Skinput
To expand the range of sensing modalities for always
available input systems, we introduce Skinput, a novel input technique that
allows the skin to be used as a finger input surface. In our prototype system,
we choose to focus on the arm (although the technique could be applied
elsewhere). This is an attractive area to appropriate as it provides
considerable surface area for interaction, including a contiguous and flat area
for projection (discussed subsequently).
Further more, the forearm and hands contain a complex assemblage of
bones that increases acoustic distinctiveness of different locations.
Absract
The Microsoft company have developed Skinput, a technology
that appropriates the human body for acoustic transmission, allowing the skin
to be used as an input surface. In particular, we resolve the location of
finger taps on the arm and hand by analyzing mechanical vibrations that
propagate through the body. We collect these signals using a novel array of
sensors worn as an armband. This approach provides an always available,
naturally portable, and on-body finger input system.
Introduction
Devices with significant computational power and capabilities
can now be easily carried on our bodies. However, their small size typically
leads to limited interaction space (e.g., diminutive screens, buttons, and jog
wheels) and consequently diminishes their usability and functionality. Since we
cannot simply make buttons and screens larger without losing the primary
benefit of small size, we consider alternative approaches that enhance
interactions with small mobile systems. One option is to opportunistically appropriate
surface area from the environment for interactive purposes. For example, [10]
describes a technique that allows a small mobile device to turn tables on which
it rests into a gestural finger input canvas. However, tables are not always
present, and in a mobile context, users are unlikely to want to carry
appropriatedsurfaces with them (at this point, one might as well just have a
larger device). However, there is one surface that has been previous overlooked
as an input canvas, and one that happens to always travel with us: our skin.
Whole Arm
(Five Locations)
Another gesture set
investigated the use of five input locations on the forearm and hand: arm,
wrist, palm, thumb and middle finger (Figure 7, “Whole Arm”). We selected these
locations for two important reasons. First, they are distinct and named parts
of the body (e.g., “wrist”). This allowed participants to accurately tap these
locations without training or markings. Additionally, these locations proved to
be acoustically distinct during piloting, with the large spatial spread of
input points offering further variation. We used these locations in three
different conditions.
Identification
Of Finger Tap Type
Users can “tap” surfaces with their fingers in several
distinct ways. For example, one can use the tip of their finger (potentially
even their finger nail) or the pad (flat, bottom) of their finger. The former
tends to be quite boney, while the latter more fleshy. It is also possible to
use the knuckles (both major and minor
metacarpophalangeal joints). To evaluate our approach’s ability to distinguish
these input types, we had participants tap on a table situated in front of them
in three ways (ten times each): finger tip, finger pad, and major knuckle. A
classifier trained on this data yielded an average accuracy of 89.5% (SD=4.7%,
chance=33%) during the testing period. This ability has several potential uses.
Perhaps the most notable is the ability for interactive touch surfaces to
distinguish different types of finger contacts (which are indistinguishable in
e.g., capacitive and vision-based systems). One example interaction could be
that “double knocking” on an item opens it, while a “pad-tap” activates an
options menu.
Conclusion
In this paper, we have presented our approach to appropriating
the human body as an input surface. We have described a novel, wearable
bio-acoustic sensing array that we built into an armband in order to detect and
localize finger taps on the forearm and hand. Results from our experiments have
shown that our system performs very well for a series of gestures, even when
the body is in motion.
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