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Modeling the constraints of human hand motion

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3 Author(s)
Lin, J. ; Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA ; Ying Wu ; Huang, T.S.

Hand motion capture is one of the most important parts of gesture interfaces. Many current approaches to this task generally involve a formidable nonlinear optimization problem in a large search space. Motion capture can be achieved more cost-efficiently when considering the motion constraints of a hand. Although some constraints can be represented as equalities or inequalities, there exist many constraints which cannot be explicitly represented. In this paper, we propose a learning approach to model the hand configuration space directly. The redundancy of the configuration space can be eliminated by finding a lower-dimensional subspace of the original space. Finger motion is modeled in this subspace based on the linear behavior observed in the real motion data collected by a CyberGlove. Employing the constrained motion model, we are able to efficiently capture finger motion from video inputs. Several experiments show that our proposed model is helpful for capturing articulated motion

Published in:

Human Motion, 2000. Proceedings. Workshop on

Date of Conference: