Net-based phase-analysis in motion processes

Back in the 1990s, one of the major problems when analysing motion processes often was a lack of data. In recent times, the situation has completely changed. Data are available nearly unlimitedly, and the problem now is to detect the important information hidden in that huge amount of automatically recorded data. Data-mining approaches are often not really helpful as long as it is not clear what to look for or what the striking features are. During the last 10 years, artificial neural networks of type Kohonen Feature Map (KFM) became more and more helpful in the area of motion data analysis by reducing data and classifying them to useful information [see W. Schöllhorn and J. Perl, Prozessanalysen in der Bewegungs- und Sportspielforschung , Spectrum der Sportwissenschaft 14 (1) (2002), pp. 30--52 and J. Perl, A neural network approach to movement pattern analysis . Hum. Mov. Sci. 23 (2004), pp. 605--620]. It should be added that for some of the described applications, in particular in the case of two-level analysis in Section 2.3, the special KFM-derivate Dynamically Controlled Network (DyCoN) is necessary [see J. Perl, DyCoN : Ein neuer Ansatz zur Modellierung und Analyse von Sportspie-Prozessen mit Hilfe neuronaler Netze, in Sportspiele Erleben, Vermitteln, Trainieren , K. Ferger, N. Gissel, and J. Schwier, Hrsg., Czwalina, Hamburg, 2002, S. 253--265]. In the following sections, some concepts and current approaches in the field of net-based data analysis are presented, and a case study demonstrates how it works in practice.