tiple antennas attached to a single device, projects like WiSee3 and Wi Track1
began presenting credible solutions.
WiSee was able to send signals from
outside a room and track simple movements of a human; WiTrack demonstrated how human hand gestures can
be decoded from signal perturbations.
With greater push, the techniques were
becoming amenable to practical off-the-shelf devices.
One may wonder why RADAR technologies are not directly applied to this
problem. The challenges were threefold: ( 1) RADAR had mostly catered to
open-space outdoor applications; indoor multipath, on the other hand, is
significantly more complex, especially
when it came to “localizing” the object. ( 2) The large size of metal planes,
tornadoes, and buildings produced
strong reflections, while their movements left discernible residues in Doppler Shifts. Human-sized objects, and
their interaction with 2.4GHz signal
wavelengths, called for new approaches. ( 3) Finally, the antennas in many
RADAR solutions were large, often offering the ability to rotate. With indoor
WiFi systems, the limited antenna array needed different solutions as well.
At this time, a non-trivial jump
emerged when Adib et. al. showed the
feasibility of measuring a baby’s breath-
ing and average heartrate. 2 At a high
level, the movement of the chest dur-
ing the act of breathing alters the
reflections enough that it could be
sensed and (heart-rate) counted with
consistency. Other movements in the
environment would obviously pollute
such sensing, however, the rhythmic
frequency of breathing is amenable to
signal isolation. Moreover, since the
breathing rate is related to the average
heart rate, it was becoming possible to
approximate an ECG device.
The natural next step was to count
the heartrate precisely. It is against
this backdrop that the following paper made another leap in the ability
to zoom into human micro-motions.
Adib et. al. shows that not only can the
heartrate be counted with accuracy
comparable to ECG devices, but the
variabilities of the heart signals—in
each pulse—can be recognized as well.
The implications are exciting—various
emotions, such as happiness, excitement, sadness, manifest themselves
into heartbeat patterns that in turn
become visible in the ECG output. This
paper reveals how these minute patterns of the heart, buried in the breathing action of the individual, and further
buried in all the environmental dynamism, can be extracted consistently,
such that a simple classifier can predict the user’s emotion. Yes, you read
it right; tomorrow’s smart-home may
indeed turn down the lights and play
a soft country song when you are back
home, because your WiFi base station
may automatically sense that you are
tired and glum. Let me stand no more
in your way to reading this paper.
1. Adib, F., Kabelac, Z., Katabi, D. and Miller, R.C. 3D
tracking via body radio reflections. In 11th USENIX
Symp. Networked Syst. Design and Implement.
2. Adib, F., Mao, H., Kabelac, Z., Katabi, D. and Miller, R.C.
Smart homes that monitor breathing and heart rate.
3. Pu, Q., Gupta, S., Gollakota, S. and Patel, S. Whole-home gesture recognition using wireless signals. In
Proceedings of the 19th Annual Intern. Conf. Mobile
Comput. & Netwk. (2013), ACM, 27–38.
4. Wilson, J. and Patwari, N. Demo abstract: Radio
tomographic imaging. MobiCom (2008).
5. Youssef, M., Mah, M. and Agrawala, A. Challenges:
Device-free passive localization for wireless
environments. MobiCom (2007).
Romit Roy Choudhury is a professor and Jerry Sanders III
AMD Inc. Scholar in the Department of ECE and CS at
the University of Illinois at Urbana-Champaign, IL, USA.
Copyright held by author.
THE TITANIC DISASTER in 1912 prompted the first patent in echo-location,
where sound waves would be sent under water to detect the presence of
objects. Through the next decade, the
technology matured into what was
called SONAR in 1930, an acronym for
SOund Navigation And Ranging. At the
peak of World War II in 1940, in-air
technologies matured fully into what
the U.S. Navy called RADAR, or RAdio
Direction And Ranging. The core principle in all of them is intuitive—
detecting the presence and movement of objects by transmitting a signal toward
them and analyzing their reflections.
Of course, these “objects” evolved
through the course of history, starting
from icebergs, submarines, and airplanes, to clouds, tornados, and weather conditions, to today’s images of the
urban environment from self-driving
cars. In this evolving timeline, the next
“object” is likely to be humans; and the
next RADAR-capable device may already
be in your home: your WiFi base station.
A number of research groups in academia and industry are exploring the
possibility of sensing humans through
WiFi signals. The core vision was
seeded by a challenge paper in ACM
MobiCom, 5 where authors envisioned
device-free localization. Said differently,
could the WiFi base station transmit
a carefully designed signal, gather
the various signal components that
bounce off the surroundings (
including humans), and infer the location of
one or many users? Since humans impact the attenuation and reflections of
these signals—called multipath—the
challenge was essentially around careful multipath disentanglement.
Clusters of ideas emerged. Patwari
et. al. 4 and others instrumented the environment with multiple transmitters
and receivers and constructed a 3D lattice of wireless links, somewhat similar
to criss-cross laser beams. As humans
moved through this wireless environment, they “cut through” different subsets of links, revealing their locations.
Progress accelerated and, with mul-
Is Your WiFi a Sensor?
By Romit Roy Choudhury
To view the accompanying paper,
A number of research
groups are exploring
the possibility of
through WiFi signals.