In this paper we present a sensor-based table tennis stroke detection and classification system. We attached inertial sensors to table tennis rackets and collected data of 8 different basic stroke types from 10 amateur and professional players. Firstly, single strokes were detected by a event detection algorithm. Secondly, features were computed and used as input for stroke type classification. Multiple classifiers were compared regarding classification rates and computational effort. The overall sensitivity of the stroke detection was 95.7% and the best classifier reached a classification rate of 96.7%. Therefore, our presented approach is able to detect table tennis strokes in time-series data and to classify each stroke into correct stroke type categories. The system has the potential to be implemented as an embedded real-time application for other racket sports, to analyze training exercises and competitions, to present match statistics or to support the athletes' training progress. To our knowledge, this is the first paper that addresses a wearable support system for table tennis, and our future work aims at using the presented results to build a complete match analysis system for this sport.
© Copyright 2015 Proceedings of the 2015 ACM International Symposium on Wearable Computers - Osaka, Japan — September 07 - 11, 2015. Published by ACM. All rights reserved.