Signal Processing - March 2016 - 57

sensitivity versus false positive rate. A
the impact shock during a body's fall. The
The adoption of a devicein-lab system of Figure 8(a) consists of a
free wireless fall detection sensitivity of 0.97 and false positive rate
of 0.007 compares well with performancdeployment of wireless devices exchangtechnology is highly
es of existing device-based systems [30].
ing data over 2.6-GHz bands using an
attractive in the context
OFDM radio front end. A single-antenna
of assisted living, as a
OFDM transmitter communicates with a
Concluding remarks
receiver employing two antennas (with
and future directions
person who has fallen
spacing of 24 cm). The receiver extracts
This article focuses on device-free radio
might not only be unable
and processes two, possibly incomplete,
vision systems acting as a flexible sensto activate a personal
CSI power footprints / Ω 6st ,@ from the
ing tool and addressing key challenges in
emergency response
assisted living applications. The goal of
corresponding links (LOS and NLOS)
system but may have also
this emerging research field is to develop
, ! " , 1, , 2 , . Body falling is monitored
forgotten how to use it.
models and processing methodologies for
over a predefined position (x): localizaexploiting the inherent (but currently
tion can be obtained by DF-L methods.
unused) sensing capabilities of the multiT he obser ved sequences st , a re
tude of available wireless communication links, opening
modeled by a hidden Markov model (HMM) [23], [24] with state
Q
also to investigate new radio technologies and unexplored
space Q , ^Hh ! R Q # 1 = [q j] j = 1 containing Q-selected values from
bands. Future research on radio vision systems is expected
the CQI profiles Ts , ^Hh learned during training for falling-state
to combine the use of localized RF signal inspection with
estimation. HMM parameters, m , ^Hh = [A ,, B ,, π ,], include
large-scale and big-data processing. Running real-time anaprobabilities of state transition 6A ,@i, j = p 6q t = q i | q t - 1 = q j@,
lytics from massive volumes of RF data will pose new sigobservation 6B ,@i, j = p 6st ,,t = s i | q t = q j@, and initial state
6π ,@i = p [q 0 = q i] . The HMM parameters are learned by the
nal processing relevant problems, as well as the redesign of
conventional statistical learning tools applied to unpreceexpectation maximization algorithm (e.g., Baum-Welch algodented high-dimensional data structures.
rithm) and trained separately for each link [23]. Other methods
[24] can be adopted to leverage space-time profiles correlation
over colocated links. A decision about a human fall is based on
Authors
the model likelihood
Stefano Savazzi (stefano.savazzi@ieiit.cnr.it) is a researcher at
the Institute of Electronics, Computer, and Telecommunication
L [st , | m , (H)] = / p [st ,, q | m , (H)]
(7)
Engineering of Consiglio Nazionale delle Ricerche, Italy. He
6q ! Q , (H)
received the Ph.D. degree (with honors) in information
technology from Politecnico di Milano, in 2008. He was a
with state sequence q = [q 1, f, q T ] and joint probability
T
t
t
researcher at Uppsala University in 2005, the University of
p [s ,, q | m , (H)] = P t = 1 p [s ,, t | q t] $ p [q t | q t - 1] . F u n c t i o n s
California San Diego in 2008, and Forschungszentrum
L 6st , | m ,@ are continuously evaluated for both links [Figure
Telekommunikation Wien in 2010. His main research interests
8(a) on the right].
include cooperative and cognitive wireless networks, industrial
Fall detection can be based on a hard decision with respect to
Internet of Things, and device-free methods for radio-frequenprecalibrated threshold x such that L 6st , | m , ^Hh@ /
cy vision and localization.
L 6st , | m , ^Qh@ 2 x. Likelihood L 6st , | m , ^Qh@ is obtained for
Stephan Sigg (stephan.sigg@aalto.fi) is an assistant profesHMM m , ^Q h that considers arbitrary (but safe) body movements
sor in the Department of Communications and Networking at
in the same position. After the impact shock is detected, a simple
Aalto University, Finland. He was previously with the Computer
change detector can be applied to the observed CQI sequences
Networks group of Georg-August-University of Göttingen and a
for tracking any postfall event and, in turn, detect possibly long
researcher at TU Braunschweig. As an academic guest, he has
lie conditions, corresponding to negligible RF fluctuations.
cooperated with the Wearable Computer Lab at ETH-Zurich, the
Nodes Laboratory at the University of Helsinki, and the
Impact shock detection
National Institute of Informatics in the information systems
In the complete case study highlighted in Figure 8(b), the
architecture research division. He obtained his Ph.D. degree
human fall detector is now based on an optimized subset of
(with honors) from the University of Kassel, Germany. His
pre-existing links [23] deployed around the subject of interest
research interests include the design, analysis, and optimization
and selected during a calibration procedure (noninformative
of algorithms for ubiquitous systems, in particular for devicelinks are purged). A decision about the fall/nonfall event is
free passive activity recognition.
based on majority voting over the optimized link subset.
Monica Nicoli (monica.nicoli@polimi.it) is an assistant
The analysis of detector performance is crucial: undeprofessor in the Dipartimento di Elettronica, Informazione e
tected falls might have a dramatic impact-on the other
Bioingegneria, Politecnico di Milano, Italy. She received her
hand, an excessive number of false activations might cause
Ph.D. degree in communication engineering from Politecnico
the detector to be perceived as useless. Validation of detecdi Milano in 2002. She was visiting researcher at Uppsala
tor accuracy is thus illustrated in Figure 8(b) where the
University, Sweden, in 2001. Her research interests are in the
receiver operating characteristic (ROC) curve relates
IEEE SIgnal ProcESSIng MagazInE

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March 2016

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57



Table of Contents for the Digital Edition of Signal Processing - March 2016

Signal Processing - March 2016 - Cover1
Signal Processing - March 2016 - Cover2
Signal Processing - March 2016 - 1
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Signal Processing - March 2016 - 128
Signal Processing - March 2016 - Cover3
Signal Processing - March 2016 - Cover4
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