Detection of the moment when a muscle begins to activate on the basis of EMG signal is important task for a number of biomechanical studies. In order to provide high accuracy of EMG onset detection, we developed novel method, that give results similar to that obtained by an expert. By means of this method, EMG is processed in two stages. The first stage gives rough estimation of EMG onset, whereas the second stage performs local, precise searching. The method was applied to support signal processing in biomechanical study concerning effect of body position on EMG activity and peak muscle torque stabilizing spinal column under static conditions
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.
We show how the applications utilizing a Future Internet architecture can benefit from its features like quality of service (QoS) provisioning and resources reservation. We demonstrate, how proposed applications address content, context and user awareness basing on the underlying Next Generation Network (NGN) infrastructure and how it can be used to host service-based applications.
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