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We present a data fusion-based methodology for supporting the sports training. Training sessions are planned by coach on the basis of the analyzed data obtained during each training session. The data are usually acquired from various sensors attached to the athlete (e.g., accelerometers or gyroscopes). One of the techniques dedicated to processing the data originatnig from different sources is data fusion. The data fusion in sports training provides new procedures to acquire, to process, and to analyze the sports training related data. To verify the effectiveness of the data fusion methodology, we design a system to analyze training sessions of a tennis player. The main functionalities of the system are the tennis strokes detection and the classification based on data gathered from the wrist-worn sensor. The detection and the classification of tennis strokes can reduce the time a coach spends in analyzing the trainees’ data. Recreational players for self-learning may also use these functionalities. In the proposed approach, we used Mel-Frequency Cepstrum Coefficients, determined from the accelerometer data, to build the feature vector. The data are gathered from amateur and professional athletes. We tested the quality of the designed feature vector for two different classification methods, that is, k-Nearest Neighbors and Logistic Regression. We evaluate the classifiers by applying two tests: 10-fold cross-validation and leave-one-out techniques. Our results demonstrate that data fusion-based approach can be used effectively to analyze athlete’s activities during the training.

Data Fusion in Ubiquitous Sports Training: Methodology and Application