Fuzzy C-Means Clustering for Motion Capture Tennis Time-Series Data

Abstract

Creating the proper player profile in training is crucial for athlete development. Although there is a great number of studies concerning this subject, there is no solution that would allow to model it in a convenient way. Applying fuzzy modelling clustering can be useful in this field. Moreover, the application of sophisticated acquisition techniques, like motion capture systems, allow ones to obtain accurate data corresponding to athlete’s movement in the form of a multivariate time series. In this study, the authors undertook the task of clustering the most important at the stage of training tennis strokes such as: Forehand, backhand, and volley. They were represented as trajectories of the tennis racket based on four retro-reflective markers attached to it. The Fuzzy C-Means algorithm, which utilizes the dynamic time warping-based distance to cluster analysis of tennis strokes, has been applied with highest success to group various kinds of movement of tennis players. The comprehensive analysis included numerous separate tennis moves and their groups. The clustering have been performed for: 1) separated tennis moves, 2) grouped forehand and backhand groundstrokes, and 3) all analysed tennis movements. The obtained results allowed creation of the reference stroke model, which can be used for further examination of the tennis players’ performance. The Fuzzy C-Means has been thoroughly compared with K-Means, Gaussian Mixture, and Birch method. The following evaluation metrics have been applied: Rand Index, Adjusted Rand Index, Normalized Mutual Information, Silhouette score, Davies-Bouldin Index and Calinski-Harabasz score.

Keywords

• Birch clustering
• dynamic time warping
• fuzzy C-means
• Gaussian mixture clustering
• K-means clustering
• tennis forehand