Cleaning Up Your Data

This page deals with issues of missing and messy data. In an ideal capture session, your captured trials will be reconstructed perfectly, with no gaps or missing markers. In reality, markers disappear with some frequency. How often this occurs will depend on a multitude of factors. At the most basic level, data quality is dependent upon: the number and placement of motion cameras, speed of subject movement, and frequency of marker occlusion.

Gap filling and data cleaning are the bane of motion capture operators. It is far quicker to spend 20 minutes optimising the capture volume before a session starts, than it is to spend hours, days, or even months cleaning up bad data. To optimise your system requires an understanding of how it works. Thus, this section concludes with a few Tips on Getting Clean Data.

There are five general types of data issues: 1) markers disappearing momentarily, 2) markers becoming unlabeled (gray), and then becoming labeled again (yellow/coloured), 3) markers being incorrectly labeled after disappearing momentarily, 4) markers disappearing and then reappearing only to stay unlabeled, and 5) markers moving erratically at times. The first section, Gap Filling, will address the issues 1 and 2. The second section, Missing Data, will address issues 3 and 4. We have no solution to the 5th issue.

Gap Filling

This section covers the first two types of missing data. For this section, a new capture trial was conducted, with the explicit aim of creating messy data.

1. The trial should be loaded and reconstructed.

2. The first step in data cleaning is to examine the entire trial by eye, and mentally identify the various problems. Click the play button on the Time Bar.

3. Nexus does a reasonable job of automatically identifying data issues, and will often pick up small gaps imperceivable to humans when the trial is replayed at normal speed. Under the Tools pane, click the Label/Edit button, and examine the Gap Filling section.Figure 1 illustrates the Gap Filling section of the Label/Edit pane for our messy data. This section lists instances of missing data. The Trajectory column tells us which marker went missing, while the #Gaps column tells us how many times during that trial that marker went missing. 

4. Left-click on one of the marker names (Intermediate in Figure 1). The Time Bar will automatically jump to the first instance where a marker gap occurred. The blue squiggly lines don't relate to missing data, but are instead a history line of marker motion throughout the trial.

5. Notice when we click on different marker names in the Gap Filling list, the information in the Range textboxes below this changes. The range numbers indicate the number of frames before and after the current frame where the marker has gone missing. In Figure 1, the marker went missing 1 frame before the current frame, and continued to be missing for 2 more frames. Thus, Nexus jumps to the middle of the gap, not the first frame. For example, if you had a marker missing for 201 frames, you would see -100, 100. If it went missing for 20 frames, you would see -9, 10.

By taking the absolute value of these, adding them together, and adding 1, you know how many frames the marker went missing for. In the first gap instance for the Intermediate marker, the gap was 4 frames (1 + 2 + 1). 

6. It is at this point that the art of motion capture comes into play. The size of the gap will depend on what gap filling algorithm you use, how the result will look, and whether an alternate method will be needed. There are however two general approaches: 1) small gaps should be filled with the Spline Fill algorithm. 2) large gaps should be filled with the Pattern Fill algorithm. 

7. The spline fill method is an automatic method, which essentially extrapolates the missing trajectory based on the last known, and first reappearing coordinates. This method will be the primary form of gap filling you will use, and can generally be used for gap instances of around <=60 frames. The larger the gap, to more likely the spline fill will give an erratic result. The spline fill algorithm is also extremely susceptible to erratic motion occurring the last few frames before the marker gap. This is because the spline fill extrapolates the unknown trajectory based on where it was going before it disappeared. Unfortunately, a marker often disappears due to erratic motion (sometimes a result of the system itself, not the subject). 

8. The Pattern Fill algorithm is used in cases where the Spline algorithm fails. For this method, you pick another marker which you think exhibits a similar motion to the missing marker. Nexus then generates a trajectory based on the selected marker. For example, if we were dealing with two markers on a fixed bone, and one went missing, we would select the nonmissing marker. This method works best when we are dealing with body symmetric markers (e.g., lip corners). We will now run through an example of Pattern Fill. 

9. A slightly larger gap for the intermediate marker is selected. Next, note down the frame at which the gap occurred. The current frame number is displayed on the Time Bar, below the word General. In Figure 1, the current frame is 246, in Figure 2, it is 627. Click the Pick Source button, and select the Proximal marker, as shown in Figure 2. One the proximal marker has been selected, the Fill button will be enabled. Click the Fill button. 

10. You should now review the results of the fill operation. Do this by clicking the Play button, paying close attention when you approach the frames of missing data. If the gap algorithm produced erratic results, you can undo the operation by clicking control-z, or clicking the third left-most icon button under the file menu (a u-turn icon). You can redo this undone operation by hitting control-y, or the fourth left-most icon button (the other u-turn icon).

11. If you are happy with the gap fill, click Save.12. We will now examine the second

Figure 1. The Gap Filling section of the Label/Edit pane show numerous gaps in our trial.

Figure 2. Pattern Fill algorithm is used for larger gaps.

Missing Data

This section covers the third and fourth type of missing data. The third is when after a gap, markers are labelled incorrectly. The fourth is when a marker goes missing, and then comes back as gray. Nexus will not report the first issue (as is has done the labelling), and often the gap in the fourth case. Instead, you will identify these issues when you first play the reconstructed trial from the Time Bar.

1. We know of only two solutions which can be applied to these issues. The first is neat, the second is not.

2. The first solution involves manually relabelling the marker(s) in question. 

3. First, go to the place in the Time Bar where the gap issue has occurred. Under Tools pane, click the Label/Edit icon button. Under the Manual Labeling section, the set of template markers is listed. Click the first marker name. The mouse will turn into a cross hair. Select the appropriate marker, going through the entire list. When done, replay the entire trial.

4. If this operation is successful, Nexus will have relabelled the marker without unlabelling/mislabelling the same marker elsewhere in the trial.

5. If the operation is successful, save the trial. If it is not, undo the operation.

6. The second solution involves fixing the outputted data file after the rest of the gaps have been filled.

7. Sometimes Nexus is unable to automatically relabel a marker due to an error in marker reporting. This occurs because for a split second, Nexus thinks that two unique markers exist at very similar points in space. The data file for this instance is illustrated inFigure 3 (we will cover data files later in Analysis). A copy of this file can be found in the resources directory, which accompanies this manual.

8. The data lists five markers: Metacarpal, Proximal, Intermediate, Distal, and *1. Notice how marker *1 has no data points up until frame 657. At this exact frame, Distal has its last frame. Thus, Nexus is reporting the same marker in two distinct locations. We fix this issue by simply cutting the remaining data points from *1 marker, and pasting them into the Distal gap. This solution works because we can see what has happened. However, in more complex templates, with multiple markers disappearing, this solution may not work.

Figure 3. Data file when manual relabelling fails.

Tips on Getting Clean DataAs mentioned at the beginning of this section, the quality of captured data is largely dependent upon three things: cameras, subject speed, and marker occlusion. We will address each of these areas separately respectively. 

Camera Numbers and PlacementThe most important factor in obtaining clean data is the motion capture cameras. How many cameras you have, and their placement with respect to the motion being observed, will determine the quality of your data. The simplest method to increase data quality is to buy more cameras. However, given the cost of motion systems, such a strategy is not always feasible. Thus, if you have limited cameras, how you place the cameras in relation to the motion being captured is vital.In order for a marker to be visualised, it must be viewable by at least two cameras. However, this minimum requirement is often insufficient. In such an environment, markers will frequently disappear due to: occlusion, quick movements, or system errors. Thus, it is advisable to have markers viewable by three or more cameras if possible. In the Macquarie setup, which began with facial analysis, four cameras were placed in a tight array, spaced 2m from the subject. This setup produced high-quality data. You may consider using a similar arrangement if examining a localised body part.

Processor Settings and Subject SpeedVicon is designed to capture a whole range of movement, from the very slow (turtle) to the very quick (golf swing). Because of this, Nexus has a variety of settings which allow trade-offs between data quality and subject speed. These settings can be found under the Resources pane, System tab, clicking on the Local Vicon System tree node, in the properties subpane. Specifically: marker movement speed, label model rigidity, and quality/speed. If your model is slow moving, with relatively fixed markers, you will want to set these values low. A description of these and settings can be found in Managing Local Vicon System of the Vicon system manual.

Subject Tips

Occlusion is the single biggest problem of marker-based motion capture systems. It is because of this issue that large, multi-camera setups are needed. When considering the size of typical markers (3mm, 1cm), the smallest crease in clothing, fold of skin, shape of the body (e.g., lip curl), or loose hair, is enough to cause occlusion, and therefore missing data. There however a number of simple steps you can take to minimise these and other issues.

1. If capturing the body, ask subjects to wear tight fitting, minimal clothes (e.g., micra). Markers also stick better to skin than to clothing. Also, the model will be far more rigid (clothing is loose and dynamic, but skin is fixed to the body).

2. Ask subjects to tie their hair back.

3. If capturing facial data, ask female subjects to minimise the amount of make-up, and ask male subjects to shave. These issues effect how well markers will stick to the face. If a marker falls off for any reason, you will need to recalibrate the subject.

4. Consider the shape of your subjects body, and vary marker placement accordingly. For example, if subject's lower lip has a pronounced curl, place the marker a lower to avoid occlusion by the lip. This will often happen if the subject tilts their head down momentarily.

5. Ask subjects to remove all jewelry and shiny objects.

6. Ask subjects to minimise non-related movements during the capture (e.g., licking lips, scrunching up their nose). This will reduce the frequency of movement spikes in the data.

Reference: http://psy.mq.edu.au/me2/mocap_v1/clean_gaps.html