Hi there, my name is Florian. I'm from the LMU Munich and I'm
going to present to you some work that I conducted together
with my co authors, Daniel, Andrii, Dominik, Sebastian,
Thomas, and Martin on a toe-based input modality. Over
the last few years, we've seen a constant stream of works
exploring food-based interaction techniques for all kinds of
devices and contexts, from desktop PCs to head-mounted
displays. And I have also written a couple of these. If we
now take a close look at these papers, we see that they are
mainly concerned with the external degrees of freedom of
the foot, that is how the joint of the knee together with the
leg and also the ankle joint, move and rotate the foot as a
whole. What we haven't seen that much in contrast, is research on
the internal degrees of freedom of our feet, especially with
regard to our toes. And those few papers that considered the
toes for input mostly use them as simple binary switches for
pressing buttons. However, if you give this a try and play
with your toes, you realize that we probably can do more fine
grained operations than just pushing buttons with our toes.
And this is what we are looking at in this paper, how accurately
and efficiently can we use our toes for for interaction as an
input modality. Here you see an illustration of the main muscles
in our feet. I'm not going into details on the exact anatomy,
but one key thing that we can see here and that you can also
realize when trying to move your toes is that there are muscles
that allow us to flex and to extend our toes, that is moving
them upward, or downward. And second thing to remember is that
all but the big toe are extended and flexed by the same muscles.
This means that while we can independently move our big toe
to some extent, independent movement of the remaining four
toes is not feasible. Based on these two findings from the
anatomy of the human foot, we designed a controlled experiment
to understand the accuracy, efficiency and user experience
of toe based interaction. For this we varied five independent
variables. And the first one was the toes used for interaction
which we varied between the big toe which is also named hallux
in the medical literature, the remaining lateral toes, and all
toes together. As the foot and our toes are obviously involved
in the process of keeping our balance while we were standing,
we also varied the posture as a second variable between standing
and sitting. Third, we varied the direction of the interaction
between extension and flexion, so upward movements of the toes,
or downward like grabbing movements. Finally, we want to
understand the capabilities for fine-grained interaction. So we
also varied the subdivision of the interaction area as the
scaling with 4, 12 and 28 subdivisions per direction, so
that means in both extension and flexion direction, as well as
the target with four levels. Through the design of the
scaling and the target factors, all targets were in the center
of a field of the scaling. To currently track the participants
toe movements, we used an optical tracking system and
attached reflective markers to the hallux and third toe. We
then calibrated the maximum movement range per participants,
and mapped our targets to this range. Now, what did we have our
participants do? we placed them in front of a screen that
depicted the scaling, you can see it here on the on the slide.
And then one of the targets was highlighted in red, and the
participants first task was to move the toe to the highlighted
field. You can see that the cursor position was highlighted
in blue, and once one participants reached the target,
their task was to press a button that they had held in the hand.
Directly after the click, you can see that the system removed
all highlighting, and the participant second task was to
hold that position steady for three seconds. And this is
something that could be used for selection in a later system
similar to dwell-based selection in a eye-based interaction
technique. So here you see an overview of the methodology of
our experiment. We have the five independent variables that I
already talked about. And we measured these dependent
variables to assess the accuracy, efficiency and user
experience of our 18 participants. Now let's have a
short look at the results and just as a warning, this is a
really number heavy paper so I only provide you with a short
glimpse on the results here. For much much more findings, please
have a look at the paper So the first thing that I wanted to
share with you is that toe based interaction actually works.
Obviously, the scaling so the number of possible items to
select had the biggest impact on the accuracy, but with four
targets per direction, we found accuracy rates around 98-99%
when using all toes together. When we increase the number of
options, we obviously decrease the accuracy. So remember,
toe-based interaction works, but only with a limited number of
options. Another thing that showed a strong influence on the
accuracy was the selection of toes. We found that all toes
worked significantly better across all conditions compared
to hallux, or lateral toes. Beyond the accuracy, we also
found significantly increased task-completion times for
independent toe movement, especially flexing the lateral
toes made the interaction really slow. This dislike of using the
toe separately is also something that we saw on the
questionnaires. As you can see here, there was a much better
feedback for all toes compared to hallux, or lateral on the
convenience of the system. So remember, independent toe
movement is to be considered difficult to impossible. When we
looked for the posture, we found that sitting versus standing did
not have a big influence on the accuracy rates. However, we
found that extension movements were significantly faster while
standing compared to flexion movements. When we look at the
question as again, we see that extension was rated more
convenient, and less physically demanding compared to flexion.
Our explanation for this is that we need to shift our weight so
we can bend our toes to flex or flex our toes downward while
standing why we can extend them, so move them upwards, without
any problems. So remember, while standing, give the user the
option to interact with the system using extension. At last
point that I wants to share with you is that while four options
seems to be the only feasible option wide standing, we can
have more options while sitting where we found also comparable
accuracy rates for seated interaction in flex direction
with 12 targets, thats three times the targets, but basically
no difference in the accuracy rates. So remember, while
sitting you can give the user more options through flex
directions. Now, where would we use input techniques built
around toe movements. One example could be private
interaction in mobile settings. While riding the bus for
example, we could quickly and easily switch to the next song
or adjust the volume without using our hands or using voice
commands, which basically automatically would share our
music selection with the people around us. Another example is
situations where our hands are dirty. In this example here we
can control an application that shows cooking instructions on a
display without touching it. As a last example, we think that
toe-based interaction techniques are a great fit for head-mounted
displays and could support input in a variety of outdoor and
mobile situations. And this list is of course not conclusive.
There are a lot of other use cases, for example, for people
with injuries or disabilities or in other situation where
traditional input devices and modalities are not accessible or
practical. And with this, I want to end my talk on this toe-tally
new interaction concept. Thank you and see you in Hamburg.