Signal Processing - March 2016 - 49

and ankles, when a person is standing, there is a larger RSS
attenuation with respect to the same body lying on the floor.
The profiles Ts , ^Hh, superimposed in solid lines, average out
noise and time-warping effects. Detection of the human state
can be based on matching (e.g., using simple time-domain features) the observed entries st , ^Hh, or the estimated deviations
Tst , ^Hh, with the corresponding RSS profiles learned during
a training procedure. Human-state estimation possibly entails
denoising, time warping, and reconstruction of missing RSSI
observations (i.e., by interpolation methods) [12], [13]. Missing
or incomplete data can be represented as / Ω 6st , ^Hh@ over the
set of t ! Ω 3 J received frames, where / Ω ($) is the sampling operator nulling the entries of st , ^Hh not in Ω [7].

Baseband modeling of CSI
Baseband CSI measures the channel response at symbol level:
CSI estimation is typically obtained from training/reference
signals (RSs) multiplexed with information symbols and periodically placed in every frame. Therefore, in contrast to RSS,
processing of CSI for the purpose of radio vision can leverage
on multiple independent measurements at frame level and can
be used to capture fast human body movements and gestures.
Assuming frequency-flat channel as for narrowband communication but time-varying for dynamic multipath environments,
the received RSs rt = h t (H) ~ t + n t at symbol time t ! J
(with ~ t and n t the transmitted RSs and the noise term,
respectively), captures the moving body in state H through the
corresponding complex channel envelope adopted from (1)
h t (H) = / k = 0 a k (t | H) e
N

-jz k (t | H)

= h (Q) + Th t (H) .

(3)

Human body effects on the channel response are now embedded into a characteristic footprint of channel variations over T
received symbols h ^Hh ! C T # 1 = [h t ^Hh] t ! J . The CSI profile set is Th ^Hh ! C T # 1 = [Th t ^Hh = h t ^Hh - h ^Q h] t ! J , with
h ^Q h = E [h t ^H = Q h] being the average response for the
human-free state. Noisy profiles Tht ^Hh = [ht t ^Hh - ht ^Q h] t ! X
with estimated channels ht t ^Hh and human-free response ht ^Q h
are typically observed over a subset of times (or symbol indexes) Ω 3 J accounting for the training/data multiplexing, and
missing symbols.
The use of multicarrier (OFDM) modulation enables multidimensional processing of CSI over the time-frequency grid and
allows a fine-grained classification of human motion [12]. As
depicted in Figure 3(b), the CSI estimation is carried out by
periodic transmission of RSs over standard defined time-frequency patterns [3]. The received RSs rt over the K pilot
subcarriers " f1, f, fK , inside OFDM symbol t can be written as rt = diag [~ t] $ h t ^Hh + n t with vector ~ t collecting
the transmitted RSs, and baseband channel vector
f
h t ^Hh = [H f, t ^Hh] fK= f1 containing the Fourier transform
F ($) of channel h t ^x | Hh
H f, t ^Hh = H f ^Q h + F (Th t ^x | Hh) | f .
14444244443
TH f,t ^Hh

(4)

The CSI footprint is the matrix H ^Hh ! C K # T = [h 1 ^Hh, f,
h T ^Hh] t ! J wit h hu ma n-i nduce d prof i le TH ^Hh =
t ^Hh is evaluated over the
6H ^Hh - H ^Q h@ . The estimate H
time-frequency set Ω now accounting for framing structure
and irregular time-frequency RSs spacing. In Figure 3(b)
(bottom), an OFDM transmission over 2.6 GHz is implemented in-lab using software-defined radio (SDR) devices: a
person is crossing the link and standing for four seconds,
causing an average attenuation of 5 dB. The CSI power foott f, t ^Hh 2 are shown for K = 4 pilots and
print estimates H
T = 223, 000 symbols.
A crucial problem for quantitative evaluation of radio
vision system performance is the availability of a simple but
realistic model to describe human body-induced shadowing.
Ray-tracing [14], EM/stochastic [15], [16], and geometricbased (see [2] for a review) models have been investigated to
predict the correlation between the human body position x
and the corresponding channel perturbations. EM methods
that exploit geometric/uniform theory of diffraction (GTD/
UTD), as well as ray-tracing algorithms, can be employed
for their ability to accurately evaluate the EM field at the
receiver, but they are usually very complex, time consuming
and, above all, require perfect knowledge of the shape, composition, and properties of the obstacle. In "DiffractionBased Modeling of Human Body Shadowing," we consider a
simplified but effective framework based on the Fresnel-
Kirchoff diffraction theory as shown in Figure 4(a).

research on radio vision: A survey
There has recently been an increasing interest in research on
wireless human tracking via RF devices. This broadly
defined domain encompasses different research areas such
as signal processing, computer vision, communication networks, and human-machine interfaces. The first experimental activity dates back to the works [17], [18] showing that
body motions leave a characteristic footprint on RSS patterns [17], while RSS fluctuations can be effectively used for
body localization [18].
Focusing on device-free human body localization, the
radio tomography imaging (RTI) proposed in [6], [19], and
[20] adopts computed tomography methods to reconstruct an
image of the object(s) inside the network area. The technology has been now transferred to a commercial product (i.e.,
Xandem system) targeting assisted living applications. The
methods introduced in [5], [9], and [11] allow the explicit
tracking of the target's (or targets') position using a Bayesian
approach that jointly process the RSS mean and standard
deviations [11]. More recently, device-free systems based on
Bayesian tracking of RSS profiles have been also designed
for obstacle/object two-dimensional (2-D) mapping [21],
detection of human breathing [13], [22], and fall detection
[23], [24]. Human gesture recognition and body motion
detection have been addressed in recent research projects
(SenseWaves, E-eyes, WiSee, and Wi-Vi) targeting both
RSS [25] and baseband CSI analytics using radio devices
operating at 900 MHz [8], [26], 2.4 GHz with 20-MHz

IEEE SIgnal ProcESSIng MagazInE

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March 2016

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49



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
Signal Processing - March 2016 - 2
Signal Processing - March 2016 - 3
Signal Processing - March 2016 - 4
Signal Processing - March 2016 - 5
Signal Processing - March 2016 - 6
Signal Processing - March 2016 - 7
Signal Processing - March 2016 - 8
Signal Processing - March 2016 - 9
Signal Processing - March 2016 - 10
Signal Processing - March 2016 - 11
Signal Processing - March 2016 - 12
Signal Processing - March 2016 - 13
Signal Processing - March 2016 - 14
Signal Processing - March 2016 - 15
Signal Processing - March 2016 - 16
Signal Processing - March 2016 - 17
Signal Processing - March 2016 - 18
Signal Processing - March 2016 - 19
Signal Processing - March 2016 - 20
Signal Processing - March 2016 - 21
Signal Processing - March 2016 - 22
Signal Processing - March 2016 - 23
Signal Processing - March 2016 - 24
Signal Processing - March 2016 - 25
Signal Processing - March 2016 - 26
Signal Processing - March 2016 - 27
Signal Processing - March 2016 - 28
Signal Processing - March 2016 - 29
Signal Processing - March 2016 - 30
Signal Processing - March 2016 - 31
Signal Processing - March 2016 - 32
Signal Processing - March 2016 - 33
Signal Processing - March 2016 - 34
Signal Processing - March 2016 - 35
Signal Processing - March 2016 - 36
Signal Processing - March 2016 - 37
Signal Processing - March 2016 - 38
Signal Processing - March 2016 - 39
Signal Processing - March 2016 - 40
Signal Processing - March 2016 - 41
Signal Processing - March 2016 - 42
Signal Processing - March 2016 - 43
Signal Processing - March 2016 - 44
Signal Processing - March 2016 - 45
Signal Processing - March 2016 - 46
Signal Processing - March 2016 - 47
Signal Processing - March 2016 - 48
Signal Processing - March 2016 - 49
Signal Processing - March 2016 - 50
Signal Processing - March 2016 - 51
Signal Processing - March 2016 - 52
Signal Processing - March 2016 - 53
Signal Processing - March 2016 - 54
Signal Processing - March 2016 - 55
Signal Processing - March 2016 - 56
Signal Processing - March 2016 - 57
Signal Processing - March 2016 - 58
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Signal Processing - March 2016 - 60
Signal Processing - March 2016 - 61
Signal Processing - March 2016 - 62
Signal Processing - March 2016 - 63
Signal Processing - March 2016 - 64
Signal Processing - March 2016 - 65
Signal Processing - March 2016 - 66
Signal Processing - March 2016 - 67
Signal Processing - March 2016 - 68
Signal Processing - March 2016 - 69
Signal Processing - March 2016 - 70
Signal Processing - March 2016 - 71
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Signal Processing - March 2016 - 73
Signal Processing - March 2016 - 74
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Signal Processing - March 2016 - 76
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Signal Processing - March 2016 - 78
Signal Processing - March 2016 - 79
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Signal Processing - March 2016 - 81
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Signal Processing - March 2016 - 85
Signal Processing - March 2016 - 86
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Signal Processing - March 2016 - 88
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Signal Processing - March 2016 - 91
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Signal Processing - March 2016 - 100
Signal Processing - March 2016 - 101
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Signal Processing - March 2016 - 106
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Signal Processing - March 2016 - 109
Signal Processing - March 2016 - 110
Signal Processing - March 2016 - 111
Signal Processing - March 2016 - 112
Signal Processing - March 2016 - 113
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Signal Processing - March 2016 - 116
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Signal Processing - March 2016 - 118
Signal Processing - March 2016 - 119
Signal Processing - March 2016 - 120
Signal Processing - March 2016 - 121
Signal Processing - March 2016 - 122
Signal Processing - March 2016 - 123
Signal Processing - March 2016 - 124
Signal Processing - March 2016 - 125
Signal Processing - March 2016 - 126
Signal Processing - March 2016 - 127
Signal Processing - March 2016 - 128
Signal Processing - March 2016 - Cover3
Signal Processing - March 2016 - Cover4
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