Automatic Joint Parameter Estimation from Magnetic Motion Capture Data
James F. O'Brien, Bobby Bodenheimer, Gabriel Brostow, and Jessica K. Hodgins
This paper describes a technique for using magnetic motion capture
data to determine the joint parameters of an articulated hierarchy.
This technique makes it possible to determine the limb lengths,
joint locations, and sensor placement for a human subject without
external measurements. Instead, the joint parameters are inferred
with high accuracy from the motion data acquired during the capture
session. The parameters are computed by performing a linear least
squares fit of a revolute joint model to the input data. A
hierarchical structure can also be determined in situations where
the topology of the articulated model is not known. We present the
results of running the algorithm on human motion capture data, as
well as validation results obtained with data from a simulation and
a wooden linkage of known dimensions.
Last modified: Thu Feb 10 10:06:28 2000