Inverse kinematics positioning using nonlinear programming for highly articulated figures
ACM Transactions on Graphics (TOG)
Prediction of human reach posture using a neural network for ergonomic man models
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Kinematic and Dynamic Simulation of Multibody Systems: The Real Time Challenge
Kinematic and Dynamic Simulation of Multibody Systems: The Real Time Challenge
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
SIAM Journal on Optimization
An inverse kinematics architecture enforcing an arbitrary number of strict priority levels
The Visual Computer: International Journal of Computer Graphics - Special section on implicit surfaces
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Digital human modeling provides a valuable tool for designers when implemented early in the design process. Motion capture experiments offer a means of validation of the digital human simulation models. However, there is a gap between the motion capture experiments and the simulation models, as the motion capture results are marker positions in Cartesian space and the simulation model is based on joint space. Therefore, it is necessary to map the motion capture data to simulation models by employing a posture reconstruction algorithm. Posture reconstruction is an inherently redundant problem where the collective distance error between experimental joint centers and simulation joint centers is minimized. This paper presents an optimization-based method for determining an accurate and efficient solution to the posture reconstruction problem. The procedure is used to recreate 120 experimental postures. For each posture, the algorithm minimizes the distance between the simulation model joint centers and the corresponding experimental subject joint centers which is called the mean measurement error.