AllSee detects the unique signal changes (shown on the oscilloscope) and classifies a rich set of hand gestures.
Mute the song playing on your smartphone in your pocket by flicking your
index finger in the air, or pause your "This American Life" podcast
with a small wave of the hand. This kind of gesture control for
electronics could soon become an alternative to touchscreens and sensing
technologies that consume a lot of power and only work when users can
see their smartphones and tablets. University of Washington computer scientists have built a low-cost
gesture recognition system that runs without batteries and lets users
control their electronic devices hidden from sight with simple hand
movements. The prototype, called "AllSee," uses existing TV signals as
both a power source and the means for detecting a user's gesture
command. "This is the first gesture recognition system that can be implemented
for less than a dollar and doesn't require a battery," said Shyam
Gollakota, a UW assistant professor of computer science and engineering.
"You can leverage TV signals both as a source of power and as a source
of gesture recognition."
The technology is set to appear April 2-4 at the Symposium on Networked Systems Design and Implementation conference in Seattle.
The
researchers built a small sensor that can be placed on an electronic
device such as a smartphone. The sensor uses an ultra-low-power receiver
to extract and classify gesture information from wireless transmissions
around us. When a person gestures with the hand, it changes the
amplitude of the wireless signals in the air. The AllSee sensors then
recognize unique amplitude changes created by specific gestures.
Sensors
use three to four times less power than existing gesture recognition
systems by harvesting power from wireless transmissions. This allows for
mobile devices to always have the gesture technology on and enabled.
Gesture
recognition already is possible on some mobile devices, including the
Samsung Galaxy S4 smartphone. But users have to first manually enable
the feature and be able to see the device for the gesture technology to
work, and if left on, the gesture system quickly drains the phone's
battery. In contrast, AllSee consumes only tens of microwatts of power
and can always be left on. The user could gesture at the phone in a
pocket or handbag to change the volume or mute the phone without having
to touch or see the phone. This technology could allow sensors to be
attached to household electronics, making it possible to interact with
everyday objects using gestures and also connect them to the Internet
and to each other in an "Internet of Things" world.
"Beyond
mobile devices, AllSee can enable interaction with Internet of Things
devices. These sensing devices are increasingly smaller electronics that
can't operate with usual keypads, so gesture-based systems are ideal,"
said Bryce Kellogg, a UW doctoral student in electrical engineering.
The
UW team tested AllSee's capabilities on smartphones and battery-free
sensors using eight different hand gestures such as pushing or pulling
to zoom in and out. The prototype could correctly identify the gestures
more than 90 percent of the time while performed more than 2 feet away
from the device.
Researchers have tested the technology for
response time and whether it can distinguish between other motions and
those directed at it. They found that the technology's response time is
less than 80 microseconds, which is 1,000 times faster than blinking an
eye.
"This enables a seamless and interactive experience for the
user," said Vamsi Talla, a UW doctoral student in electrical
engineering. The researchers also designed a wake-up gesture that allows
the system not to confuse unintentional motions for actual gestures.
This
technology builds on previous work by Gollakota on leveraging Wi-Fi
signals around us for gesture recognition around the home. Prior
wireless gesture recognition techniques, however, consume tens of watts
of power and aren't suitable for mobile or Internet of Things devices.
The research is funded by a Google Faculty Research Award and the Washington Research Foundation.
Video: http://youtu.be/tJCQZxi_0AI
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