This operation is the most general of the skeleton calibration operations. It is used to fully calibrate a labeling skeleton from a trial in which the subject is moving. This is normally a ROM trial but can sometimes be a dynamic trial.
Functional Skeleton Calibration optimizes both joint and marker positions. It also calculates joint and marker statistics.
Ensure the trial covers the full range of motion that is expected in the dynamic trials.
The Functional Skeleton Calibration operation runs two algorithms:
❙ The first optimizes the skeleton segment and marker parameters. This is done using a subset of the frames in the trial. These are chosen to get the subject in a variety of poses. The more frames that are considered, the better the skeleton will be, however using more frames makes the calibration take longer.
The calibration algorithm simultaneously tries to get the skeleton marker positions to be as close as possible to the corresponding labeled reconstructions. It does this by changing the joint angles, segment poses and, marker positions. It considers only the selected frames, so selecting more frames gives the algorithm more poses to try to match. The algorithm minimizes a statistical distance measuring how close the skeleton markers are to the reconstructions. This distance accounts for the fact that some skeleton markers (with a larger covariance) are expected to be found a larger physical distance away from their reconstructions. The default parameters reset this covariance to the template covariance (in the VST). The motion that is allowed between segments is constrained by the joint type. Any joint type mis-modeling will not be absorbed into the joint, but rather by either the segment or marker positions, where the effect will have less impact. In sparse marker sets this is sometimes a trade-off that has to be made.
❙ The second algorithm calculates the joint and marker statistics (see Calculate Skeleton Joint & Marker Statistics operation).
❙ This calibration method requires more processing time but generates a skeleton with the best quality labeling results. This is because the method provides a large amount of data for markers and joint movement.
❙ Creating a custom labeling skeleton template defined using the labeling template builder.
For information on how to use this operation in common Nexus workflows, see Subject calibration workflows.