Hello. I'm Yizheng Gu from Tsinghua University,
China
Today, I would like to introduce my research
The title is QwertyRing
text entry on physical surface using a ring
As the figure shows
the device is a ring with an inertial measurement unit
The "surface" here
is any physical surface in our daily life
For examples, the top of a desk
or a table
The QwertyRing supports software
qwerty keyboard on physical surface
The user wears the ring on the middle phalanx of the index finger
He can type
on any desk-like surface as if there is a qwerty keyboard
The user
does not see a keyboard on the physical surface
Instead he
imagines that there is a qwerty keyboard within the reachable
area of the index finger
The user touches to input characters
Long press to enter candidate's selection, swiping left
and right to select the desired word
and touch up to pick it
The user delayed a world by a swipe left gesture
QwertyRing enable wearable text entry in many scenarios
For example, some emerging smart device such as AR
VR headset
and smart TV is do not support efficient text entry yet
QwertyRing enables touch-based test entry on these devices
which is efficient and convenient
In a reminder of the presentation
I would like to introduce the key problems to implement
the QwertyRing
In literature, the feasibility of typing
on physical surface using a ring is unexplored
We raised
three research question as follows
First
how to detect
touch events
on physical surface using a ring
The second question is
what is the use behavior when using QwertyRing
And how to design
the text entry decoder
The third question is, how fast
can user type with the QwertyRing
We conducted three user studies, each contribution
contributing to answer one of the three research questions
For the first research question
how to detect touch events on physical surface
We use machine learning to solve the problem
We conduct the first study to collect user data of touch interaction, including
uh tapping
long press
and swiping
The figure shows the IMU data
Here is the touchdown moment
and There is a touchup moment
We find some features that are helpful to the recognition of touch events
For example
the vertical component of the acceleration is a good feature to detect, touch up
Based on the data analysis
we designs a SVM model to detect touch down and touch up events
The accuracies were more than 99% based on the sensing of touchdown and touchup
We can recognize other touch events
including long press
swiping
by threshold methods
For the second research question
what is an optimal designs for the text entry decoder
We conducted, the second study to collect users typing data
We do not have a decoding
algorithm at this stage
The system allows
the system always displays the correct character
no matter how the participant typed
we collected users' typing pattern
There was some interesting result
For example
the figure shows the typing locations of the 26 letters
The keyboard in most users' minds was fan-shaped
but not rectangle
Finally, we designed a text entry decoder
according to the finding in this study, the result shows that
The decoder supports test entry with word suggestion
For the third research question
how fast can a uses type with the QwertyRing
We evaluate the performance of QwertyRing through a 5-day user study, participant achieved
13.7 word
per minute in the first
40 minutes
And reached
20.6 word per minute after a 5-day training
The results shows that QwertyRing enable efficient touch-based text entry on physical surface
Finally, I would like to make some
closing remarks for today's presentation
The QwertyRing supposed two interaction abilities on physical surfaces
The first ability is
detecting touch events on physical surface using only a ring
The second one is to enable
touch-based text entry or physical surface
The typing speed is about 20 word per minute
so Thank you for
listening
I would like to start our Q&A section.