  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 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 rotary joint model to the input data. A hierarchical structure
for the articulated model can also be determined in situations where the
topology of the model is not known. Once the system topology and joint
parameters have been recovered, the resulting model can be used to perform
forward and inverse kinematic procedures. We present the results of using 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.

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