Hello! I’m Corentin Bernard from Aix-Marseille University, and I will present the paper :
Eyes-off your fingers: gradual surface haptic feedback improves
eyes-free touchscreen interaction. In this presentation, I will show you
how we can use haptics to reduce the dependence on visual information when using complex interfaces.
On mechanical interfaces, basic operations like setting the music volume or controlling the air
conditioning with a slider, are very familiar and can be performed almost without looking at it.
In new car cockpits, however, most of the mechanical buttons have been removed and replaced
by touchscreens. The same simple tasks require much more visual attention because you don’t have
any tactile feedback. This visual distraction can lead to dangerous situations while driving.
To address this problem, we propose here a new interaction with a haptic touchpad.
It is designed for the precise use case of setting a parameter.
As you can see on the video, the user can adjust the setting by scrolling up and down with his
finger on the pad, as with a mouse scroll wheel. The setting is here an abstract value that
varies from 0 to 100, which can represent any interface parameter from 0 to 100 percent.
The pad is enhanced with haptic feedback. It means that the user
can feel a synthetic texture on the surface, and the key point of this
work is that the synthetic texture gradually evolves with respect to the setting value.
For example, when the setting value increases, the spatial frequency of
the texture also increases, and we can feel a texture that becomes finer to the touch.
Here is presented the setup. It mimics the touchpads that you
can find near the driver armrest in some new cars.
As haptic technology, we used one called ultrasonic friction modulation. It works
by using ultrasonic vibrations to create a thin film of air between the finger and the plate.
By modulating the amplitude of the vibration, we can control the friction under the fingertip.
That way, we can render the illusion of felling bumps and textures.
Here is how we constructed the haptic feedback.
The setting value is linked to the spatial frequency of the synthetic texture.
The spatial frequency can be seen as the number of ridges per millimeter.
The exponential relationship between the setting value and the spatial frequency is important.
It has been designed based on our perception to ensure that
the frequency changes are perceived equally over the entire setting range.
With this feedback, we can adjust a setting without vision. But to master the interaction,
we first need to learn the association between the value and the texture that we feel.
So, we conducted a user study with 3 goals : First, we wanted to prove the validity of the
interaction, that indeed users can operate the device only with touch.
We wanted also to know how accurately and how fast the subjects could operate the device.
And finally, we compared different training strategies
in order to investigate how we can optimize the learning process.
The task is presented here. We asked the subject to reach a desired
value as quickly and as precisely as possible. In the protocol, there were 2 target values,
either 60 or 80 percent and the setting was set randomly among 5 initial values.
The subjects had to perform first a training session and then an evaluation session.
During the learning phase, after each trial, the subject could see value that they had selected.
This feedback enables them to understand how the interaction works and how to improve.
So that was the basic training session, but we experimented other learning strategies
with the assistance of other modalities.
There was a visual condition with a slider on the screen, directly linked to the setting value.
There was also an audio condition, with a sound that informs the user on the setting value.
We added two other conditions, in which the additional feedback,
visual or audio, is progressively removed during the learning session.
Each subject performed one of the 5 training sessions,
and all the subjects performed the same evaluation session.
So, here is an example for the visual condition, with the slider…
And you can see the selected value at the end.
For the audio condition, we mapped the setting value to the pitch of a sound that was modulated
in amplitude like the texture. It enabled the two stimuli to be perceived as congruent.
Here is the audio feedback:
For the analysis, we measured in each trial 3 metrics,
the selected value, the error and the completion time.
Here are the global results, by taking into account the trials of
all the subjects during the evaluation, regardless of their training condition.
We can see that the subjects performed well at choosing either one of the two target values,
with 8 percent of error on average If we consider the completion time,
we can see that is clearly related to the distance subjects had to scroll, with 4 seconds on average
We can compare the 5 training strategies by looking at the learning curves. They show how
the error evolves during the experiment. We can see that at the beginning,
the errors are pretty high, especially for the conditions without the visual slider,
That's because the task is not familiar. But the fact that the curves decrease
shows that the subjects are learning and improving, until they reach a plateau.
Even if each strategy presents its own learning trend,
at the end all groups of subjects performed quite equally during the evaluation.
Moreover, we can see that there are big differences between subjects, with some subjects
that performed the tasks really well and quickly, whereas the task seems more difficult for others.
So I have presented a new interaction with a haptic device. We showed that subjects were
able to adjust a setting on a 0-100 percent scale without vision, in 4 seconds and with 8 percent of
error in average. This level of performance is acceptable for applications that do not require
a high precision, such as opening a car windows or setting the ventilation.
The experiment demonstrated that the learning process is necessary,
and that all training strategies led to similar results.
It means that the learning method can be adapted to the context and to the users’ preferences.
Future works will investigate long-term retention of the feedback and how it can be applied to
different use cases in real-life scenarios. Thank you for your attention.