Gesture Recognition by Electromagnetic-Wave Reflectionsettings. Dynamic elements include moving...

Instituto Superior Técnico and Instituto de Telecomunicações, Lisboa – Portugal 1

Abstract—The interaction between users and devices tends to

be increasingly flexible. The need for harmonization of IT

platforms with the user has strongly increased. With a single

gesture we can control the TV sound, turn off the lights or even

call the emergency number. This feature revolutionizes

applications in various fields, including areas such as home

automation, health of the elderly and children, games, etc.

Actually, most of the deployed solutions based on sensors and

computer vision. However, the price of this vision sensor-based

technology and the difficulty of installation turns out to be

complicated its development on a large scale. This dissertation

addresses gesture recognition through the reflection of

electromagnetic waves. The aim is to develop a sustainable and

realistic technology for gesture recognition based on the reflection

of electromagnetic waves, giving solution to the challenges related

to this issue, such as getting the gestures information, influence of

dynamic elements in this context, fundamental utility of Doppler

Effect, minimizing interference, safety issues associated and the

effect of multipath approach.

Index Terms—Doppler Shift, Electromagnetic waves, Gesture

Recognition, MIMO, OFDM, Reflection, Wi-Fi.

I. INTRODUCTION

ESTURES enable a new interaction technique for

computing embedded in the environment. Commercially

available sensors facilitate gesture-based interaction using

depth sensing and computer vision. However, the burden of

installation and cost makes most vision-based sensing devices

hard to deploy at large scale. To overcome these limitations, a

part of this sensing has been moved onto the body and the

need for environmental sensors has been reduced. However,

even on-body approaches are limited to what people are

willing to carry and may be infeasible in some scenarios.

This paper addresses gesture recognition systems that

leverage wireless signals to enable sensing and recognition of

human gestures [1]. Since wireless signals do not require line-

of-sight and can traverse through walls, gesture recognition

can be achieved without requiring human body sensing

Manuscript received April 1, 2015. This work was supported

by Financiamento Estratégico UID/EEA/50008/2013, Instituto de

Telecomunicações. Authors are with Instituto de Telecomunicações, Instituto Superior

Técnico, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal (phone: +351-

218418479; fax: +351-218418472; e-mail: [email protected]).

devices. This can be achieved by looking at the Doppler shifts

and multi-path distortions that occur with these wireless

signals from human motion in the environment.

Doppler shift occurs whenever a wave source moves relative

to the observer. In the context of wireless signals, if we

consider the multi-path reflections from the human body as

waves from a source, then a human can perform a gesture,

resulting a pattern of Doppler shifts at the wireless receiver.

Human gestures result in very small Doppler shifts that can

be hard to detect from typical wireless transmissions (e.g., Wi-

Fi). A 1 m/s gesture results in a 33 Hz Doppler shift on a 5

GHz Wi-Fi transmission. Typical wireless transmissions have

orders of magnitude higher bandwidth (20 MHz for Wi-Fi).

Thus, for gesture recognition, we need to detect Doppler shifts

of a few Hertz from the 20 MHz Wi-Fi signal. By transforming

the received signal into a narrowband pulse with a bandwidth

of a few Hertz, this problem can be addressed. The receiver

must track the frequency of this narrowband pulse to detect the

small Doppler shifts resulting from human gestures.

Multiple people can affect the wireless signals at the same

time. The multiple input multiple output (MIMO) capability

can be used to focus on gestures from a particular user. MIMO

provides throughput gains by enabling multiple transmitters to

concurrently send packets to a MIMO receiver. If we consider

the wireless reflections from each human as signals from a

wireless transmitter, then they can be separated using a MIMO

receiver.

Algorithms to extract gesture information from

communication-based wireless signals need to be developed.

These algorithms extract the instantaneous Doppler shifts from

wideband OFDM transmissions that are typical to most

modern communication systems, including Wi-Fi.

For this study, we had a number of important and necessary

concepts so that we could understand the full question and the

technology involved. A more detailed explanation will be

given throughout this paper.

II. BASIC CONCEPTS

This section will address a number of key-concepts so we

can understand the questions and the technology involved in

later sections. It will be reviewed issues such as how waves

propagate from one point to another, emphasizing the

multipath, specifically reflections, Doppler effect and its

existence in the propagation of electromagnetic waves,

Gesture Recognition by Electromagnetic-Wave

Reflection

José M. Garcia and António L. Topa, Member, IEEE

G


communication standards such as IEEE 802.11, MIMO

antennas and its functioning, OFDM, among other issues.

a) Indoor Propagation

In order to understand the effects on propagation of

electromagnetic waves, it is necessary to recall three

electromagnetic wave propagation mechanisms: reflection,

diffraction and dispersion. The following definitions assume a

short-wavelength signal, long distance (relative to wavelength)

and sharp material boundaries for a typical indoor scenario.

Reflection occurs when the wave hits an object larger than the

wavelength. During reflection, part of the wave is transmitted

into the object with which the collided waveform. The

remaining part of the energy is reflected back to the

environment where the traveling wave was. Indoor objects like

walls and floors can cause reflection. When the path between

the transmitter and the receiver is blocked by a surface with

sharp irregularities, the transmitted waves undergo diffraction.

Diffraction allows the waves beyond the obstacle. The objects

that can cause diffraction include furniture and large

appliances. The third mechanism that contributes to the

propagation of electromagnetic waves is the dispersion or

scattering. Dispersion occurs when the wave propagates

through a medium in which there are a large number of objects

smaller than the wavelength. In an indoor environment, things

such as plants and tiny objects can cause dispersion. The

combined effect of reflection, diffraction and scattering causes

multipath. Multipath occurs when a transmitted signal arrives

at the receiver by more than one path. The signal components

from the different paths are combined at the receiver, resulting

in a distorted version of the transmitted waveform [2, 4, 5, 22].

b) Doppler Effect

The Doppler effect is essencial in the development of this

work, so it is very important to talk about the topic. Doppler

effect is the change in frequency of electromagnetic radiation

when the source and the observer are in motion relative to one

another. If the source is moving toward the observer, the

frequency of the radiation perceived by the observer will be

larger than the reference frame of the source [2, 15, 16]. 0f is

the frequency of the electromagnetic wave recorded in the

observer, ff is the frequency emitted by the source, c is the

relative speed between the observer and source, so the

equations for the Doppler effect take the following forms,

0

1

1f

v

cf fv

c

(1)

if the observer and the source deviate relative to one

another, and:

0

1

1f

v

cf fv

c

(2)

if the observer and the source approach relative to one

another.

c) IEEE 802.11

Another crucial topic is the 802.11 standard. It defines the

functions and services needed for users 802.11, so this

operates in two possible ways: Ad-Hoc or Infrastructure mode

[2]. It defines the regulation of the station’s mobility in each

operation mode [18]. It also describes the procedures and

techniques of the MAC level and the physical level that allow

the coexistence of multiple wireless 802.11 networks in the

same space, but where the user is only in one specific network

without interfering with the users of other networks present.

Defines the requirements and procedures necessary to maintain

the security of information circulating among wireless and

proper authentication of clients [3, 8, 19].

d) OFDM and MIMO Systems

Attempting to develop and design a technology as the one

mentioned previously, it is required highly advanced

assumptions. So, it is necessary to use OFDM and MIMO

antenna systems. The OFDM system is a subset of frequency

division multiplexing, where in a single channel there are

multiple subcarriers on adjacent frequencies. In addition, the

subcarriers of an OFDM system are superimposed to maximize

spectral efficiency [10]. Typically, overlapping adjacent

channels may interfere with each other. However, the

subcarriers of an OFDM system are orthogonal to each other.

Thus, they are able to overlap without interference. As a result,

the OFDM systems are able to maximize spectral efficiency

without causing adjacent channel interference. The frequency

domain of an OFDM system can be represented in Fig. 1 as a

set of channels separated by guard bands, consisting of

overlapping subcarriers [10].

Fig. 1 – OFDM system frequency domain

The OFDM is commonly applied in many emerging

communication protocols, because it offers several advantages


over traditional FDM approach to communications channels.

More particularly, OFDM systems allow greater spectral

efficiency, reduced intersymbol interference (ISI) and higher

resistance to multipath distortion arising [12].

In radio systems, MIMO is a method for multiplying the

capacity of a radio link with multiple transmission and

multiple reception antennas to mitigate the adverse effects of

multipath. MIMO has become an essential element of wireless

communication standards including IEEE 802.11n (Wi-Fi),

IEEE 802.11ac (Wi-Fi), HSPA + (3G), WiMAX (4G), and

Long Term Evolution (4G). In modern usage, MIMO refers

specifically, a practical technique of sending and receiving

more than one data signal in the same channel at the same time

through multiple propagation paths. The main challenge of the

future wireless communication systems is to provide high-

speed data with high quality service access. Since spectrum is

a scarce resource and propagation conditions are often hostile

due to fading caused by destructive adding components from

the multipath and interference by other users, it is necessary to

radically increase the spectral efficiency and improve the

reliability of systems [13].

III. INDOOR ELECTROMAGNETIC WAVE PROPAGATION

The electromagnetic propagation aims to study how energy

is transported along the environment. As mentioned earlier, it

is necessary to take into account the propagation mechanisms

(reflection, diffraction and dispersion) and adequately study

how electromagnetic waves behave. It is then essential and

important to try to see this behavior and quantify as much as

possible this interaction.

In this context, using software Winprop - Wallman &

Proman creates an environment, Fig. 2. It is an offices floor

where there is a large variety of materials and a large number

of divisions [2].

Fig.2 - Simulation scenario - 3D visualization

The physical environment can be classified based on static

and dynamic elements. Static elements comprise a variety of

natural and artificial materials, geometry and spatial limits

settings. Dynamic elements include moving objects (swaying

objects, people and cars seen through the windows). In the

following cases, it will, only, be considered static elements.

Since it is a radiopropagation problem it is needed to assign

the infrastructure and electromagnetic waves properties, so we

can get a realistic, robust and accurate analysis. It is shown in

table 1.

Table 1 - Electromagnetic and infrastructure properties [2]

After determining the structure of the scenario that is

simulated, we must choose the antenna that will be the source

of electromagnetic waves. The directive antenna is a WiMAX

90, with a power intensity of 1Watt, with a 5000MHz working

frequency, an azimuthal angle of 90º and an angle of downtilt

of 5º. In Fig. 3 is represented, the radiation diagram of the

antenna in 3D display and the vertical and horizontal planes,

respectively, on a logarithmic scale [2].

Fig.3 - Antenna Pattern (a) 3D (b) Vertical Plane (c) Horizontal Plane

Here, the model used is the multiwall COST 231. It is a

sophisticated empirical model. All walls which intersect the

direct ray between the emitter and receiver are considered and

for each intercepted wall, we set the properties of the material

and wave to quantify the attenuation. This method suggests a

decrease in the attenuation in each of the individual

obstruction as the number of obstacles increase [9].

2310

1 1 1 1

10 log( )wall

MWCOST

K KflI J

wallk flk

i k j k

L L n d L L

(3)

0L : Attenuation at 1 meter distance [dB];

n : Expoent decay parameter;

d : Distance between the transmitter and receiver [m];

wallkL : Attenuation due the wall kind i and due the

intercepted wall k [dB];


flkL : Attenuation due the floor kind i and due the intercepted

floor k [dB];

I : Number of the kinds of walls;

J : Number of the kinds of floors;

wallK : Number of crossed walls of kind i ;

flK : Number of crossed floors of kind j ;

It’s important to notice that the predictions of this model are

often too pessimistic,so there has been added a computational

extension to the model. With this extension, the model

achieves good results with faster computation times [2].

In order to try to describe what happens, it evaluates the

quantitative behavior of power with empirical models, close to

reality. In indoor environment, a transmitted radio wave

undergoes the transformation process shown, and reaches the

receiving antenna through more than one path, thus giving rise

to multiple paths, as already mentioned. It employs stochastic

theory and probability distribution functions. A vision

somewhat underestimated of this effect is a sign of changes

within the office, where if there is line of sight, it uses Rice

distribution and where there is no line of sight between the

transmitter and receiver, it is approached by Rayleigh

distribution. The Rayleigh distribution function describes a

process in which a large number of incident rays that are added

randomly obtaining the amplitude versus time. The distribution

of Rice is similar to the distribution of Rayleigh, except that

contains a strong dominant component. Usually the dominant

component is the direct ray or the first ground reflection [20].

The multipath introduces random variations in amplitude of

the received signal. The effects of multipath vary depending

the type of antenna used, as well as the location of the antenna

[19]. Fading can be fast or slow. Rapid variations over short

distances are set as the small-scale fading. Thus, fading effects

on a small scale can be described using the time delay

resulting from multipath. Since the signal may take many paths

before reaching the receiver, the signals will experience

different arrival times. In order to quantify certain indoor

routes, Figs. 4 and 5 are used. Fig. 4 is the first result of the

simulation. It is a line of sight scenario, where power generally

decreases as a function of distance. However, we must not

forget the contribution of several reflected rays with different

time delays. These may be either constructive or destructive

interference. Each line line illustrates, simbolically, a path

taken by a user and the corresponding power values are

represented as function of the distance. In this case, there is

some conclusions we can draw. In the near field zone (up to

2.3m from the transmitter), power decreases linearly in a

logarithmic scale. There is only the contribution of the direct

ray which decreases as the distance increases. Between 2.5m

and 10m of the route, there is a zone with variations since the

reflected rays interfere constructively and destructively. There

is also a corridor which can be seen as a waveguide. However,

due to the multiple inputs of this area, the contributions of the

rays are variable. Among the 10m and 25m from the

transmitter, there is again a linear decrease, as reflected rays

have less influence on the signal, since the area of the

infrastructure is wider, making it more dispersive with less ray

interference.

Fig.4 - Power quantification - LOS

It can be seen in Fig. 5, also tendentiously, the power

decreases depending on the distance. However, this decrease is

more pronounced when there is an obstacle (wall) and power

can also increase due to the communication channels between

the transmitter and the point at issue. It coincide with the areas

in LOS, one since the antenna is at 1.2 meters hight.

Fig.5 - Power quantification – NLOS

Now, we make use of simulations to study the trajectory and

orientation of the main rays transmitted and reflected in the

simulation scenario. The methods of tracing the rays is based

on Geometric Optics (GO) [21]. The direction of the new ray

is determined by Snell's Law. The ray tracing is done using the

ray launching method. The rays are homogeneously issued

from a unit sphere centered on the sender and all regions are

covered evenly by rays. Subsequently, the rays cross the

detection area around the receiver after a number of reflections

and transmissions. This is responsible for the received signal.

Increasing the number of rays, it reduces the probability of


error detection, however, since the detection area is a sphere,

the rays may not reach the receiver (too small sphere) or, on

the other hand, the rays may reach an area that normally would

not reach (too big sphere), resulting in inflation of the received

power.

In Fig. 6, as first example, we analyze an area without line

of sight between transmitter and receiver, however, the

inherent attenuation is not too significant.

Fig.6 - Example path of electromagnetic rays

The power delay profile (PDP) of multiple power

contributions can be seen in Fig. 7. Once we have the PDP of

the channel, the delay can be calculated by,

( )

( )

PDP

PDP

(4)

Note in this case that there is no influence of a dominant

component, but many small contributions, which may be quite

time-staggered.

Fig.7 - Power temporal delay profile at the point analysis

The directions of the various contributions can be analyzed

in Fig. 8, as well as its intensity. These are distributed evenly,

i.e., it has a uniform distribution of phase values. This occurs

since there is a large number of electromagnetic phenomena

between the transmitter and receiver [2].

Fig.8 - Phases of the contributions that reach the point

Finally, in Fig. 9, it is analyzed a point of space with line of

sight. The reception place analyzed is 21,89 meters appart

from the transmitter.

Fig.9 - Example path of electromagnetic rays

In this case, what immediately draws the attention is the

intensity of the first contribution to reaching the receiver,

achieving state, by implication, which is the direct ray. From

what has been said or thought, there should be less

contributions, however, this fact is due to the considerable

distance between the transmitter and receiver (21.89m) and

there are many possible points for reflection. In Fig. 10, it is

represented the power delay profile.

Fig.10 - Power temporal delay profile at the point analysis

In Fig. 11, the phase of these rays do not have a uniform

behavior over, it can be grouped into two groups almost in

phase opposition. There is a preferential distribution. The first


group is mainly obtained by direct rays and the second group

is caused by a fraction of those rays reflected from the first

wall that is after the receiver.

Fig.11 - Phases of the contributions that reach the point

IV. GESTURE RECOGNITION

For reading the gestures it makes use of the Doppler Effect

properties, which changes the frequency of the wave when the

supply system moves relative to the observer. Taking into

account the reflections from the human body and considering

the source (virtual), it is known that when it performs some

action will create a Doppler shift pattern at the receiver. Thus,

if the user performs a gesture towards the receiver it will

therefore generate a positive Doppler shift pattern, while if the

movement is in the opposite direction to the receiver it will

cause a negative pattern on the Doppler Effect. To clarify and

provide orders of magnitude, it may be made a simple

calculation. The wireless signals are electromagnetic waves,

and thus propagate at the speed of the light c [m/s], while the

human being performs movements at v [m/s], the maximum

Doppler Effect is (assuming hypothetically, 0 ):

∆ f ∝ 2 cos( )v

fc

(5)

Thus, a gesture of 1 m/s results in a Doppler shift of 33 Hz at 5

GHz Wi-Fi transmission. We can, already, draw some

conclusions:

The Doppler Effect depends on the angle of movement

performed in relation to the receiver. When the object

moves orthogonally to the receiver, it is seen that the

Doppler shift is zero. However, since, typically,

human gestures involve a set of points in space and

various directions, it can be concluded that the

Doppler Effect serve to classify and identify human

gestures;

Higher transmission frequencies originate a larger

Doppler Effects. Thus, the Wi-Fi transmission at 5

GHz is better than 2.4 GHz. However, this frequency

increase must be moderate, since the greater

frequency, the greater attenuation and may not be

feasible for the area of a house in which often we

have a case of NLOS;

The higher the movement speed, the higher the

Doppler Effect, while if the movement is

implemented slowly, the Doppler Effect will be more

difficult to detect [2].

Typically, the bandwidth in Wi-Fi is in the order of 20

MHz. So the challenge is in detecting the Doppler Effect of a

few Hertz in 20MHz Wi-Fi signal bandwith. This problem is

solved by transforming the received signal into a narrow band

signal of a few Hertz as needed. Given this, the receiver

inspects the frequency of the pulses, as such, it can detect the

Doppler Effect of this order. The system uses OFDM based

systems. OFDM divides the available bandwidth into sub-

bands, carrying out modulation of each of these subchannels.

For example, the Wi-Fi divides the 20 MHz channel in 64 sub-

channels of 312.5 KHz. In this case, the receiver does not

perform the FFT operation on each OFDM symbol, but

performs a large number of FFT on M consecutive OFDM

symbols. As a result of this operation, the bandwidth of each

OFDM subchannel is reduced by factor M. In Fig. 12, it is

noted that the receiver performs the FFT operation on the 2N

points over two consecutive OFDM symbols,

2 22

2 2

1 1

i kn i knN N

N Nn k k

k k N

X x e x e

A (6)

A : Noise from the OFDM decoding process.

Fig.12 - Result of the FFT operation on one and two symbols respectively [1]

Returning to the above equation, since the first N symbols are

identical to the last N symbols, meaning that k k Nx x , with

1k until N, we can rewrite the equation,

2 2 ( )

2 2

1 1

i kn i k N nN N

N Nn k k

k k

X x e x e

A (7)

It is obtained,


2

2

1

1i knN

i nNn k

k

X x e e

A (8)

The above equation have two possible outcomes, i.e. when it is

an even number, and when it is an odd number. So we can

rewriting the equation:

2

2

1

2i klN

Nl k

k

X x e

, 2 1X 0l (9)

The first graph of Fig. 12 shows the result of the FFT on an

OFDM symbol. The second graph of the Fig. 12 shows the

result of the FFT operation on two identical OFDM symbols.

This occurs because the same information is sent in two

modulated OFDM information symbols. Thus, the bandwidth

of each subchannel passes to half of it. More generally, when a

receiver performs FFT on an OFDM symbol points repeatedly

M times, the bandwidth of each channel is M times reduced. In

conclusion, it is possible to create several narrow-band signals

centered at the center frequency of each subchannel by

repeating an OFDM symbol and performing a large number of

FFT operations.

In a house or building, typically, there are several people

influencing the electromagnetic propagation at the same time.

In order to solve the interference coming from the non-target

users, it is used MIMO capacity, this in order to focus the

gesture in a particular direction, and therefore in a particular

user [14, 17]. If we consider the various reflections from the

various humans as various emitters, it can, then, be separated

using a MIMO receiver. It is important at this point to note

that the decoder estimates the MIMO channel between the

transmitting antenna and the receiver by sending a known

preamble of each emitter. The target user needs to make a

repetitive gesture that become in a personal preamble and as a

result. The receiver can take this preamble to estimate the

MIMO channel that maximizes the energy of the reflections

from that user. Once booked this channel, we can already do

normal gestures.

Until now, it was assumed that the transmitter is transmitting

continuously and the receiver uses the signal to calculate the

Doppler Effect. However, this simplification is not feasible.

Although the transmission remains possible in a not very dense

network, in a real scenario can significantly affect the

throughput of other devices [3]. This technology does the

following: linearly interpolates the received OFDM symbol to

fill the time intervals where transmission does not happen. The

interpolation is performed by subchannel, after the OFDM

symbols are transformed to the frequency domain. After the

interpolation, the receiver makes all OFDM symbols, both

original and interpolated, back to the time domain and forms a

continuous time trace synthesized [2].

A transmitter sends a cyclic prefix between every two

consecutive OFDM symbols so that there is no intersymbol

interference. The aforementioned prefix is generated by taking

the last k bits of each OFDM symbol, so that, this prefix can

be regarded as a discontinuity between OFDM symbols. As

with this sampling of symbols is not supposed to change the

default Doppler Effect, so in practice the prefix is not

processed, reducing also the computational complexity.

Now, it is explained in three steps how to extract the

Doppler Effect and hence it makes the correspondence with

human gestures. Firstly, there is the Doppler extraction, which

processes the Doppler effect coming from the narrowband

signals; secondly there is the segmentation, which identifies a

set of segments that identifies the gesture and finally there is

the classification that determines the most likely gesture from

the set of defined gestures.

Doppler Effect extraction

The receiver extracts information about the Doppler

Effect processing a time-frequency profile of a

narrowband signal. To this end, the receiver

calculates an FFT sequence over time, i.e., calculates

the FFT on the samples in the first half second. FFT

can have such a resolution of 2 Hz. The receiver then

moves 5ms (prefix) and calculates the FFT during

another half second. Repeat the process until the

time-frequency profile is completed.

Fig.13 - Time-frequency profile of a gesture [1]

The Fig. 13 shows the time-frequency profile,

expressed in dB, from a user moving the hand

towards the receiver. The profile portrays that the

energy is concentrated around the DC frequency,

which corresponds to the signal energy between the

transmitter and the receiver without human

intervention. However, when the user starts the

movement is observed an increase of the positive

Doppler shift (corresponding to the hand’s

acceleration), and then a lower positive Doppler shift

(corresponding to the hand’s deceleration).

Segmentation

To accomplish this step, the receiver inspects

the Doppler profiles from the gestures defined in

Fig. 14, shown in Fig. 15. The last figure

mentioned shows that the profiles are a combination

of positive and negative Doppler Effect. Thus, each

listed profile contains a set of segments having

positive or negative Doppler Effect. For example, the

profile (a) has only one segment with a positive

Doppler Effect, however, the profile (e) is constituted

by two segments, one is a positive Doppler Effect and

the other is a negative Doppler Effect. The receiver

inspects the mentioned properties of the profile and

then clusters the segments to be able to see a pattern

that is probably the gesture.


Fig.14 - Tabulated gestures used in recognition over the

electromagnetic waves

Fig. 15 - Standard Doppler effect of each previously tabulated

gesture [1]

Classification

As already mentioned, the Doppler profile is

considered as a set of segments. As it can be seen in

Fig. 15, the Doppler profile is unique for each gesture

defined. So, the receiver can sort the gesture-based

segments of positive or negative Doppler Effect.

There are three segments: segments with Doppler

Effect only positive, segments with Doppler Effect

only negative and segments with positive and

negative Doppler Effect - mixed. It can represent

these effects to "1", "-1", "2", respectively. Every

gesture set can now be represented by a unique

sequence of three numbers. Given this, the

classification of the gesture may be made by

comparing the sequence obtained with a set of

already determined and unique sequence of each

gesture.

The described, so far, assumes that the target user performs

a gesture from a fixed location and performs a repetitive

motion (preamble) when changing position on the

environment. However, one can eliminate the need to repeat

the pattern mentioned when the user moves in space. In a more

concrete way, some human movements such as walking or

running, create a very significant Doppler Effect. It is greater

than the defined movements. So in principle the receiver can

track the MIMO channel as the subject user moves, without

thereby any need to repeat the gesture new preamble, because

it ignore the Doppler Effect bigger that the defined. It creates a

maximum and a minimum Doppler energy, ignoring what is

outside the range.

One of the hazards of this technology is a stranger to the

home user may have control over the receiver and

consequently of the house. To address this issue, there may be

a secret Doppler profile, so that only after this we can use

technology normally.

The extraction of Doppler profiles in the presence of signals

from multipath is quite challenging. However using only

positive and negative Doppler Effect in gestures classification

greatly simplifies the problem. There are two issues to be

resolved in this section. First, due to multipath, the target user,

gesturing toward the receiver in a certain room, can create

positive or negative Doppler Effect in the receiver. Secondly,

reflective materials such as metallic surfaces, for example, can

produce both positive and negative Doppler effects.

Exemplifying the situation, the receiver may see a negative

Doppler Effect if the target user move your hand towards the

receiver if it is near a metal surface behind the user [2]. To

solve the problem of obtaining the positive or negative

Doppler Effect, the receiver inspects the preamble. More

specifically, since, before completion of the gesture required to

move the hand towards the receiver, creating repetitive

preamble, a receiver can perform calibration and get the

correct Doppler Effect. For example, if the receiver note a

negative Doppler Effect where it should be positive (known

preamble), then the gesture recognition receiver inverts the

sign of the Doppler Effect. In conclusion, it is possible to

recognize the gesture, regardless of user location.

V. CONCLUSION

This paper addresses the use of the existing wireless

technologies, such as Wi-Fi, to build a system that recognizes

human gestures. Since the wireless signals need not line of

sight and can pass through walls, this system allows the

recognition of gestures in a certain indoor area with the use of

a few devices.

REFERENCES

[1] Q. Pu, S. Gupta, S. Gollakota, and S. Patel, “Whole-home gesture

recognition using wireless signals,” MobiCom’13, September 30–

October 4, Miami, FL, USA, 2013;

[2] J. M. Garcia; Topa, A., "Gesture Recognition by Electromagnetic-Wave

Reflection", Proc Encuentro Ibérico de Electromagnetismo

Computacional - EIEC , Baeza, Spain, Vol. 1, pp. 1 - 2, May, 2015;

[3] John C. Stein, “Indoor Radio WLAN Performance Part II: Range

Performance in a Dense Office Environment”, Intersil Corporation,

2401 Palm Bay, Florida 32905;

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