Abstract
A prototype system for monitoring, transmitting and processing performance data in sports for the purpose of providing feedback has been developed. During training, athletes are equipped with a mobile device and wireless sensors using the ANT protocol in order to acquire biomechanical, physiological and other sports specific parameters. The measured data is buffered locally and forwarded via the Internet to a server. The server provides experts (coaches, biomechanists, sports medicine specialists etc.) with remote data access, analysis and (partly automated) feedback routines. In this way, experts are able to analyze the athlete’s performance and return individual feedback messages from remote locations.
Highlights
Coaches and athletes require effective methods to improve sports performance
In conjunction with the heart rate, the lag can be used for calculating the heart rate variability (HRV), a subject specific parameter used for more detailed analyses
As shown in the graphical user interface (GUI) design of the A-client in Figure 6, the application consists of three tabs: Setup, Channel-Data and GPS
Summary
Coaches and athletes require effective methods to improve sports performance. As a consequence of advances in technology, systems have been constructed to present relevant sports specific feedback information to athletes during and shortly after training and competition. Our main research goal is to establish a mobile coaching system that is capable of integrating the above mentioned information and communication technologies and makes use of their advantages for the purpose of providing intelligent ―online-feedbacks‖ This system requires a bidirectional information flow and a framework that includes prompt and suitable sensor data acquisition solutions as well as methodologies for the detection of relevant information during training. The intention is to design effective services for the convergence of mobile devices and sensor networks in terms of feature extraction, data reduction and information retrieval [18] Another complex task is to implement and adapt intelligent algorithms based on the integration of training data as well as individual performance profile data into knowledge-based and expert systems for the automatic generation of feedback (e.g., [19]).
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