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Ohne Zweifel kein Wissen und keine Innovation, dies gilt für die Forschung im Allgemeinen und natürlich auch an unserer Hochschule. Gerade in der Wissenschaft ist der methodische Zweifel oft der Ausgangspunkt einer spezifischen Untersuchung. Er soll dabei behilflich sein, Klarheit zu erlangen. Frei nach dem Philosophen Rene Descartes: Was kann ich eigentlich mit Sicherheit wissen? Nur wer ab und an zweifelt, der schaut um die Ecke, stellt sich, andere und seine Umwelt in Frage, sucht nach neuen Wegen, Antworten und strebt nach Veränderung. Und auch dort, wo Wissenschaft vermittelt wird, also im Seminar, in einer Übung oder Vorlesung, muss Platz sein für eine selbstreflexive Grundhaltung. An der H-BRS ist Zweifeln also nicht nur erlaubt, sondern erwünscht.
Doubting - Path to Science
(2016)
Förderpreise 2016
(2016)
During exercise, heart rate has proven to be a good measure in planning workouts. It is not only simple to measure but also well understood and has been used for many years for workout planning. To use heart rate to control physical exercise, a model which predicts future heart rate dependent on a given strain can be utilized. In this paper, we present a mathematical model based on convolution for predicting the heart rate response to strain with four physiologically explainable parameters. This model is based on the general idea of the Fitness-Fatigue model for performance analysis, but is revised here for heart rate analysis. Comparisons show that the Convolution model can compete with other known heart rate models. Furthermore, this new model can be improved by reducing the number of parameters. The remaining parameter seems to be a promising indicator of the actual subject’s fitness.
Analyzing training performance in sport is usually based on standardized test protocols and needs laboratory equipment, e.g., for measuring blood lactate concentration or other physiological body parameters. Avoiding special equipment and standardized test protocols, we show that it is possible to reach a quality of performance simulation comparable to the results of laboratory studies using training models with nothing but training data. For this purpose, we introduce a fitting concept for a performance model that takes the peculiarities of using training data for the task of performance diagnostics into account. With a specific way of data preprocessing, accuracy of laboratory studies can be achieved for about 50% of the tested subjects, while lower correlation of the other 50% can be explained.
The Fitness-Fatigue model (Calvert et al. 1976) is widely used for performance analysis. This antagonistic model is based on a fitness-term, a fatigue-term, and an initial basic level of performance. Instead of generic parameter values, individualizing the model needs a fitting of parameters. With fitted parameters, the model adapts to account for individual responses to strain. Even though in most cases fitting of recorded training data shows useful results, without modification the model cannot be simply used for prediction.