is achieved with k-NN classifier using the fusion of gyroscope and accelerometer data, which outperforms the existing state-of-the-art methods.