Hello, everyone. My name is Tony Wu,
and today I'm presenting ZebraSense
on behalf of my co-authors Shiho Fukuhara,
Nicholas Gillian, Kishore Sundara-Rajan, and Ivan Poupyrev.
ZebraSense is a textile capacitive touch sensor
capable of detecting which surface the touch interaction occurred,
and it is our team's latest innovation in blending interactive
technologies with soft goods to create novel user interfaces.
Historically, a major shortcoming of on-body
capacitive touch sensing is that the sensor is prone
to unwanted signals attributed to the interior proximity of the body.
Some existing solutions place a thick fabric between the body
and the sensing area to create sufficient attenuation
of the body coupling which works well for certain types of apparels,
but not if they need to be really breathable or conformal.
Hence, one original motivation behind ZebraSense was to solve this issue
more elegantly than existing methods.
However, we soon realized that ZebraSense could in fact
be much more impactful than we had originally intended.
Most touch devices today, whether big or small,
are mainly constrained by their form factor
and use case to only allow interaction
with one surface of the device.
I mean, why would you want to have touch controls
on the inside of your washing machine anyway?
But what's different about textiles is that it naturally has affordances
which invite the user to touch both sides of the fabric.
Because they're thin, flexible, and are often worn on the body,
textiles provide the perfect surfaces for ZebraSense to enable
double-sided touch interactions and meaningfully new user experiences.
The sensing structure of ZebraSense can be easily created
with an array of conductors alternatingly placed
into two layers which are separated by a small distance delta z.
For reference simplicity, the top layer is labeled
with even indices and the bottom layer odd indices.
When a capacitive touch is introduced,
nearby sensing elements on both layers respond,
but the ones on the layer farther in the z-direction
will have attenuated signals due to the delta z separation.
We propose a simple z-direction detection formula--
which we label as kz--that evaluates the sum of capacitance differences
between each even and odd sensing pair in the array.
The result is positive if the touch is from the even layer
and negative if odd,
while the amplitude of kz indicates the confidence of direction detection.
Every sensor has its own strengths and weaknesses,
so let's discuss some of the limitations
that ZebraSense currently faces and some example solution strategies.
In general, we're interested in two interaction modes
for ZebraSense: asynchronous and synchronous.
Asynchronous interactions are where only one side of the sensor
is being touched at one time
and z-direction detection is simple by just applying
the kz formula mentioned previously.
Synchronous interactions are where both sides of the sensor
are being touched simultaneously,
in this case, the kz result can become ambiguous for some scenarios.
Let's consider two contrasting examples of synchronous interactions
in the context of an interactive sleeve.
When the sleeve is tight, the relative motion between ZebraSense
and the arm is limited, hence the sensor will see very little signal
from the interior thanks to common baselining techniques.
Any intentional gesture on the exterior surface
will dominate and it is trivial to recognize the z-direction.
On the other end of the spectrum,
when the sleeve is hanging loosely,
the sensor will get pushed into the arm
and the sensor will see strong signals from both surfaces simultaneously.
In this case, direction detection with the basic kz formula
will not suffice and some more robust techniques are required.
For these situations, we have explored two mitigation strategies:
based in soft goods and in software.
On the soft goods side, our experiments showed that by simply
adding a 0.8mm thin fabric between the body and the interior sensing area,
one can completely mitigate wrongly classified directions
with simple kz heuristics.
And on the software side, we've implemented
more robust signal processing and ML techniques
to address these harder synchronous cases.
The details of these techniques are more thoroughly discussed in our paper.
Finally, I'd like to wrap up the presentation with two fun use case demos
that we've built with ZebraSense.
[light music]
In addition to the acknowledgements in the paper,
I'd like to thank the following people for helping make
this presentation possible.
Thanks for your time, and please check out our paper
for more details on ZebraSense.