The following standard workflow descriptions outline recommended ways of working with various scenarios. Your choice of workflow depends upon the raw data you are able to collect and your desired outcome.
The following workflows are the recommended ways of working when you are setting up a subject for labeling.
This is a recommended workflow for setting up a subject for labeling when you want to produce a labeling skeleton that can be used for trials that capture simple data, such as basic gait, non-ballistic/sports movements, or other movements that are not multi-segment, high velocity, or complex, where segments or markers tend to interact. This method uses less data (single static frame) than Functional Skeleton Calibration, and can be processed very quickly.
1. Put markers on the subject and get them to perform a static trial.
2. Reconstruct the trial and run the Auto Initialize Labeling pipeline.
The Auto Initialize Labeling pipeline consists of three operations:
1. A T-pose label operation (Autolabel Static). This operation labels the trial for the following two operations to use.
2. Subject scale (Scale Subject VSK). This operation takes the labeled reconstruction cloud and scales the template skeleton to be the same size. This enables you to use the same template skeleton for both children and adults.
3. Static Skeleton Calibration - Markers Only. This operation finishes off the set up by moving the skeleton markers to the correct locations in the segment coordinate frames. This is to allow for the variable placement of the markers.
This workflow calibrates both the bone lengths and marker positions from a single frame. However, the calibration is split over two operations. Scaling the subject changes all of the bone lengths by the same factor. The marker-only calibration can then use the scaled skeleton to optimize the marker positions.
The standard Auto Initialize Labeling workflow is useful in cases where the subject’s ability to perform a full ROM trial might be limited or where total time of collection/calibration is paramount. In these types of collection scenarios, the Auto Initialize Labeling pipeline will often produce completely acceptable labeling. If less than ideal labeling performance is found, the addition of the Calculate Skeleton Joint & Marker Statistics operation can improve labeling.
To do this, you (semi-)manually label one of the dynamic trials and run a Calculate Skeleton Joint & Marker Statistics operation on it. This calculates the joint and marker statistics that represent the subject in that particular activity.
Important: Ensure that the trial contains no labeling errors, as any errors have the potential to significantly increase the estimated covariance of affected markers.
This workflow for setting up a subject provides more information (multi-frame, multi-joint range movements) to the Nexus subject calibrator and gives the best labeling performance in most scenarios. However, the increased amount of calibration data results in higher processing times than the simpler Static method (see Auto Initialize Labeling pipeline above).
This workflow consists of the following steps:
1. The subject performs a range of motion trial in which they fully exercise all of their joints. It is recommended that the subject starts the ROM trial in the static autolabel pose, so that the Auto Initialize Labeling pipeline can be run on the first frame to generate a skeleton that can be used to help label the rest of the ROM trial.
2. After the trial has been captured you must reconstruct and label it. The recommended way of doing this is to run the Auto Initialize Labeling pipeline on a T-pose frame and use the skeleton generated by that operation to label the rest of the trial.
Important: If the trial is being labeled semi-automatically, scrub through the trial to make sure that all of the labels are correct. Incorrect labels degrade the quality of the calibration.
3. After you have labeled the trial, you run the Functional Skeleton Calibration operation. This calculates bone lengths, marker positions, and skeleton statistics.
You may find yourself in a situation where a quick recalibration is preferable to performing a new full calibration. The following are two examples where a recalibration operation may be preferable to a full calibration.
Some capture sessions involve trials in which the subject is wearing an orthosis and others without. If the othosis is large or moving significantly with respect to the segment(s), the trials with the orthosis might not label well. In this case you might want a quicker calibration procedure than a full Functional Skeleton Calibration.
One way of achieving this is to capture a second ROM trial with the orthosis. Instead of running a full Functional Skeleton Calibration, you could run a Functional Skeleton Calibration - Markers Only operation to update the marker positions and the subject statistics for the trials using the orthosis.
Markers sometimes get knocked off the subject and need to be re-applied. In this case you can use a frame in which the marker has been re-applied to run a Static Skeleton Calibration - Markers Only operation to recalibrate the marker that had fallen off.
In this situation it is highly likely that the marker covariance will not need to be updated so you do not need to run a Functional Skeleton Calibration - Markers Only operation.