wifi gestures


Wisee uses wi-fi signals
to recognise body
gestures
By Leo Kelion
Technology reporter
5 June 2013 Last updated at 14:13
The Wisee system does not require a
camera to be attached to a computer for
it to recognise gestures
Researchers say they have found a
way to detect and recognise human
gestures based on how they affect wi-
fi signals.
They suggest it could let users control
home appliances with a wave of the hand
while in any room of the house.
They say the WiSee system offers a
“simpler, cheaper” alternative to
Microsoft’s camera-based Kinect and
other specialist gesture sensors.
However, other experts in the field
question whether the new tech will be
able to be as accurate.
Details about the project have been
published by the University of
Washington’s computer science
department ahead of the Mobicom
computing conference in Miami in
September. The paper is a “working
draft” and has not appeared in a journal.
Doppler effect
The researchers suggest offering an
alternative to a vision-based system
could make a range of home-based
gesture controls practical.
“For example, using a swipe hand motion
in-air, a user could control the music
volume while showering, or change the
song playing in the living room while
cooking, or turn up the thermostat while
in bed,” wrote lead researcher Shyam
Gollakota.
To achieve this, the researchers have
experimented with the Doppler effect –
the way a wave’s frequency changes at
the point it is observed depending on
the source of the wave’s movements.
The best known example of the effect is
how one hears the pitch of a train’s
whistle change as a it approaches and
then passes.
The team say a wireless router can be
used to detect related changes in wi-fi
signals – which are electromagnetic
waves – as they reflect off a moving
human body.
By using a specially developed software
algorithm, the computer scientists say,
they were able to distinguish nine
different types of movements including a
pushing motion, a punch, a circular hand
movement and a kick.
To demonstrate the system, the team
carried out tests in an office and a two-
bedroom apartment.
They say their system was able to
correctly identify 846 of the 900
gestures performed – a 94% accuracy
rate.
They say these included situations in
which the user was in a different room
to both the wi-fi transmitter and
receiver, requiring the waves to pass
through walls before being detected.
Gesture passwords
The researchers acknowledge the risk of
such a system being triggered by
unintended gestures or even the risk of a
hacker seeking to take control of a
target’s equipment.
The researchers say their software can
use a wi-fi router to detect when a
human is making a gesture
To tackle this they suggest a password
system that would involve the user
repeating a preset sequence of gestures
four times in order to ready their
equipment for a command.
They say that an added benefit of this
would be that it would reduce the risk of
false positives – situations when the
system mistakes natural variance in wi-fi
signals for a gesture.
The researchers claim their equipment
can work with up to five people in the
vicinity of the router so long as it it is
fitted with multiple antennas. But they
note that the more people there are, the
less accurate the system becomes.
They say their next step is to try to work
out the best way to use the system to
control multiple devices at once.
In the meantime they have set up a
website to publicise WiSee , suggesting
they want to bring it to market.
“Imagine that in the near future you
would buy wireless router which could
also do gesture recognition. WiSee
enabled,” they say.
“Unlike other gesture recognition
systems like Kinect, Leap Motion or MYO
[sensor armband], WiSee requires
neither an infrastructure of cameras nor
user instrumentation of devices.
“WiSee requires no change to current
standards.”
Limited accuracy
Dr Richard Picking – a human-computer
interaction specialist at Glyndwr
University in Wrexham – said the US
team’s research had merit, but would
still need to overcome several hurdles to
become a commercial product.
“There is real potential for WiSee to
compete with other devices in their
established markets, such as gaming and
entertainment,” he told the BBC.
“However, although the developers claim
that it is unlikely that false commands
could be triggered and that it is
essentially a secure technology, there is a
long way to go before people will be
convinced that it will be reliable and safe
enough to control household appliances.”
Another activity recognition expert –
Daniel Roggen from Newcastle University
– agreed that the system had potential,
praising its idea of reusing existing
resources rather than requiring the
installation of cameras and other
sensors.
Microsoft’s Kinect 2 sensor uses a 1080p
resolution camera to improve its activity
recognition abilities
However, he noted that the wi-fi
equipment used by the researchers was
more expensive than the norm, costing
about 10 times the price of Microsoft’s
Kinect.
“It remains to be seen if either a single
such device is sufficient to cover an
entire house and if the price of the
equipment can be brought down,” he
said.
“The activities presented in the paper
are also very coarse. It remains to be
seen if subtler human behaviours can be
picked up.
“[By contrast] Microsoft’s Kinect is
specifically designed for activity
recognition. As such, it has an advantage
over ‘opportunistic sensing’ approaches,
and it can pick up subtler movements
such as finger movements in its newest
version.”

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