Zusammenfassung
Movements are time-dependent processes and so can be modelled by time-series
of coordinates: E.g., each articulation has geometric coordinates;
the set of the coordinates of the relevant articulations build a
high-dimensional configuration. These configurations--or "patterns"--give
reason for analysing movements by means of neural networks: The Kohonen
Feature Map (KFM) is a special type of neural network, which (after
having been coined by training with appropriate pattern samples)
is able to recognize single patterns as members of pattern clusters.
This way, for example, the particular configurations of a given movement
can be identified as belonging to respective configuration clusters,
where the sequence of clusters to which the time-depending configurations
belong, characterizes the process as a 2-dimensional trajectory.
The advantages of this method are that: the high dimensionality of
the original processes is reduced to two dimensional trajectories,
the clusters are automatically determined by the network, and all
data for further analyses can automatically be transferred into a
data base. Thus, the processes can either be visualized and analysed
by an expert or again processed by further automatic analysing tools,
as has been done with similarity matrices. The disadvantage is that
a KFM-training needs a huge amount of information, which normally
is not available from experiments. However, the Dynamically Controlled
Network DyCoN (a special type of KFM) makes it possible to reduce
the amount of original training data substantially--e.g., by adding
stochastically generated ones. Currently, DyCoN is used in several
projects in order to generally support analyses of processes in sport.
It should be emphasized that the presented approach is not meant
to improve the understanding or to develop models of human movement
but to give a survey of the advantages and methodological aspects
of net-based movement analysis.
Nutzer