Computer animation of knowledge-based human grasping
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
ACM Transactions on Graphics (TOG)
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
SIAM Journal on Optimization
Handrix: animating the human hand
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
A new digital human environment and assessment of vehicle interior design
Computer-Aided Design
Development of the virtual-human Santos®
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Understanding hand gestures using approximate graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Memory-Based Human Motion Simulation for Computer-Aided Ergonomic Design
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Grasping is an essential requirement for digital human models (DHMs). It is a complex process and thus a challenging problem for DHMs, involving a skeletal structure with many degrees-of-freedom (DOFs), cognition, and interaction between the human and objects in the environment. Furthermore, grasp planning involves not only finding the shape of the hand and the position and orientation of the wrist but also the posture of the upper body required for producing realistic grasping simulations. In this paper, a new methodology is developed for grasping prediction by combining a shape-matching method and an optimization-based posture prediction technique. We use shape matching to pick a hand shape from a database of stored grasps, then position the hand around the object. The posture prediction algorithm then calculates the optimal posture for the whole upper body necessary to execute the grasp. The proposed algorithm is tested on a variety of objects in a 3-D environment. The results are realistic and suggest that the new method is more suitable for grasp planning than conventional methods. This improved performance is particularly apparent when the nature of the grasped objects is not known a priori, and when a complex high-DOF hand model is necessary.