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Introduction to the course

n this video, Jonna and Alexander discuss some core questions that are at the centre of attention in this course.
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Hi and welcome to this RITMO course on motion capture. My name is Alexander Jensenius, and I’m a professor of Music Technology here at the University of Oslo. And my name is Jonna Vuoskoski. And I’m an associate professor in Music Cognition, also here at the University of Oslo. And the topic of this course that we are going to teach you now is on motion capture. But, what is actually motion capture? Well, that’s actually a very good question, because it’s not so easy to answer. Because some people think about motion capture as these suits that you put on, with markers, et cetera.
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But you may also think about motion capture as, more generally, just the way of being able to capture some kind of human body motion. And in general, I would say that you can kind of separate between observational-based motion capture, where you can watch someone move. And you can kind of just by looking at them, you can try to analyse what’s going on. You can write it down with pen and paper, et cetera. So that’s one type of motion capture. Another one is to use different types of technologies to be able to capture the motion. And that’s the focus of the course that we’re going to have here now, where we’re going to look at two different types of motion capture.
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Camera-based on one side, where you use cameras of different types to be able to capture the motion. And sensor-based, where you use different types of sensors that you put on the body, for example, to measure this. But then, Jonna, you have also been working with motion capture quite a bit in your research. And then, why do we actually want to work with motion capture in the first place in music research? And also, outside of music research? Yeah, well, that’s a very good question. First of all, we know that the body is actually very important for different kinds of cognitive processes, also from the perspective of music cognition.
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If we want to really understand how we make sense of music, how we react to music, we also have to look at the body. And human body movement is a super complex phenomenon. And if we want to really scientifically study it, we have to have a method of objectively, reliably measuring it. And motion capture is one way of doing it, whether that’s some sort of an observational method or like a technology-based method. And going more into these technology based measures or methods also, the human eye is not that accurate. If we want to look at very sort of fine details or very fine movements, we need to have something that goes beyond the capabilities of the human eye.
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So motion capture methods are able to capture these very sort of minute, micro movements even. When we are, for example, trying to stand still while listening to music. And also, we are able to extract different kinds of features from motion. So like the velocity or acceleration or the size of movements, when we are engaging in different kinds of music-related activities, like dancing or playing a musical instrument. And then we can, in experimental settings, we can perhaps vary the conditions. And then we can see how these different features change between those different conditions.
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So, for example, if you are interested in looking at dance movements, you could see how the acceleration and size of dance movements varies depending on the genre of music that you are dancing to. And finally, also, you can use motion capture data to illustrate a movement or make illustrations that you can use in publications or even animations of movements, the sort of stick figure animations or other types of animations. And these you can also use them in other types of experiments, like perceptual experiments, where you ask people to watch these videos, make evaluations. And then you can, again, try to find these associations between these objectively extracted movement features and people’s evaluations, for example.
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But are there any challenges associated with motion capture? There are just challenges everywhere, really, when you work with motion capture. And that’s something we have been exploring quite a lot at RITMO because we have been looking at using different types of motion capture systems. And many of these challenges we’re going to talk more about in the later chapters in this course between. But very briefly, you can say that, for example, the location that you’re using is very important for what type of motion capture you can use. For example, in a setting like we have here, it’s a very different kind of setup than when you are standing in our lab. And we have a much more controlled environment.
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Of course, it also depends on how many people that you are going to capture. Are you looking at one person? Or are you looking at multiple people? Are you looking at the musician having an instrument, for example? That’s something else then. If you want to try to capture someone just kind of moving to this to turn to music, for example. And what we see is, also, that we have so many different types of systems. And also how they integrate it is another challenge. Often we want to use motion capture together with, for example, EMG, which measures the muscle tension. Or ECG for measuring the heart rate. Of course, since we are music researchers, we also want to record audio.
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Possibly we want to have video as well, perhaps, in addition to kind of some sensors. And then the integration of all these things is tricky. Of course, we generate a lot of data. And how do we store this data in a meaningful way? And synchronise all of these et cetera. So there are tonnes of challenges really. And many of these, we will also discuss later in the course to really give you also some tools and give you some of our experience, when it comes to try to solving these issues. So that’s kind of part of all the challenges of the data collection itself. But then, after we have done the data collection, we are moving them towards the analysis.
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Of course, you need to clean up the data, et cetera And then we can move on to the analysis. And we’ll talk more about more advanced analysis things later on. But just very briefly, what type of analysis can you do really with motion capture data? Well, broadly speaking, you can do both different qualitative as well as quantitative analysis with motion capture. So I suppose qualitative methods would evolve types of interpretation. Like if you want to, for example, categorise different kinds of movements or find what the functional purpose of those movements is, for example. And, of course, then quantitative methods involve statistics, measurement statistics, sometimes also machine-learning techniques.
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And you can also draw kind of parallels between these quantitative and qualitative approaches, as well as the more descriptive types of analysis and functional types of analysis. So descriptive types of analysis could, for example, relate to the kinematics of the movement, like acceleration or velocity. Or spatial properties, like the size of movement, or location, or where the movement is happening in space or time, time-based aspects like the frequency, or periodicity, or speed, or things like that of those movements. And then, the functional analysis is looking more at what is the purpose of the movement.
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Or, for example, if you’re playing a musical instrument, is it specifically a sound-producing action or just a sound or a sound-accompanying action or some other type of communicative action, for example. And so indeed, these descriptive methods tend to be quantitative, so extracting these quantitative features and analysing them, which is statistics. Whereas making these interpretations about the functionality is a bit more qualitative. And you might use illustrations as help in that, as well. And we’ll get back to some of these later on in the course to give you some examples, but also some specific tools to be able to do this type of analysis. So I think that’s enough for the introduction. You will be able to learn more throughout.
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We have split this course up into different sections, so we will go through the different technologies. First of all, of course, we need to start with a little bit background about the body and how the body is actually working, if that makes sense. And we’ll have a combination of videos and text material, so that you will be able to learn from both of these modalities. And of course also, there are some quizzes and tests that you can take along the way also, to check that you have learned what is necessary to learn. So again, welcome to this RITMO course on motion capture. And enjoy the rest of this.

Welcome to the RITMO Course on Motion Capture!

In this video, Jonna and Alexander discuss some core questions that are at the centre of attention in this course:

  • What is motion capture?
  • Why is it interesting to use motion capture?
  • What are some challenges with motion capture?
  • How do you analyze the data?

Of course, they can only scratch the surface in this short video, but we will soon get into more details about all of these questions.

We shot this video at the Library of the University of Oslo in parallel to a complex motion capture session. Here a string quartet played a concert in a so-called “ecological” environment. Working in real-world settings is generally more challenging than capturing in a laboratory context. We will discuss the differences between doing motion capture in a lab versus a real-world environment later.

But for now, welcome. Let’s get started!

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Motion Capture: The Art of Studying Human Activity

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