View source for Decomposition of Human Motion into Dynamics Based Primitives with Application to Drawing Tasks

{{HTDB paper
| authors = D. Del Vecchio, R. M. Murray and P. Perona
| title = Decomposition of Human Motion into Dynamics Based Primitives with Application to Drawing Tasks
| source = <i>Automatica</i>, 39(12):2085-2098
| year = 2003
| type =  
  Preprint
| funding = NSF
| url = http://www.cds.caltech.edu/~murray/preprints/dmp03-automatica.pdf
| abstract = 
Using tools from dynamical systems and systems identi&#64257;cation we develop a framework for the study
of primitives for human motion, which we refer to as movemes. The objective is understanding human
motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet
of dynamical systems. In this work we address the problem of de&#64257;ning conditions under which
collections of signals are well-posed according to a dynamical model class M and then can generate
movemes. Based on the assumption of well-posedness, we develop segmentation and classi&#64257;cation algorithms
in order to reduce a complex activity into the sequence of movemes that have generated it.
Using examples we show that the de&#64257;nition of well-posedness can be applied in practice and show
analytically that the proposed algorithms are robust with respect to noise and model uncertainty. We
test our ideas on data sampled from &#64257;ve human subjects who were drawing &#64257;gures using a computer
mouse. Our experiments show that we are able to distinguish between movemes and recognize them
even when they take place in activities containing more than one moveme at a time.
| flags = 
| tag = dmp03-automatica
| id = 2002f
}}