Net-based phase-analysis in motion processes

Jürgen Perl

Mathematical and Computer Modelling of Dynamical Systems, 2010, vol. 16, issue 5, 465-475

Abstract: 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.

Date: 2010
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13873954.2010.507097 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:nmcmxx:v:16:y:2010:i:5:p:465-475

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/NMCM20

DOI: 10.1080/13873954.2010.507097

Access Statistics for this article

Mathematical and Computer Modelling of Dynamical Systems is currently edited by I. Troch

More articles in Mathematical and Computer Modelling of Dynamical Systems from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().