Hey, everyone.
My name is Carlos Tejada.
I'm from the University of Copenhagen,
and I'm here to talk to you guys about EchoTube.
So as we know, touch and press sensing
are one of the most fundamental ways that we
interact with our devices.
And they can enable a wide variety
of applications ranging from pressing buttons
to collision detection.
However, in order to get the most out
of these interactive modalities, we
need to use techniques that are robust so they
can be deployed in adverse environments
and not prone to environmental noises.
Also work at very large scales and be easily deployable.
So it was with this criteria in mind that we designed EchoTube.
With EchoTube, we sense press interactions
along a rubber tube using waveguided ultrasonic waves.
So EchoTube has a lot of interesting properties.
The first one is that EchoTube is very easily to deploy.
So we can add press sensing into a surface
in a matter of seconds.
And it's made up of inexpensive off-the-shelf components.
EchoTube is also very robust.
So because all of the electronics
are isolated from where the interaction is taking place,
we can deploy this in adverse situations,
because all the electronics are safe.
EchoTube is also modular.
We can extend these tubes by concatenating
other tubes into them and increase the interactive area.
EchoTube is flexible, so we can round these tubes
through varying geometries and enable touch and press sensing
into them.
And EchoTube works at very large ranges, up to 5 meters.
So how did we get here?
There's been a large body of work
that explores touch and press sensing into surfaces.
The ones closer to EchoTube are those
who use acoustic techniques to enable these interactions.
Some of these are passive acoustic techniques
where they basically listen to sounds
made by the interactions.
Some highlights here.
We have scratch input where they record scratching sounds
on surfaces and then assign different actions
to different scratch gestures.
Or another scratch technique which
is acoustic barcodes, where they create ridges in a surface,
essentially creating a barcode.
So when you scratch your fingernail through there,
you're making it a viable sound.
However, the problem with these is
that there's some scaling issues,
and they're not very flexible, not easy to deploy.
Another technique is using--
where they use an array of microphones
to use time of flight in detecting
tap noises along laptops and smartphones.
The problem is that this is very prone to noise,
and it was only proven to work at small scales.
Other techniques are active.
So this means that they inject sounds
into objects or surfaces, and then they
sense how the interactions that we made into them
change these sounds.
Some highlights here.
We have touch and activate, where they inject the frequency
sweep into objects, and then after some training
they're able to identify touch positions.
The problem with this is that it requires successful training
to enable these interactions, and it was only
proven to work at small scales.
And it's also very fragile since we expose the electronics
to where the interaction is taking place.
Similarly, in acoustic instruments,
they route frequency seeps into objects
and enable different interactions
like buttons, sliders, and dials.
But it has similar plot insights and touch
and activate since they've only been
proven to work in small scales and require fabrication
techniques.
So inspired by these limitations,
we designed EchoTube.
EchoTube begins with a rubber tube.
We use inexpensive off-the-shelf tubes due to their durability.
So in order to enable sensing in these tubes,
we use ultrasonic sensors.
These are off-the-shelf electronic components
and are often used to detect obstacles and objects
in the open air.
So these work by emitting a pulse, and this pulse gets
reflected back out of any objects and obstacles
that it found are in its way.
So by getting the delay that takes this pulse
to go from the transducer to the object and back,
we can then calculate how far this object
was from the transducer.
Enclosing these transducers into tubes gives us two main things.
So first, we can string all the sensing just
to the tube itself, so it makes us a bit robust
over environmental noise.
And we can route these tubes down to surfaces
because these transducers require line of sight
to the objects that they're sensing.
The last piece of EchoTube is a microcontroller
that we use to drive and sample the range finder.
Its only main purpose is just to get this data
and send it back to the computer for further processing.
So even though these space finders
provide a digital output, which is
the distance of the first optical they encountered,
we sample the raw signal instead in order
to get a better understanding of what's going on.
So we put all of them together, and it
looks something like this.
We prototyped EchoTube with five different tube
of different sizes.
Some fit snugly into the transducer,
like the one in the picture.
But for others, we had to laser-cut some adapters
like the one in my title slide.
So in order to deploy test sensing,
we just combined all these components
and then deployed the tube in the locations where
we wanted to add sensing.
So once this tube is compressed, we
can sense measurable difference in the signal.
Then we do some simple fence filtering
to the same level in order to strengthen it and normalize it.
And then we identify peaks that surpass a given threshold.
With the location of these peaks,
we can then calculate the delay that must have taken the sound
to go to the object.
And with this delay, we obtained the location
of the interaction.
So with the location, we can then enable core applications
like I presented in the videos.
So how does this work?
We were interested in answering three main questions
with EchoTube.
The first one was how accurate we can sense single press
throughout our tubes.
So what we did was we laid out our tubes
straight on to the floor and then touch
at every 5 centimeter intervals, fully
compressing the tube in order to get better performance.
We then repeated this 10 times per location and then
with all of our five tubes.
And our results look something like this.
So here we see each of the blue dots
is a single press interaction.
The green line is the mean of all the locations.
And we repeated that through all our five tubes.
And there's some highlights that I'd like to share.
So we see first that the smaller tubes are the ones that
yield us the smaller ranges.
So these barely cracked a meter in range of operation,
and so they're more suitable for smaller-scale applications
like size or prototyping variables.
In contrast, the larger tubes provided us
with a larger sensing range, but they were the more noisy ones.
So these tubes then lend themselves
to larger-scale deployments, where
the exact position of the interaction
is not very much of interest.
Finally, the best case scenario we got
was with a 10 millimeter tube, where
we were able to sense up to 3 meters of interaction,
with an error of less than 5 millimeters on average.
Another thing that we wanted to answer
was, can we detect more than one press with these touches,
with this technique.
The problem is that once the signal is blocked
by the interaction, there's not really much reliable sensing
that can happen afterwards.
So to mitigate this, instead of bundling
one tube like I saw before, we bundle two into a transducer.
So this allows us to touch two separate locations,
since one tube remains always open.
So to evaluate this, what we did was we coupled two 4 millimeter
tubes into our transducer and then
touch in positions between 20 and 50 centimeters on each
of the tubes.
And the results we got looked something like this.
Here we see each of the blue triangles
are a single touch combination, and the red x
represents a centroid of all the touch interactions.
So we get the distance from the true touch
to the centroid with the red line, which
averaged around 3 centimeters.
Finally, we wanted to see how quick we can sense touch
interactions using EchoTube.
So because our technique is based on the speed of sound,
how fast we can sense is going to depend
on how long the tube we're using is, because the sounds have
to go all the way from the transducer
to the end of the tube and back in order
to sense the interactions.
So for example, a 50 meter tube allowed us
to sense interactions at a rate of 340 hertz,
while a meter and a half tube only allowed us at 113 hertz.
So we get these high repetition rates
because these transducers operate at a single frequency.
So there's no need to sweep through multiple frequencies
in order to identify the interaction.
Our experiment showed that when the position of the interaction
is not particularly of interest, we
were able to achieve rates higher than 3 kilohertz.
Finally, we developed some applications
to portray EchoTube's utility.
We showed four on the paper, but here, in the interest of time,
I'm going to present only three.
The first application was a bike tracker.
Here we put the tube into a U-shape,
meaning that the bike would interact
in two locations in its path.
So that gave us the presence of the bike,
the direction of the bike, and the speed
that the bike was riding.
We also prototyped a wearable glove,
where we bundled two 4 millimeter
tubes into our fingers, and then we
were able to sense finger bend gestures.
And lastly, we developed a modular life switch controller,
where we extended our interaction rate by appending
tubes into the ends of each.
So our technique has some limitations.
Here I'm going to show you guys two.
The first one is, as we saw in the performance testing
section, there was a trade-off between how big the tube is
and how far we can sense.
So even though the smaller tubes would be the most attractive
to deploy an application, since those were the ones that
yielded the less range, however, the application
that we developed showed there's some utility
in these smaller-range applications.
And another one is that it's related
to the theory of operation.
So our technique uses the speed of sound
in order to identify the locations of the interactions,
and this value can change depending on the environment.
So all of our experiments were carried out in our lab
at a constant of 25 degrees.
But you can imagine if you deploy an EchoTube application
into a hot summer's day, it can reach temperatures up
to 40 degrees, which can introduce up to 7% noise
in to our calculations.
To mitigate this, we could add a temperature and humidity sensor
into our setup and then calculate the speed of sound
on the fly, and we'll not have this problem.
So some final thoughts.
I presented EchoTube, a robust, inexpensive way
to quickly deploy press sensing into rubber tubes
using waveguided ultrasound.
I discussed the algorithm that we
used to identify these touches, and characterized
EchoTube's performance.
Thanks a lot for listening, and I welcome your questions.
[APPLAUSE]
Thank you for the great talk.
This is [INAUDIBLE].
I actually have a series of question,
but they're really, really short.
So have you considered swiping, for example,
as a possible interaction?
Have you considered maybe using different type of frequencies?
Now, why I'm asking is if you could do so-- for example,
you could imagine having both side of the tube
having a different transducer, with perhaps
a different frequency.
And you could actually identify multi-touch, in that sense--
I mean dual-touch.
So we did implement swiping.
So in the very short video in the beginning,
when I showed the music controller here,
there's a volume controller.
So you swipe to increase the volume.
We did not try multiple transducers,
if I remember correctly.
We tried some actual biometric sensors,
but not for multiple sensing, for multi-touch,
but more like press sensing.
We were interested in press sensing,
but we weren't able to accurately correlate
the amplitude of the signal to the force exerted in the tube.
But that's an interesting idea of maybe mixing frequencies
to see what we're able to get.
Hi, I'm Lauren [INAUDIBLE] from LMU in Munich.
My question was, I think in the video
for the multi-touch the distance on the two tubes is different.
And does it work when you have the same distance on the--
is it actually they're the same distance on both tubes?
No, no.
So we only see the difference in distance.
If you were to have the same distance on both tubes,
you will only see one peak, because it's the same obstacle
from the transducer.
So if you notice on the evaluation of multi-touch,
here, the diagonal is empty, so we
weren't able to-- we didn't do the same positions
on both tubes.
Hey.
[INAUDIBLE] from Sussex University.
Have you ever considered filling the pipe with soft material?
Because you have problem touching it,
and it's blocking the sound.
And have you ever considered any soft material to put in there,
and then you will never touch through all-- through the pipe?
So yeah, [INAUDIBLE].
So we didn't insert anything into the tubes.
However, we did try tubes of different rigidity.
So there was one that was fairly rigid,
so we weren't able to physically collapse completely.
But those in our experiment had very variable accuracies,
so we decided not to push forward with those.
I believe if we were to introduce materials
into the tube, it may add some recognition rate,
but I can't say off the top of my head.
Maybe we should follow up online.
Hi, my name is Ron from [INAUDIBLE] University.
Hi.
So when you say that you added a second tube
to detect multiple taps, so did you consider adding one more
sensor on the other side of the tube to detect multiple taps?
No, no, we didn't.
We only tried with a single range finder on one end.
We didn't try coupling to multiples, even
with the same frequency.
I'm not really sure how that would go, really.
So depending on how short the tube is in having--
sometimes you can actually get some noise
from outside of the tube.
Maybe with some filtering you'll be able to get it,
but I'm not really sure how that would go.
Can't say right now.
OK, thank you.
All right, thank you very much.
I believe many of the--
no, the presenting of work is also done by the demo, right?
Yeah.
So you can also check out the demo in the afternoon.
Again, now, I would like to close out my session,
and I'd like to thank our great speakers, right--
Hadley and Theo and Cyan and Carlos.
Let's give them all big hands again.