Publication: 3D hand trajectory segmentation by curvatures and hand orientation for classification through a probabilistic approach
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ISBN: 978-1-4244-3804-4 (online)
ISBN: 978-1-4244-3803-7 (print)
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2009-12
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IEEE
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Abstract
In this work we present the segmentation and
classification of 3D hand trajectory. Curvatures features are
acquired by (r, θ, h) and the hand orientation is acquired by
approximating the hand plane in 3D space. The 3D positions of
the hand movement are acquired by markers of a magnetic
tracking system [6]. Observing humans movements we perform
a learning phase using histogram techniques. Based on the
learning phase is possible classify reach-to-grasp movements
applying Bayes rule to recognize the way that a human grasps
an object by continuous classification based on multiplicative
updates of beliefs. We are classifying the hand trajectory by its
curvatures and by hand orientation along the trajectory
individually. Both results are compared after some trials to
verify the best classification between these two kinds of
segmentation. Using entropy as confidence level, we can give
weights for each kind of classification to combine both,
acquiring a new classification for results comparison. Using
these techniques we developed an application to estimate and
classify two possible types of grasping by the reach-to-grasp
movements performed by humans. These reported steps are
important to understand some human behaviors before the
object manipulation and can be used to endow a robot with
autonomous capabilities (e.g. reaching objects for handling).
Description
Proceedings of: The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), October 11-15, 2009, St. Louis, USA
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Bibliographic citation
The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009) (pp. 1284-1289). IEEE, 2009