Creating skeletons and rigid bodies

So in– some systems have pre-defined skeletons that you can use, but in this programme that we’re using now, you can actually create bones yourself, between the markers, which makes it easier to see what you’re actually looking at. And here is a recording. and where the markers are attached by bones.

Yes. Probably it’s also useful to explain, if you choose to capture people using a skeleton, this is kind of a model that the software uses that helps it keep track of the different markers and where they are. So there are different skeletons that you can choose from. And then you have to put the markers in that specific location, according to the way the skeleton is defined. And this should help. It should help with tracking if you put the markers in the right location. Because the system knows, if the elbow disappears, it knows to look again in this location, when another marker reappears. And hopefully then it joins those disjointed trajectories together and saves you some time with the labelling.

And the other thing that you can do is define an object as a rigid object. So for me, I might do this with a musical instrument. And you require a few markers to do this. And this would be in the case where the markers do not change their position relative to each other. So it has to be something that’s solid. So if you imagine a violin. You can put a few markers on a violin and it’s not that you’re going to break the violin in half, so the markers position relative to each other will stay the same. This is different from a person, where the person moves in all kinds of ways.

So the markers of the elbow are sometimes going to be closer to the head, and sometimes further away. So when you’re happy with your labelling and gap-filling, then you need to export your data. And you do that into a text file. And the format of that file, it kind of depends on the software that you will use later for analysing your data.

And so you have many choices when you export the data, depending on what you want to take and what you want to do with it. So you might choose to just export marker positions, or you maybe you’ve defined bones between the markers, and so you want to also export information about the bones. So the systems can use algorithms to estimate the centre of a bone or something. With rigid objects, you can also export information about the centre of the object in the way it’s rotated in space.

So once you’ve exported your data as a text file and you’ve imported it into whatever software you like to analyse things in, usually the next step would be to have a look at your data and see if you need to do any smoothing. So we’ve already talked a little bit about the type of noise that can occur in motion capture recordings. This plot shows in black the original recording of a particular marker, and you can see that there’s a small amounts of jitter happening. So this is really zoomed in on a very small part of the marker trajectory. This is 200 times– 200 times 8, so it’s less than two seconds that we’re seeing here.

And it might be useful for you to get rid of this jitter that you see in the black line. So this is what I’ve done. You can use some sort of mathematical smoothing algorithm. There are various choices to get rid of this jitter. So the red line here is a smoothed version of that marker trajectory. And it follows the same contours and maintains the important information, but it just gets rid of all the small amounts of jitter. And I would say this is especially important if you’re planning to look at derivatives of position, like velocity or acceleration, because whatever jitter there is there, it’s going to multiply itself every time you take a derivative.

So it’s important, if you don’t want to look at that noise, it’s important to get rid of that.

That good?