Publikation
Unsupervised Segmentation of Human Manipulation Movements into Building Blocks
Lisa Gutzeit; Frank Kirchner
In: IEEE Access, Vol. 10, Pages 125723-125734, IEEE, 12/2022.
Zusammenfassung
During the last years, new approaches were proposed in which robotic behavior is generated
by imitating human movement examples. This process can be sustainably simplified by an automatic
detection of the movement sequences which should be imitated. For this, automated approaches for human
movement segmentation are needed to avoid time-intensive manual data analysis. Suitable examples for
imitation learning are building blocks movements, which are basic movements that can be combined to
solve different tasks. Recently, we introduced the velocity-based Multiple Change-point Inference (vMCI)
algorithm, which automatically segments human demonstrations of manipulation movements into sequences
with a bell-shaped velocity of the hand which is said to be a characteristic feature of manipulation building
blocks.
In this paper, the velocity of the hand as well as other features of human manipulation movements
recorded with a marker-based motion tracking system are evaluated with respect to their suitability to detect
segment boundaries of manipulation building blocks. Additionally, we perform a more intensive evaluation
of vMCI compared to the original publication by evaluating the algorithm on different manipulation
movement demonstrations recorded from several subjects and comparing the approach to other state-ofthe-
art segmentation algorithms. The results support the assumption that the velocity of the hand is one
of the main features to detect segment boundaries in human manipulation movements and that the vMCI
algorithm can detect these segment borders online and unsupervised, also in movement recordings with a
noisy velocity.