Analyzing Movement Trajectories Using a Markov Bi-Clustering Method
Keren Erez, Jacob Goldberger, Ronen Sosnik, Moshe Shemesh and Moshe Abeles
Journal of Computational Neuroscience
In this study we treat scribbling motion as a compositional system
in which a limited set of elementary strokes are capable of
concatenating amongst themselves in an endless number of
combinations, thus producing an unlimited repertoire of complex
constructs. We broke the continuous scribblings into small units
and then calculated the Markovian transition matrix between the
trajectory clusters. The Markov states are grouped in a way that
minimizes the loss of mutual information between adjacent strokes.
The grouping algorithm is based on a novel markov-state
bi-clustering algorithm derived from the Information-Bottleneck principle.
This approach hierarchically decomposes scribblings into increasingly
finer elements. We illustrate the usefulness of
this approach by applying it to human scribbling.