1. The record is for continuously standing motionless.
2. You must stand: sitting is not allowed.
3. No facial movements are allowed other then the involuntary blinking of the eye.
4. Deep breathing is permitted provided it does not involve observable movement notably greater than that in normal breathing.
5. No rest breaks are allowed at any point during the event.
6. The venue for such an event should be such that the general public can view.

But from my point of view, being interested in micromovements, I would be very curious to see how still these record holders actually were.

At the ArtsIT conference next month I will present the results of a study on standstill that I have conducted together with Kari Anne Bjerkestrand. I have given a sneak peek of the data earlier, and below is another figure with plots of motion capture data from the study. The plots show data of a marker placed on the neck, from six different 10-minute long standstill recordings of myself and Kari Anne. It is only the vertical position of the marker that is plotted.

From the plots we can see that the running marker displacement was at the scale of only a few millimeters, with a maximum displacement of less than 10mm. It can be argued that this is not much, but it certainly is not absolutely still.

One thing is the quantitative data, another is our subjective experience of standing still. Even though we tried our best to stand physically still, we could easily notice how we were swaying back and forth, doing postural adjustments, etc. Observing the video recordings of ourselves afterwards, it is also possible to see these micromovements through visual inspection only.

Based on these findings, I would be very curious to see how still a person can actually stand, not only measured in hours and minutes, but also in millimeters. So to any aspiring world record breakers: please come and do your next attempt in our lab!

I could probably have recorded the accelerometer data in my phone, but I wanted to try an even more low-tech solution: an audio recorder.

While cleaning up some old electronics boxes the other day I found an old Creative ZEN Nano MP3 player. I had totally forgotten about the thing, and I cannot even remember ever using it. But when I found it I remembered that it actually has a built-in microphone and audio recording functionality. The recording quality is horrible, but that doesn’t really matter for what I want to use it for. The good thing is that it can record for hours on the 1GB built-in memory, using some odd compressed audio format (DVI ADPCM).

Since I am mainly interested in recording motion, I decided to put it in my sock and see if that would be a good solution for recording the motion of my foot. I imagined that the sound of my footsteps would be sufficiently loud that they would be easily detected. This is a fairly reduced recording of all my motion, but I was interested in seeing if it was relevant at all.

The result: a 35 MB audio file with 2,5 hours of foot sounds! In case you are interested, here is a 2-minute sample of regular walking. While it is possible to hear a little bit of environmental sounds, the foot steps are very loud and clear.

Now, what can you do with a file like this? To get the file useable for analysis, I started by converting it to a standard AIFF file using Perian in QuickTime 7. After that I loaded it into Matlab using the wonderful MIRToolbox, resampling it to 100 Hz (from 8kHz). It can probably be resampled at an even lower sampling late for this type of data, but I will look more into that later.

The waveform of the 2,5 hour recording looks like this, and reveals some of the structure:

But calculating the smoothed envelope of the curve gives a clearer representation of the motion:

Here we can clearly identify some of the structure of what I (or at least my right foot) was doing for those 2,5 hours. Not bad at all, and definitely relevant for macro-level motion capture.

Based on the findings of a 2 Hz motion peak in the data reported my MacDougall and Moore, I was curious to see if I could find the same in my data. Taking the FFT of the signal gives this overall spectrum:

Clearly, my foot motion shows the strongest peaks at 4 and 5 Hz. I will have to dive into the material a bit more to understand more about these numbers.

The conclusion so far, though, is that this approach may actually be a quite good, cheap and easy method for recording long-term movement data. And with 8kHz sampling rate, this method may also allow for studying micro-movement in more detail. More about that later.

There are, however, two words/terms that I still find very challenging to define properly and to differentiate: movement and motion. In Norwegian we only have one word (bevegelse) for describing movement/motion, which makes everything much simpler. But when writing in English, which word should be used? and what is the difference?

It only adds to the confusion that Wiktionary defines movement as “physical motion between points in space”. And Wikipedia has a page on motion (in physics), while none of the many movement pages are related to body movement.

During the last years I have asked many native English speakers about the difference between motion and movement, but have not received any good explanations yet. Many of them think they are slightly different, although this is usually based on their feeling rather than on a proper explanation of the difference. Some native speakers think the two words are the same and can be used interchangeably.

I have also asked researchers working on various types of movement-oriented disciplines about their use of the words, and they often tend to stick to one or the other. From these discussions I have come to think that people working in biomechanics and physics prefer motion, while people  working in physiotherapy, dance and music prefer movement. That motion is a more scientific term is is also suggested here. From this we could assume that motion is related to measurable displacement of objects, which the term motion capture attest to, while movement refers to the qualities or meaning of the displacement.

The above assumptions are, however, only my assumptions. So I thought it would be interesting to see if I could get some more empirical data on the topic. So I decided to use the powers of Google to quantify the differences. Here are some figures from google and google scholar:

So, clearly, movement seems to be used much more frequently than motion in general language, and also in the scientific literature. However, body movement and body motion are used almost the same amount of times in scientific papers.

But what if we search for the use of the two terms in different fields? Then we get these numbers:

Again, we see that movement is generally used more than motion, even in physics and mechanics. I am quite surprised that music+motion is used so frequently, particularly since movement has a double meaning in music (i.e. parts of a piece).

What to conclude from all of this? I still do not know what the difference between movement and motion is, and the numbers show that movement is used more than motion also in the disciplines that I thought used motion almost exclusively. Still I like the idea that motion is used to describe physical properties, while movement is used to describe the qualities of motion. So I will stick to that for a while myself.

What do you think? Any comments or suggestions are highly welcome!

Working with various types of sensor and motion capture systems, I see the same need to know how much motion there is in the system, independent of the number of variables and dimensions in the system studied. Thus, whether we use a single 1-dimensional MIDI slider or 32 6-dimensional sensors in a motion capture system, we still need to be able to say whether there is any movement in the system, and approximately how much movement there is.

So I have made a small abstraction in Max that sums up all incoming values, divides by the number of values, finds the first derivative and takes the absolute value of this.

I had two optimization questions while working on the patch:

1. Does it matter whether derivation is done before or after summing up the values?
2. Is it more efficient to use Max objects than Jitter objects?

Answers:

1. No, it does not matter.
2. Max objects are ~3 times faster

A screenshot of the efficiency test patch is shown below, and a zip-file of the patches.

/gdif/instrumental/excitation/loudness x
/gdif/instrumental/modulation/pitch x
/gdif/instrumental/modulation/formants x1 x2
/gdif/instrumental/modulation/breathiness x
/gdif/instrumental/selection/phoneticclass x

Here he is using Cadoz’ division of various types of instrumental “gestures”: excitation, modulation and selection, something which would also make sense for describing other types of instrumental actions.

I am looking forward to getting back to working on GDIF again soon, I just need to finish this semester’s teaching + administrative work + moving into our new lab first…

Project leader Rolf-Inge Godøy started with a short presentation of the new project.

Then Marcelo M. Wanderley (McGill, Montreal) held an overview of various types of motion capture solutions, and the pros and cons of each of them. He stressed two main challenges he had had over the years: synchronisation of various types of mo-cap data with audio, video, music notation, etc., and backwards compatibility of hardware, software and data.

Ben Knapp (SARC, Queens, Belfast) followed up with a lecture on various types of biosensing solutions.

Kjell Tore Innervik testing Ben Knapp’s EEG and EOG sensor.

The seminar attracted a very mixed group of researchers, from music, composition and performance to informatics, medicine and psychology.

After lunch, Patrik Almström from Swedish company Qualisys, presented their optical motion capture equipment.

A very interesting feature of the Qualisys system, is that it can use both active and passive markers. It is also great that the cameras can double as regular high speed video cameras.

Kristian Nymoen dressed in the mo-cap suit, and demonstrated the realtime possibilities of the system.

Then we walked across campus to the new project lab.

Mats Høvin demonstrated the robot Anna, which we will train as a musical conductor in the autumn.

Mats also showed a mechanical hand with artificial muscles, something which we will also test for musical purposes in the project.

Rolf Inge, Marcelo, myself and Ben “sightseeing” in Oslo after the seminar.

I held a guest lecture at the speech, music and hearing group at KTH in Stockholm a couple of weeks ago, and got a tour of the lab afterwards. There I got a demonstration of the Optitrack optical motion capture system, which, as compared to other similar systems, is an amazingly cheap solution starting at $4999. Obviously, it has lower accuracy and precision than the larger systems, but then it also costs 1/20 of the price… However, 100 Hz speed and millimeter precision is decent for a USB-based system, and the cameras are really portable (10×5 cm or so each). The best thing was that the KTH people had written a small program sending OSC in realtime, and they showed a demo where the system would control a PD patch on another computer.