Using IMU Data to Demonstrate Hand-Clapping Games to a Robot

All over the world, people find joy and amusement in playing hand-clapping games such as "Pat-a-cake" and "Slide." Thus, as robots enter everyday human spaces and work together with people, we see potential for them to entertain, engage, and assist humans through cooperative clapping games. This paper explores how data recorded from a pair of commonly available inertial measurement units (IMUs) worn on a human's hands can contribute to the teaching of a hand-clapping robot. We identified representative hand-clapping activities, considered approaches to classify games, and conducted a study to record hand-clapping motion data. Analysis of data from fifteen participants indicates that support vector machines and Markov chain analysis can correctly classify 95.5% of the demonstrated hand-clapping motions (from ten discrete actions) and 92.3% of the hand-clapping game demonstrations recorded in the study. These results were calculated by withholding a participant's entire dataset for testing, so these results should represent general system behavior for new users. Overall, this research lays the groundwork for a simple and efficient method that people could use to demonstrate hand-clapping games to robots.