Efficient, Automated Parameter Optimization Method for Marker-Based Human Motion Capture

EasyChair Preprint 13436

Abstract

Optical motion capture is highly dependent on the geometry of the underlying rigid body model, so obtaining as accurate a model as possible is of great importance. This paper shows an optimization method that performs the scaling together with the identification of other model parameters, such as rotation axes, by recording a few motions and solving a single large-scale nonlinear optimization problem. The combination of analytical derivatives and a sparse solver leads to a very efficient implementation, which which allows for fitting the model to the measured markers in a matter of seconds.

Keyphrases: Biomechanics, Optimization, kinematics, motion capture

@booklet{EasyChair:13436,
  author    = {Urbano Lugrís and Santiago Beron and Florian Michaud and Javier Cuadrado},
  title     = {Efficient, Automated Parameter Optimization Method for Marker-Based Human Motion Capture},
  howpublished = {EasyChair Preprint 13436},
  year      = {EasyChair, 2024}}