Hierarchical Segmentation of Human Manipulation Movements
Lisa Gutzeit
In Proc. of the 26th International Conference on Pattern Recognition, (ICPR-2022), 21.8.-25.8.2022, Montreal, QC, IEEE Computer Society, pages 2742-2748, Aug/2022.
Zusammenfassung (Abstract)
:
This paper introduces a segmentation algorithm,
which splits complex human manipulation movements into movement segments and automatically groups these into labeled
actions. With this hierarchical algorithm the basic movement
identities, which we call building blocks, as well as their concatenation to more complex actions can be identified in one
handed as well as dual arm human manipulation movements.
The algorithm can be used, e.g., in robotic applications such
as imitation learning, in which human movement examples are
directly used to generate robotic behavior. In this paper, we
present two variants of the hierarchical segmentation algorithm,
one supervised approach which requires a small number of prelabeled movements as training data, as well as an approach which
uses unsupervised algorithms to group building block segments
which belong to the same movement. In both variants, the building block movements are detected based on the velocity of the
hand(s), using the velocity-based multiple change-point inference
algorithm. We evaluate both methods on human manipulation
movements recorded from several participants with a markerbased motion tracking system. The first evaluations are done
on simple one-handed point-to-point movements, followed by an
evaluation on a complex dual arm manipulation task. The results
show, that the presented approaches are able to identify basic
movements as well as their concatenations into more complex,
labeled action