- I'm a undergrad student from Tsinghua University.
So unfortunately our first officer
could not come here due to some problem with his visa.
So he brought as one of his students here,
to present with me our work.
So I'm happy to came here to share our work,
Investigating the Effects of Post-Selection Feedback
for Acquiring Ultra-Small Targets on Touchscreens.
So touch is still a very important
measure for us to interact with our
mobile devices and/or smartphones.
However, the fat finger problem
still limits our touch
our touch performance.
Because of the inclusion of the contact
area when we are touching our fingers,
when we're touching the targets with our fingers.
Users can now see the exact contact point
on the touchscreen so that they will rely
on their mental model to perform the touching.
And this problem gets even worse
when the screen size becomes smaller
and the target size is really small.
For example, imagine you're targeting at
a hyperlink on a website like the figure show on the left.
So you'll probably be mad if you try several times.
And so post-selection feedback
tackled this problem by adding a ripple effect
after the users linked up his finger
after finishing the touching and therefore,
the user can receive a contact point exact
actual contact point of where he just touched
and can make adjustments afterwards.
So noted that the purpose of the post-selection
feedback is not to improve accuracy of the
touch at this time
but actually we tried to
give the users more information
about the touch position so that
he can make a more accurate touch in the next time.
So we first review really works
that are examining of the touch performances.
So this paper shows some results
about users touch accuracy, for the target
of different sizes on different devices.
And we have found the result that the touch
accuracy differs, varies a lot
around the different devices.
And is relatively low when the target gets really smaller.
For example, Cockburn et al. found an average of
13% error rate for 5mm targets,
while Bi et al. reported
66% error rate for 2.4 targets.
So in this research we focused on composited
sensitive touchscreens and the targets that are two
to four millimeters.
So the inaccuracy of the touch
come from two components.
The first one is the system offset
which is the main location of the end points.
And the second component is the standard deviation
which reflects the stability of our touch performance.
So in terms of the system offset Holz and Baudisch et al.
Found that when touching the target,
finger rolling and pinching would affect the system offset.
And it further reported that when performing touch,
people align their finger, the outlines
wth the target in top down manner.
And in terms of the standard deviation,
resent work on touch modeling show that,
the standard deviation of touch derived
from both the end position of the touch
performance itself and the split accuracy trade off.
So Bi et al's work reported that when considering
these two factors together, we can better modeling
the variability of the touch endpoints.
There has also been several techniques
to improve the touch accuracy.
So one approach is to enlarge the targets like the tap tap.
Another way is to provide visual feedback
for targeting like provide an offset view
when you point on the targets.
And then (mumbles) has studied the post-selection feedback
in a tablet scenario and found that the feedback
can improve touch accuracy significantly.
Another work deployed a tech centric game
To collect touch data and they also provide
points showing where the user just touched
and found that this kind of post-selection
feedback could improve touch performance significantly.
We recognized two major limitations
from previous research, first no research has been
conducted examining the effect of this post-selection
feedback on the really ultra small targets.
It may not be surprising or the feedback will remain
or even bigger but it's still necessary to give a
concrete number to show how this
effects are on the small targets.
And second but more importantly,
previous research does not provide
detailed analysis about the spatial
distribution of the end points
so it was difficult to draw further insight
about how this post-selection feedback
could affect our touch behavior.
So in this work, we will look more on the endpoints
and also the special distribution of them.
So these two limitations together motivates our work.
And next Shane will introduce our study
and draw our conclusions, too.
- Thank you.
Now in this work, we conducted three
experiments to investigate the effects
of post-selection feedback for
different tasks and different scenarios.
Now I'll report the experiment as well as the main results.
Now in the first experiment we used a crosshair target,
as we were interested in users basic point ability.
We required participants to repeated touch
the target and after each pointing they had to
lift their fingers up to reset a position.
And we used a smartphone as the platform.
According to our own test, the coordinate
of touch-end point was reported
as the center of finger contact area
and there was no systematic level of set manipulation.
The only independence factor is feedback condition.
We compared no PS feedback that is
short for post-selection feedback
and PS feedback provided conditions.
Our hypothesis is that post-selection feedback
could improve touch position but slow
down equal speed at the same time.
This illustrates the crosshair
target we used in this experiment.
Our results were significant.
Post-selection feedback results in
9.7% more selection time than no PS feedback.
Meanwhile, acquiring targets with PS feedback
were significantly much more accurate than with no PS.
This table shows the systematic offset
and standard deviation of each condition.
You can see that both of them were
largely reduced with PS feedback.
For example, the post-selection feedback reduced
systematic offset by over 74% on average.
And to examine if there was any asymmetric
skill transfer effect, we tested the effect
of presentation order of moment time as
systematic offset for both PS feedback and no PS feedback.
Results showed no significant effects as such
the hypothesis of asymmetric skill
transfer effect was not supported.
According to our own observations,
touch skill learned with post-selection
feedback was rather (mumbles).
And had to be reinforced continuously.
During our post experiment review,
our participants reported that post-selection feedback
could improve their understanding of their finger
point making and some were very surprised to see
their systematic offset was that large
and no post-selection condition.
And besides, all participants reported
a similar strategy of utilizing
post-selection feedback that if an offset occurred
they would perform adjustments to what opposite
direction to compensate the offset.
Now the second experiment we designed
two more factors, the target size and movement distance.
We required the users to perform the typical
Fitts's Law task for each condition participants performed
52 trials with the first four trials ignored as practice.
And this illustrates the reciprocal
task we used in this experiment.
Now this figure shows the error rate across each condition.
Overall PS feedback yielded a significant lower
error rate than no PS, with an average error
rate being about 10% and 44% respectively.
Meanwhile, the effect of target size was also significant.
The average error rate for increasing size ranged from 41%
to 13% that is increasing size to
reduce error rate that's, of course.
And here shows the systematic
offset across distance and size.
Significant effects of feedback was found
on offsets in both x and y dimensions.
Systematic offsets got smaller as distance
increased and no significant effect of size was
found on offsets in either x or y dimensions.
And there was significant feedback distance
and size on standard deviations in x and y dimensions.
Post-selection feedback reduced variations by 32%
on average increasing target size and increasing
distance with both increase and point deviations.
And we fitted data to the effective
width model of Fitts's Law.
We got an R square value about 0.94
for both PS and no PS conditions
suggesting at this date of this model.
In general, providing a PS feedback
requires users to spend more time in pointing.
In a third experiment, we tested effect of PS feedback
in the more realistic scenario by asking users to
perform pointing tasks on a real smart watch.
In addition to its factors, we have
tested in the previous experiment.
We also tested the effect of postures
that is sitting and standing.
Now in this experiment, two circles
were displayed on a white screen.
A reset circle and a target circle.
The reset circle was always four millimeters wide
and for each trial the participants first
touched the reset circle and then the target circle.
If the participants missed the target an audio
feedback will be played to notify an error
and then the participant will have to repeat
the acquisition until succession.
After that a new pair of targets would
appear on the screen for the next trial.
The system will record the total time
and repititions to each trial.
Now the mean target acquisition time was about
900 milliseconds for PS feedback
and about 1.5 seconds for no PS.
It was not surprising to find that PS feedback
improved selection accuracy and thus
resulted in less time of completion.
And we also defined the error rate,
just as this figure illustrate.
The error rate is defined according to the
number of repetitions required to
successfully acquire the target.
For example, if a user hits the target
at the first trial, the error rate will be zero.
And if at a second time the error
rate will be 50% and so on.
And we found that feedback, distance, size
and scenario all yielded significant
differences on error rate.
Now as a summary, we know that providing post-selection
feedback can significantly improve touch accuracy.
But both systematic offset and standard
deviation can be largely reduced.
You can see these numbers here
and user subjective feedback also supported the results.
Now we performed some deeper analysis
about users touch behavior with
and without the post-selection feedback.
To do this we first conceptualized
some adjustment behavior attached.
Suppose there's a target on the screen
and a user touch it with a touch point
landing from a distance dx from the target center.
For the next touch the user noticed the offset
and performed some adjustments.
Now this caused a second touch point to land
from a distance dx1 from the target center.
So we can calculate the adjustment as (mumbles) x1.
Now based on this representation
we formed a hypothesis that is,
a post-selection feedback should affect the
correlation between successive touch points.
This is because that with a post-selection feedback
users should be more likely to adjust their
touch behavior to compensate the offset of the
previous touch in the next selection.
Therefore, we test the correlation between
touch points for PS and no PS conditions.
The result is interesting but also surprising.
We found no correlation for PS condition
but significant correlation for no PS condition.
In other words, if a post-selection feedback is present,
the touch points are almost independent to each other.
However, if we remove the feedback, the current touch
would, have significantly correlated with the previous one.
And next we look at the touch of adjustment.
In this figure, the horizon axis represent the
offset of the previous touch and vertical axis
represent an adjustment of the next touch.
First we regard data points falling into the second
and fourth quadrants as positive adjustment.
Which means the adjustment compensates
the offset of the previous touch.
So we can use the ratio of positive adjustment
that is PAR to compare the two feedback conditions.
This figure shows the ratio of positive adjustment across
or dx values for both PS and no PS conditions.
Obviously, the greater the aptitude effect on the previous
touch the easier for participants would
notice the offset and make a compensation
or positive adjustment for next.
This holds for both conditions, however positive
adjustment ratio converge much faster
in PS condition than in no PS condition.
This shows users perform more positive adjustments
with a powerful selection feedback
showing the actual touch points.
We also applied linear for the data points.
X intercept denotes the average dx where the positive
adjustment and negative adjustment are equal.
And the slope of the regression line
characterized the degree to which an
adjustment is made with the amplitude of the offset.
A slope of minus one represents ideal adjustment
behavior whose average adjustment exactly
compensates the offset of the previous touch.
This table summarize the regression results
for all conditions in experiment two
which showed post-selection feedback can
help users to make positive adjustment
for all size and distant conditions.
With PS feedback we have seen mean results
that is the average slope was much closer
to minus one and the average x intercept
was much smaller and the positive
adjustment ratio was higher.
To conclude in this paper we finally studied
post-selection feedback requiring tiny
targets on touch screen and we found that with
PS feedback a better finger point
mapping is formed and more effective
intentional adjustment can be made.
Okay that's all for the talk
and thank you for listening.
Welcome for questions.
(audience applause)
- [Voiceover] Any question for the end of the day?
I know it's been a long day.
Any questions?
(audience question)
- So we will guess that there exist a migrating
effect for the no feedback situation so for example
if you touch for one time without feedback
and the next time will probably be the same.
However, the users still try to
think of try to improve their touch
even given no feedback right.
So that's why we find (mumbles)
of effects of the touch points
so that makes the correlation actually higher
without feedback than with feedback.
Okay.
Hi.
(audience question)
Which experiment, you mean?
(audience question)
Uh, huh.
Oh, just one after the other, so just three short touches.
(audience question)
Actually we did back and forth
touch target selection
in like the pilot test so we
also find significant increments
with the feedback so we would
anticipate that it will help also with the gestures.
- [Voiceover] Thank you.
- Thank you.
- [Voiceover] Okay, thank you very much.
- Have a great day everyone.
(audience applause)