Modeling Player Activity in a Physical Interactive Robot Game Scenario

We propose a quantitative human player model for Physically Interactive RoboGames that can account for the combination of the player activity (physical effort) and interaction level. The model is based on activity recognition and a description of the player interaction (proximity and body contraction index) with the robot co-player. Our approach has been tested on a dataset collected from a real, physical robot game, where activity patterns extracted by a custom 3-axis accelerometer sensor module and by the Microsoft Kinect sensor are used. The proposed model design aims at inspiring approaches that can consider the activity of a human player in lively games against robots and foster the design of robotic adaptive behavior capable of supporting her/his engagement in such type of games.