8,214 research outputs found
Crowd Counting Through Walls Using WiFi
Counting the number of people inside a building, from outside and without
entering the building, is crucial for many applications. In this paper, we are
interested in counting the total number of people walking inside a building (or
in general behind walls), using readily-deployable WiFi transceivers that are
installed outside the building, and only based on WiFi RSSI measurements. The
key observation of the paper is that the inter-event times, corresponding to
the dip events of the received signal, are fairly robust to the attenuation
through walls (for instance as compared to the exact dip values). We then
propose a methodology that can extract the total number of people from the
inter-event times. More specifically, we first show how to characterize the
wireless received power measurements as a superposition of renewal-type
processes. By borrowing theories from the renewal-process literature, we then
show how the probability mass function of the inter-event times carries vital
information on the number of people. We validate our framework with 44
experiments in five different areas on our campus (3 classrooms, a conference
room, and a hallway), using only one WiFi transmitter and receiver installed
outside of the building, and for up to and including 20 people. Our experiments
further include areas with different wall materials, such as concrete, plaster,
and wood, to validate the robustness of the proposed approach. Overall, our
results show that our approach can estimate the total number of people behind
the walls with a high accuracy while minimizing the need for prior
calibrations.Comment: 10 pages, 14 figure
Breathfinding: A Wireless Network that Monitors and Locates Breathing in a Home
This paper explores using RSS measurements on many links in a wireless
network to estimate the breathing rate of a person, and the location where the
breathing is occurring, in a home, while the person is sitting, laying down,
standing, or sleeping. The main challenge in breathing rate estimation is that
"motion interference", i.e., movements other than a person's breathing,
generally cause larger changes in RSS than inhalation and exhalation. We
develop a method to estimate breathing rate despite motion interference, and
demonstrate its performance during multiple short (3-7 minute) tests and during
a longer 66 minute test. Further, for the same experiments, we show the
location of the breathing person can be estimated, to within about 2 m average
error in a 56 square meter apartment. Being able to locate a breathing person
who is not otherwise moving, without calibration, is important for applications
in search and rescue, health care, and security
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
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