Modeling the constraints of human hand motion

John Lin, Ying Wu, T. S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

198 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings - Workshop on Human Motion, HUMO 2000
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0769509398, 9780769509396
StatePublished - 2000
EventWorkshop on Human Motion, HUMO 2000 - Austin, United States
Duration: Dec 7 2000Dec 8 2000

Publication series

NameProceedings - Workshop on Human Motion, HUMO 2000


OtherWorkshop on Human Motion, HUMO 2000
CountryUnited States

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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