Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes.

Sports training generally focuses on speed of response and variety of strategies aimed at encouraging sustainable physical activity and improving learning skills for players, thus, enhance their performance and skills through matches. In this paper, we are presenting a methodology for multi-sensor fusion method of IR depth camera and smart band sensors using curve fitting to recognize and group the characteristics of strokes performed by players and adapt the training accordingly. The main aim of the methodology is to classify various techniques played in table tennis and enhance the strokes based on various body joints. Moreover, the main contribution of this paper is to experiment different sensors to get the most optimal tools in classifying players strokes. Also, to test various classification algorithms to get the optimal and best result possible. Overall, based on the experiments we have concluded that sensor fusion between internal sensors and IR depth camera has increased the classification results and robustness of the solution. The system's results indicate an average accuracy of 95% - 100%. [ABSTRACT FROM AUTHOR]