Hello, My name is Jiwan Kim and I’m here to present FingerText that exploring and optimizing
performances for wearable, mobile and one-handed typing.
Have you also done this kind of texting while walking before?
yeah probably.
We know that many people are using smartphones while walking.
Likewise, typing on wearable devices while walking also can happen, but it is challenging.
Many prior works tried to solve this problem by single-handed text entry.
Thanks to the dexterity of fingers and wrists, these were effective,
while there was a lack of considerations on mobility context.
To address this issue, we built a touch-sensitive fingernail system.
According to the prior work,
this system enables the eyes-free, one-handed, fine-grained input in multiple nail regions.
So, this is our system, FingerText.
Using FingerText, you can easily type the text even while walking.
let’s have a look closer.
To build a system, we had two considerations.
One is, which fingernail regions can we use for a key?
Following the previous work on design space of fingernail,
we set these two key configurations.
Ten key layouts utilize the side and tip regions of the fingernail,
and 5 key simplify this as one key.
Then second, we need to assign 26 letters to each key.
In this case, we might use qwerty or alphabetic layout.
However, this method is in a different form with keyboard or smartphone typing,
so we frame a hypothesis that there will be no learning benefit.
Rather, to answer this question, we conducted the metric study.
The goal was to gather the three metrics data to feed
on keyboard layout optimization process.
We measured the speed and accuracy for each pair of keys,
and the comfort for each key on a 5 Likert scale.
As well, we did this for walking and sitting pose to figure out the effect of mobility condition.
Interestingly, in terms of pose, we couldn't find significant differences in speed
between sitting and walking condition.
In terms of speed and accuracy, the results are obvious.
Double-tap and short distance were quick and accurate,
but little and long-distance were slow and error-prone.
For comfort, user felt comfortable for using thumb, index, and middle finger,
while not for using little finger.
For confusability.
If we allocate the high frequent letters to the most comfortable region,
It can cause such a confusion that couldn’t get
what the user wanted to type.
To deal with this issue, using confusability matrix,
we can consider how many possible words will be generated for each layout.
With these four metrics, we conducted optimization using the NSGA-II algorithm.
We get the min/max of each metric to normalize it
and then we conducted optimization to get the Pareto optimal keyboard layouts.
From these optimums,
we selected the layout with an emphasis on speed and comfort.
So, this is our optimization result.
As you can see, less frequent letters like Q, J, Z are located on little finger,
and vowels are located on the comfortable regions
like thumb, index and middle finger.
Then, to verify whether the optimization was effective or not,
we conducted comparison experiment.
For this comparison, we conducted word repetition study with Qwerty layout baseline.
In the study, participants made a word input 7 times consecutively for 20 words while walking.
This approach boost participants to make an expert level of input
within a short period of time.
In the results, we found meaningful performance improvement.
For each 5 and 10 key layout, optimized layout improved in both speed and accuracy
over the qwerty layout.
Compare to other text entry studies on wearables,
FingerText showed a comparable and promising result as a viable design for mobility context.
This is final shot of our prototype.
It is saying…
Thank you.