This paper presents methods for collecting and analyzing biomechanical and physiological data from several body sensors during recreational runs in order to classify an athlete's perceived fatigue state. Heart rate, heart rate variability, running speed, stride frequency and biomechanical data were recorded continuously from 431 runners during a free one-hour outdoor run. During the activity the sportsmen answered questions about their perceived fatigue state in 5 min intervals. The data were analyzed using specifically designed features computed for each of the 5 min intervals. The features were used to train different classifiers, which were able to distinguish two levels of the runner's fatigue state with an accuracy of 88.3 % across multiple study participants. Feature selection evidenced that a heart rate variability feature and two biomechanical features were best suited for classification of the perceived fatigue level. Therefore, the classification system needs the information from various sensors on the human body. The resulting classifier was implemented on an embedded microcontroller to show that it would be feasible to integrate it directly into a body sensor network. Such a wearable classification system for fatigue can be used to support sportsmen, for example by changing their training plan or by adapting their equipment to the specific needs of a fatigued athlete.

We developed a software tool for runners and other sportsmen that is capable of visualizing recorded Global Position System (GPS) data of sports activity. If available, heart rate (HR) as well as other meta data can be visualized along with the position. The goal of the work was to create public domain software under the general public license (GPL) for post-training-analysis of running sessions and the possibility to exchange the visualized result with others via the Internet. The software and its sources are available for download. The tool was realized using Google Earth and its Keyhole Markup Language (KML), which allows to visualize the data directly in Google Earth and to pass visualized data to others. Given the input data, our software creates a KML file. Besides the running path it is also possible to visualize speed and heart rate as well as other information such as elapsed time, average speed, average heart rate, running distance and altitude. Data from different sources can be automatically registered in order to improve visualization.