ABSTRACT
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.
- Andrea Bonarini. 2014. Timing issues in physically interacting robogames. In Timing in Human-Robot Interaction Workshop at 9th ACM/IEEE Conference on Human-Robot Interaction.Google Scholar
- Ginevra Castellano, Santiago D Villalba, and Antonio Camurri. 2007. Recognising human emotions from body movement and gesture dynamics. In International Conference on Affective Computing and Intelligent Interaction. Springer, 71--82. Google ScholarDigital Library
- Marlon Etheredge, Ricardo Lopes, and Rafael Bidarra. 2013. A generic method for classification of player behavior. Citeseer.Google Scholar
- Cheng Guo and Ehud Sharlin. 2008. Exploring the use of tangible user interfaces for human-robot interaction:a comparative study. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 121--130.Google ScholarDigital Library
- Basel Kikhia, Miguel Gomez, Lara Lorna Jiménez, Josef Hallberg, Niklas Karvonen, and Kåre Synnes. 2014. Analyzing body movements within the laban effort framework using a single accelerometer. Sensors 14, 3 (2014), 5725--5741. Google ScholarCross Ref
- Diego Martinoia, Daniele Calandriello, and Andrea Bonarini. 2013. Physically interactive robogames:Definition and design guidelines. Robotics and Autonomous Systems 61, 8 (2013), 739--748. Google ScholarCross Ref
Index Terms
- Modeling Player Activity in a Physical Interactive Robot Game Scenario
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