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1 CHAPTER 1 INTRODUCTION 1.1 SIGNAL A signal [1] can be defined as a physical quantity that varies with time, temperature, pressure or with any independent variables such as speech signal or video signal. Any unwanted signal interfering with the main signal is termed as noise. So, noise is also a signal but unwanted. 1.2 SIGNAL PROCESSING Signal processing [2] is an area of systems engineering, electrical engineering and applied mathematics that deals with operations on or analysis of signals, or measurements of time varying or spatially varying physical quantities. The process of operation in which the characteristics of a signal (Amplitude, shape, phase, frequency, etc.) undergoes a change is known as signal processing. A signal carries information, and objective of signal processing is to extract useful information

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CHAPTER 1

INTRODUCTION

1.1 SIGNAL

A signal [1] can be defined as a physical quantity that varies with time,

temperature, pressure or with any independent variables such as speech signal or

video signal. Any unwanted signal interfering with the main signal is termed as

noise. So, noise is also a signal but unwanted.

1.2 SIGNAL PROCESSING

Signal processing [2] is an area of systems engineering, electrical

engineering and applied mathematics that deals with operations on or analysis of

signals, or measurements of time varying or spatially varying physical quantities.

The process of operation in which the characteristics of a signal (Amplitude,

shape, phase, frequency, etc.) undergoes a change is known as signal processing.

A signal carries information, and objective of signal processing is to extract

useful information carried by the signal. The method of information extraction

depends on the type of signal and the nature of the information being carried by

the signal.

Signals of interest can include sound, electromagnetic radiation, images,

and sensor data.

Some examples are biological data such as electrocardiograms, control

system signals, telecommunication transmission signals, and many others.

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The goals of signal processing can roughly be divided into the following

categories.

Signal acquisition and reconstruction, which involves

measuring a physical signal, storing it, and possibly later

rebuilding the original signal or an approximation thereof.

Quality improvement, such as noise reduction, image

enhancement, and echo cancellation.

Signal compression, including audio compression, image

compression, and video compression.

Feature extraction, such as image understanding and speech

recognition.

1.2.2 BLOCK DIAGRAM

1.2.3 CATEGORIES

There are four categories that fall under the roof of signal processing

Analog signal processing

Discrete signal processing

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Digital signal Processing

Non Linear signal processing

1.2.3.1 Analog Signal Processing

Analog signal processing is for signals that have not been digitized, as in

legacy radio, telephone, radar, and television systems.

This involves linear electronic circuits as well as non-linear ones. The

linear ones are, for instance, passive filters, active filters, additive mixers,

integrators and delay lines.

Non-linear circuits include compandors, multiplicators (frequency mixers

and voltage controlled amplifiers), voltage-controlled filters, voltage-

controlled oscillators and phase-locked loops.

1.2.3.2 Discrete Signal Processing

Discrete-time signal processing is for sampled signals, defined only at

discrete points in time, and as such are quantized in time, but not in

magnitude.

Analog discrete-time signal processing is a technology based on electronic

devices such as sample and hold circuits, analog time-division multiplexers,

analog delay lines and analog feedback shift registers.

This technology was a predecessor of digital signal processing and is still

used in advanced processing of gigahertz signals.

The concept of discrete-time signal processing also refers to a theoretical

discipline that establishes a mathematical basis for digital signal processing,

without taking quantization error into consideration.

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1.2.3.3 Digital Signal Processing

Digital signal processing is the processing of digitized discrete-time

sampled signals. Processing is done by general-purpose computers or by

digital circuits such as ASICs, field-programmable gate arrays or

specialized digital signal processors (DSP chips).

Typical arithmetical operations include fixed-point and floating-point, real-

valued and complex-valued, multiplication and addition. Other typical

operations supported by the hardware are circular buffers and look-up

tables.

Examples of algorithms are the Fast Fourier transforms (FFT), finite

impulse response (FIR) filter, Infinite impulse response (IIR) filter, and

adaptive filters such as the Wiener and Kalman filters.

1.2.3.4 Non Linear Signal Processing

Nonlinear signal processing involves the analysis and processing of signals

produced from nonlinear systems and can be in the time, frequency, or

Spatio-temporal domains.

Nonlinear systems can produce highly complex behaviors including

bifurcations, chaos, harmonics, and sub harmonics which cannot be

produced or analyzed using linear methods.

1.2.4 APPLICATION

Signal compression, including image compression, and video compression.

Feature extraction, such as image understanding and speech recognition.

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1.2.5 ADVANTAGES

Data collected at some location.

Once all of data is collected, it then has to be processed in order to have

usable information.

Quite frequently, data is collected and processed in two separate locations.

1.3 WIRELESS SENSOR NETWORK

Wireless sensor networks [2] are composed of hundreds of thousands of

tiny devices called nodes. A sensor node is often abbreviated as a node. A Sensor

is a device which senses the information and passes the same on to a mote.

Sensors are used to measure the changes to physical environment like pressure,

humidity, sound, vibration and changes to the health of person like blood pressure,

stress and heartbeat.

Wireless network refers to any type of computer network that uses wireless

infrastructure (usually, but not always radio waves) for network connections. It

needs network components such as Wi-Fi adapter, wireless router / modem, etc.,

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Fig 1. Overview of Wireless Sensor Network

A wireless sensor network (WSN) consists of spatially distributed

autonomous sensors to monitor physical or environmental conditions, such

as temperature, sound, pressure, etc.

The WSN is built of "nodes" – from a few to several hundreds or even

thousands, where each node is connected to one (or sometimes several)

sensors.

Each such sensor network node has typically several parts: a radio

transceiver with an internal antenna or connection to an external antenna, a

microcontroller, an electronic circuit for interfacing with the sensors and an

energy source, usually a battery or an embedded form of energy harvesting.

The topology of the WSNs can vary from a simple star network to an

advanced multi-hop wireless mesh network.

1.3.2 THE ARCHITECTURE OF WSN

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Fig 2. Wireless sensor network architecture

In a typical WSN the following network components [3],

a) Sensor nodes (Field devices): Each sensor network node has typically

several parts: a radio transceiver with an internal antenna or connection to

an external antenna, a microcontroller, an electronic circuit for interfacing

with the sensors and an energy source, usually a battery or an embedded

form of energy harvesting.

b) Gateway or Access points: A Gateway enables communication between

Host application and field devices.

c) Network manager: A Network Manager is responsible for configuration

of the network, scheduling communication between devices (i.e.,

configuring super frames), management of the routing tables and

monitoring and reporting the health of the network.

d) Security manager: The Security Manager is responsible for the

generation, storage, and management of keys.

The base stations are one or more distinguished components of the WSN with

much more computational, energy and communication resources. They act as a

gateway between sensor nodes and the end user as they typically forward data

from the WSN on to a server. Other special components in routing based networks

are routers, designed to compute, calculate and distribute the routing tables. Many

techniques are used to connect to the outside world including mobile phone

networks, satellite phones, radio modems, high power Wi-Fi links etc.

1.3.3 ADVANTAGES

It avoids a lot of wiring.

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It can accommodate new devices at any time.

It's flexible to go through physical partitions.

It can be accessed through a centralized monitor.

1.3.4 REAL-TIME EXAMPLES

Area monitoring

Environmental/Earth monitoring

Air quality monitoring

Air pollution monitoring

Forest fire detection

Land slide detection

Water quality monitoring

Natural disaster prevention

Machine health monitoring

Agriculture

WIRELESS SENSOR NETWORKS FOR RESPIRATION MONITORING:

It is well recognized that sleep not only affects the productivity or physical

vitality of a person, but also is related to many diseases including diabetes, obesity

and depression[4]. Some sleep disorders such as the sleep apnea syndrome

(OSAS), can even cause stroke and heart failure. On the other hand, although

about one third of adults have suffered from poor sleep quality, most of them may

not be able to evaluate their own sleep very well [5]. Therefore, a system which is

able to provide quantitative sleep information is highly desirable.

Contactless Respiration Monitoring System, the first sleep monitoring

system based on WiFi signals. It adopts off-the-shelf WiFi devices to continuously

collect the fine-grained wireless channel state information (CSI) around a person.

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From the CSI, it extracts rhythmic patterns associated with respiration and abrupt

changes due to the body movement. Compared to existing sleep monitoring

systems that usually require special devices attached to human body (i.e. probes,

head belt, and wrist band), this system is completely contactless. In addition,

different from many vision-based sleep monitoring systems, it is robust to low-

light environments and does not raise privacy concerns. It can reliably track a

person’s respiration and sleeping postures and rollovers in different conditions.

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CHAPTER 2

ISSUES AND CHALLENGES OF WIRELESS SENSOR NETWORKS

2.1 INTRODUCTION

Sensors can be perceived to be a connecting link between the physical and

the digital worlds. Sensors gather data from the real world which could be about a

physical object or could be on the occurrence of a certain event and transform

them into digital signals which can be further processed, stored and further

transmitted to a computing system.

A sensor network [28] is a group of specialized transducers, which are

essentially sensor nodes, with a communications infrastructure intended to monitor

and record conditions at diverse locations.

The development of wireless sensor networks was motivated by military

applications such as battlefield surveillance while in today’s world, sensors are

being predominantly used to monitor parameters like temperature, humidity,

pressure, light intensity, wind direction etc. Potential applications of sensor

networks include [28]:

Industrial automation

Video surveillance

Traffic monitoring

Medical device monitoring

Monitoring of weather conditions

Air traffic control

Robot control.

The sensor node is essentially a transducer that converts the energy in the

physical world into electrical energy. [29]The resulting electrical signals in order

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to be processed are passed through a signal conditioning stage where they could be

amplified or attenuated. Further the signals are passed through filters to reduce

unwanted noise signals whose frequencies lie above or below the required range.

The signal obtained which is still in the analog form needs to be converted to

digital signals using the analog-to-digital converter. The resulting digital signal is

ready for further processing, storing, or visualization.

Numerous issues and challenges have been faced and several researches

have been carried out to study the functioning of sensor networks and the various

issues faced while setting up a sensor network.

2.2 Issues in WSN

The major issues [7] that affect the design and performance of a wireless

sensor network are as follows:

1) Hardware

2) Wireless Radio Communication Characteristics

3) Medium Access Schemes

4) Deployment

5) Localization

6) Synchronization

7) Calibration

8) Network Layer

9) Transport Layer

10) Data Aggregation and Data Dissemination

11) Database Centric and Querying

12) Architecture

13) Programming Models for Sensor Networks

14) Middleware

15) Quality of Service

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16) Security

HARDWARE

The hardware design issues of sensor nodes are quite different from other

applications and they are [8]:

1) Radio Range of nodes should be high (1-5 kilometers). Radio range is critical

for ensuring network connectivity and data collection in a network as the

environment being monitored may not have an installed infrastructure for

communication. In many networks the nodes may not establish connection for

many days or may go out of range after establishing connection.

2) Use of Memory Chips like flash memory is recommended for sensor networks

as they are non-volatile, inexpensive and volatile.

3) Energy/Power Consumption of the sensing device should be minimized and

sensor nodes should be energy efficient since their limited energy resource

determines their lifetime. To conserve power the node should shut off the radio

power supply when not in use. Battery type is important since it can affect the

design of sensor nodes.

Battery Protection Circuit to avoid overcharge or discharge problem can be added

to the sensor nodes.

4) Sensor Networks consists of hundreds of thousands of nodes. It is preferred

only if the node is cheap.

WIRELESS RADIO COMMUNICATION CHARACTERISTICS

Performance of WSN depends on quality of wireless communication. But

wireless communication in sensor networks is known for its unpredictable nature.

Main design issues for communication in WSNs are:

1) Low power consumption in sensor networks is needed to enable long operating

lifetime by facilitating low duty cycle operation, local signal processing.

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2) Distributed Sensing effectively acts against various environmental obstacles and

care should be taken that the signal strength, consequently the effective radio

range is not reduced by various factors like reflection, scattering and dispersions.

3) Multi-hop networking may be adapted among sensor nodes to reduce

communication link range and also density of sensor nodes should be high.

4) Long range communication is typically point to point and requires high

transmission power, with the danger of being eavesdropped. So we should

consider short range transmission to minimize the possibility of being

eavesdropped.

5) Communication systems should include error control subsystems to detect

errors and to correct them.

DEPLOYMENT

Deployment means setting up an operational sensor network in a real world

environment [9]. Deployment of sensor network is a labor intensive and

cumbersome activity as we do not have influence over the quality of wireless

communication and also the real world puts strains on sensor nodes by interfering

during communications. Sensor nodes can be deployed either by placing one after

another in a sensor field or by dropping it from a plane. Various deployment issues

which need to be taken care are [10, 11]:

1) When sensor nodes are deployed in real world, Node death due to energy

depletion either caused by normal battery discharge or due to short circuits is a

common problem which may lead to wrong sensor readings. Also sink nodes acts

as gateways and they store and forward the data collected. Hence, problems

affecting sink nodes should be detected to minimize data loss.

2) Deployment of sensor networks results in network congestion due to many

concurrent transmission attempts made by several sensor nodes. Concurrent

transmission attempts occur due to inappropriate design of the MAC layer or by

repeated network floods. Another issue is the physical length of a link. Two nodes

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may be very close to each other but still they may not be able to communicate due

to physical interference in the real world while nodes which are far away may

communicate with each other.

3) Low data yield is another common problem in real world deployment of sensor

nodes. Low data yield means a network delivers insufficient amount of

information.

4) Self Configuration of sensor networks without human intervention is needed

due to random deployment of sensor nodes.

A framework is proposed in [11] considering the above deployment issues.

POWER is a software environment for planning and deploying wireless sensor

network applications into actual environment.

ARCHITECTURE

Architecture can be considered as a set of rules and regulation for

implementing some functionalities along with a set of interfaces, functional

components, protocols and physical hardware. Software architecture is needed to

bridge the gap between raw hardware capabilities and a complete system.

The key issues that must be addressed by the sensor architecture are [16, 17, 18]:

1) Several operations like continuous monitoring of the channel, encoding of data

and transferring of bits to the radio need to be performed in parallel. Also sensor

events and data calculations must continue to proceed while communication is in

progress.

2) A durable and scalable architecture would allow dynamic changes to be made

for the topology with minimum update messages being transmitted.

3) The system must be flexible to meet the wide range of target application

scenarios since the wireless sensor networks to not have a fixed set of

communication protocols that they must adhere to.

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4) The architecture must provide precise control over radio transmission timing.

This requirement is driven by the need for ultra-low power communication for

data collection application scenarios.

5) The architecture must decouple the data path speed and the radio transmission

rate because direct coupling between processing speed and communication bit

rates can lead to sub-optimal energy performance.

DATABASE CENTRIC AND QUERYING

Wireless sensor networks have the potential to span and monitor a large

geographical area producing massive amount of data. So sensor networks should

be able to accept the queries for data and respond with the results.

The data flow in a sensor database is very different from the data flow of the

traditional database due to the following design issues and requirements of a

sensor network [12, 13, 14, 15]:

1) The nodes are volatile since the nodes may get depleted and links between

various nodes may go down at any point of time but data collection should be

interrupted as little as possible.

2) Sensor data is exposed more errors than in a traditional database due to

interference of signals and device noise.

3) Sensor networks produce data continuously in real time and on a large scale

from the sensed phenomenon resulting in need of updating the data frequently;

whereas a traditional database is mostly of static and centralized in nature.

4) Limited storage and scarce of energy is another important constraint that needs

to be taken care of in a sensor network database but a traditional database usually

consists of plenty of resources and disk space is not an issue.

5) The low level communication primitives in the sensor networks are designed in

terms of named data rather than the node identifiers which are used in the

traditional networks.

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CALIBRATION

Calibration is the process of adjusting the raw sensor readings obtained

from the sensors into corrected values by comparing it with some standard values.

Manual calibration of sensors in a sensor network is a time consuming and

difficult task due to failure of sensor nodes and random noise which makes manual

calibration of sensors too expensive.

Various Calibration issues in sensor networks are [19,20,21]:

1) A sensor network consists of large number of sensors typically with no

calibration interface.

2) Access to individual sensors in the field can be limited.

3) Reference values might not be readily available.

4) Different applications require different calibration.

5) Requires calibration in a complex dynamic environment with many observables

like aging, decaying, damage etc.

6) Other objectives of calibration include accuracy, resiliency against random

errors, ability to be applied in various scenarios and to address a variety of error

models.

Research includes designing various calibration techniques involving the

various issues which we have discussed previously.

LOCALIZATION

Sensor localization is a fundamental and crucial issue for network

management and operation. In many of the real world scenarios, the sensors are

deployed without knowing their positions in advance and also there is no

supporting infrastructure available to locate and manage them once they are

deployed [22, 24, 25].

Determining the physical location of the sensors after they have been deployed is

known as the problem of localization. Location discovery or localization algorithm

for a sensor network should satisfy the following requirements [23]:

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1) The localization algorithm should be distributed since a centralized approach

requires high computation at selective nodes to estimate the position of nodes in

the whole environment. This increases signaling bandwidth and also puts extra

load on nodes close to center node.

2) Knowledge of the node location can be used to implement energy efficient

message routing protocols in sensor networks.

3) Localization algorithms should be robust enough to localize the failures and

loss of nodes. It should be tolerant to error in physical measurements.

4) It is shown in [26] that the precision of the localization increases with the

number of beacons. A beacon is a node which is aware of its location. But the

main problem with increased beacons is that they are more expensive than other

sensor nodes and once the unknown stationary nodes have been localized using

beacon nodes then the beacons become useless.

5) Techniques that depend on measuring the ranging information from signal

strength and time of arrival require specialized hardware that is typically not

available on sensor nodes.

6) Localization algorithm should be accurate, scalable and support mobility of

nodes.

2.3 Challenges in WSN

Wireless sensor networks pose certain design challenges [27] due to the

following reasons,

1) The sensor nodes are randomly deployed and hence do not fit into any regular

topology. Once deployed, they usually do not require human intervention. This

implies that setup and maintenance need to be autonomous.

2) Sensor networks are infrastructure-less. Therefore, all routing and maintenance

algorithms need to be distributed.

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3) An important bottleneck in the operation of sensor nodes is the available

energy. Sensors usually rely on their battery for power, which in many cases

should be considered as a major constraint while designing protocols. The wireless

sensor node, being a micro-electronic device, can only be equipped with a limited

power source. In most application scenarios, replenishment of power resources

might become impossible. The sensor node lifetime, therefore, shows a strong

dependence on battery lifetime.

4) Hardware design for sensor nodes should also consider energy efficiency as a

primary requirement. The micro-controller, operating system, and application

software should be designed to conserve power.

5) Sensor nodes should be able to synchronize with each other in a completely

distributed manner, so that TDMA schedules can be imposed and temporal

ordering of detected events can be performed without ambiguity.

6) A sensor network should also be capable of adapting to changing connectivity

due to the failure of nodes, or new nodes powering up. The routing protocols

should also be able to dynamically include or avoid sensor nodes in their paths.

7) Real-time communication over sensor networks must be supported through

provision of guarantees on maximum delay, minimum bandwidth, or other QoS

parameters.

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CHAPTER 3

LITERATURE SURVEY

3.1.1 T. Hao, G. Xing, and G. Zhou, “isleep: unobtrusive sleep quality

monitoring using smartphones,” in The 11th ACM Sensys, p. 4, 2013.

The quality of sleep is an important factor in maintaining a healthy life

style. iSleep uses the built-in microphone of the smart-phone to detect the events

that are closely related to sleep quality, including body movement, couch and

snore. It adopts a lightweight decision-tree-based algorithm to classify various

events based on carefully selected acoustic features. The model of the features

such as root mean square (rms), and variance will be extracted from the frames

that potentially contain events of interest. Based on the detected events, iSleep

infers quantitative measures of sleep quality based on Actigraphy and Pittsburgh

Sleep Quality Index (PSQI). iSleep is very easy to use and truly unobtrusive: the

user just needs to start iSleep app and place the phone somewhere close to the bed.

3.1.2 Ching-Wei Wang∗, Andrew Hunter, Neil Gravill, and Simon

Matusiewicz “Unconstrained Video Monitoring of Breathing Behavior,”

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO.

2, FEBRUARY 2014

It is based on camera-vision. This paper presents a new real-time automated

infrared video monitoring technique for detection of breathing anomalies. The

Persistent Luminous Impression Model (PLIM) is designed to accumulate errors to

enhance the breathing signals and to differentiate between the breathing activity

and the body movement. A simple measure of activity level can be extracted from

the PLIM, and used to identify motion events. It uses a two-state algorithm, which

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switches between the normal breathing state and the motion event state. One

limitation of the presented method is the number of heuristically determined

parameters of the algorithm.

3.1.3 Ming-Chun Huang, Wenyao Xu, Jason Liu, Lauren Samy, Amir Vajid,

Nabil Alshurafa, and Majid Sarr afzadeh,”Inconspicuous on-Bed Respiratory

Rate Monitoring,” in ACM, 2013

Respiratory rate is an important indicator of a person’s overall health. It is

important for many clinical uses and is often monitoring during clinical

evaluations as one of the four standard vital signs along with temperature, heart

rate and blood pressure.

E-textile bedsheet works as a transducer which converts pressure human

body to the bedsheet into voltage. Via analyzing the voltage matrix (pressure

matrix), human body locations can be identified. When a subject sleeps on top of

the bedsheet, its chest area should be detected. Based on analyzing the features

(sum of pressure, standard deviation, and singular values), respiratory signals can

be extracted.

3.1.4 Ruth, S. Elliot, C. Ke-Yu, G. Mayank, G. Sidhant, and S. N.

Pate,“Wibreathe: Estimating respiration rate using wireless signals in natural

settings in the home,” in IEEE International Conference on Pervasive

Computing and Communications (PERCOM), 2015.

WiBreathe, a wireless, high fidelity and non-invasive breathing monitor

that leverages wireless signals at 2.4 GHz to estimate an individual’s respiration

rate. It can detect a person’s respiration rate from anywhere in a house without any

instrumentation on the body. The system only requires a pair of transmitter and

receivers that can be placed anywhere in the house.

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Breathing alters the magnitude of reflected signal inducing an amplitude

modulation on the transmitted wireless signal. The adaptive algorithm takes the

results adaptively from five different sub-algorithms, making the system robust in

a dynamically changing environment. This approach combines the estimates from

an ensemble of those different techniques and adapts dynamically over a different

scenarios to enable continuous respiration rate monitoring over long periods of

time.

3.1.5 F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller,“Smart homes

that monitor breathing and heart rate,” in CHI15, pp. 837–846, ACM, 2015.

Vital-Radio, a wireless sensing technology that monitors breathing and

heart rate without body contact. Vital-Radio uses a radar technique called FMCW

(Frequency Modulated Carrier Wave) to separate the reflections arriving from

objects into different buckets depending on the distance between these objects and

the device. Natural environments have a large number of reflectors.

Vital-Radio works by using wireless signals to monitor the minute

movements due to inhaling, exhaling, and heartbeats. Specifically, it transmits a

low-power wireless signal and measures the time it takes for the signal to reflect

back to the device. The reflection time depends on the distance of the reflector to

the device, and changes as the reflector moves. Vital-Radio measures these

changes and analyzes them to extract breathing and heartbeats.

3.1.6 N. Patwari, L. Brewer, and et al., “Breathfinding: A wireless network

that monitors and locates breathing in a home,” in IEEE Journal of Selected

Topics in Signal Processing, pp. 30–42, 2014.

This paper explores using RSS measurements on the links between

commercial wireless devices to locate where a breathing person is located and to

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estimate their breathing rate, in a home, while the person is sitting, lying down,

standing, or sleeping.

The person’s breathing rate can be estimated using the RSS measurements.

A change-detection method is used to identify (BREAKPOINTS) times at which

this motion occurs, negate it, and then accurately estimate breathing rate even

during short periods of motion. Variance-based radio tomographic imaging

(VRTI) uses the variance of RSS on links in a wireless network to identify the

location of a moving person in a building.

3.1.7 O. J. Kaltiokallio and et al., “Non-invasive respiration rate monitoring

using a single cots tx-rx pair,” in IPSN 2014, 2014.

This paper addresses respiration rate monitoring using low-cost commercial

off-the-shelf transceivers. It is composed of a single TX-RX pair, which

significantly improves applicability of existing signal strength based breathing

monitoring systems. The work in this paper addresses two major problems that are

faced when conducting breathing monitoring using RSS measurements of a single

TX-RX pair. First, the breathing signal is not observable in the RSS

measurements. This problem can be addressed by increasing the signal-to-noise

ratio (SNR) and utilizing frequency diversity to enrich the information content of

noisy RSS measurements. The Second problem to be addressed is, other

movements of the person (a posture change) which we refer to as motion

interference, dominate the frequency content of the RSS measurement. A hidden

Markov model (HMM) is developed to identify motion interference.

3.1.8 H. Abdelnasser, K. A. Harras, and M. Youssef, “Ubibreathe: A

ubiquitous non-invasive wifi-based breathing estimator,” 2015.

UbiBreathe has four components: The RSS is first processed by the

Breathing Signal Extractor module that filters the noise from the input signal and

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extracts the breathing signal. The extracted breathing signal is then passed to the

Robust Breathing Rate Extractor module that filters outliers and provides a more

stable signal reading. The Apnea Detector module applies further de-noising

techniques (Wavelet de-noising) to the breathing signal and accordingly checks for

the absence of the breathing pattern. Finally, the Real-time Visualizer module

combines the output of the different modules in a user friendly visual output and

raises alarms when an apnea is detected.

3.1.9 Jian Liu, Yan Wang, Yingying Chen, JieYang∗, Xu Chen, Jerry Cheng,

“Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi,” in ACM,

2015

The data is processed to filter out the CSI measurements that contain sleep

events (e..g, going to bed and turn over) or large environmental changes such as

people walking by via Coarse Sleep Event Detection and Filtering (Hampel filter).

After coarse sleep event detection and data filtering, based on the different

frequency information embedded inside the CSI measurements, the input is fed

into Breathing Rate Estimation. This system first performs Data Calibration and

Subcarrier Selection to preprocess the data and select only the subcarriers sensitive

to minute human body movements. Then a method is developed, Breathing Cycle

Identification (Peak finding algorithm) to estimate the breathing rate for single

person.

3.1.10 Xuefeng Liu, Jiannong Cao, Shaojie Tang, Jiaqi Wen and Peng Guo ,

“Contactless Respiration Monitoring via Off-the-shelf WiFi Devices,” 2015

This system extracts fine-grained wireless CSI around a person from off-

the-shelf WiFi device to detect the minute movements and provide accurate

breathing and heart rates estimation concurrently. The raw CSI measurements are

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passed through the preprocessing unit in which the noise and outliers are removed.

Afterwards, the CSIs are analyzed to decide whether there are some changes occur

in the CSI data. If the answer is NO, breathing rate will be estimated. Otherwise,

the data are further analyzed to see whether the change of CSIs is caused by the

change of sleeping postures, or by the sleep apnea.

3.2 Summary Of Literature Review

Author Title Year & Pub

Findings Limitations

Tian

Hao,

Guoliang

Xing,

Gang

Zhou

iSleep:

Unobtrusive

Sleep Quality

Monitoring

using

Smartphones

2013

ACM

This paper presents iSleep, a

practical system to monitor

an individual's sleep quality

using off-the-shelf smart-

phone. iSleep uses the built-

in microphone of the

smartphone to detect the

events that are closely related

to sleep quality, including

body movement, couch and

snore, and infers quantitative

measures of sleep quality.

iSleep adopts a lightweight

decision-tree-based

algorithm to classify various

events based on carefully

selected acoustic features,

and tracks the dynamic

ambient noise characteristics

to improve the robustness of

The high-rate

microphone sampling

is a major source of

energy consumption.

The environmental

noise can affect the

accuracy of detecting

sleep-related events.

Some of the metrics

used to calculate the

score of Sleep

Disturbance cannot be

measured by iSleep.

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classification.

Author Title Year & Pub

Findings Limitations

Ching-

Wei

Wang∗,

Andrew

Hunter,

Neil

Gravill,

and

Simon

Matusie

wicz

Unconstrained

Video

Monitoring of

Breathing

Behavior.

2014

IEEE

This paper introduced a

novel motion model to detect

subtle, cyclical breathing

signals from video, a new 3D

unsupervised self-adaptive

breathing template to learn

individuals’ normal

breathing patterns online,

and a robust action

classification method to

recognize abnormal

breathing activities and limb

movements. This technique

avoids imposing positional

constraints on the patient,

allowing patients to sleep on

their back or side, with or

without facing the camera,

fully or partially occluded by

the bed clothes. Moreover,

shallow and abdominal

breathing patterns do not

adversely affect the

performance of the method,

The breathing motion

is barely perceptible

(due to obscuration by

bed clothing and the

subtlety of the

breathing

movements), and

being cyclical the

movements are prone

to self-occlusion.

Consequently, the

standard motion

detection and activity

recognition methods

do not function well.

Can be negatively

affected by typical

low-light sleeping

environment and also

raise privacy concerns

to the users.

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and it is insensitive to

environmental settings such

as infrared lighting levels

and camera view angles.

Author Title Year & Pub

Findings Limitations

Ching-

Wei

Wang∗,

Andrew

Hunter,

Neil

Gravill,

and

Simon

Matusie

wicz

Inconspicuous

On-Bed

Respiratory

Rate

Monitoring

2014

IEE

In this paper, an e-textile bed

sheet with a dense pressure-

sensitive array system is

introduced to measure

human respiratory rate under

any home/clinical

environment. This system

continuously detects a

patient’s pressure

distribution on the bed.

Respiratory rate and sleep

positions could be extracted

via analyzing time-stamped

pressure map sequences. The

e-textile bed sheet was

selected to be a normal

human-sensor interface

because it is close to a

regular fabric-made bed

sheet in feel and comfort.

This system can still

make inaccurate

measurements if the

subject under test is

moving or is in a side

position.

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Author Title Year & Pub

Findings Limitations

R.Ravich

andran,

S. Elliot,

C. Ke-

Yu, G.

Mayank,

G.

Sidhant,

and

S.N.Pate

WiBreath:

Estimating

Respiration

Rate Using

Wireless

Signals in

Natural

Settings in the

Home.

2015

IEEE

In this paper, WiBreathe is

presented, a whole-home

respiration rate sensing

system that reliably estimates

respiration rate in a user’s

changing environment. This

system leverages wireless

narrowband signals to

monitor the breathing of an

individual anywhere in a

home, even when the person

is behind the walls.

Specifically, the adaptive

algorithm clusters and

chooses between multiple

respiratory rate extraction

algorithms, and adapts to a

dynamically changing

environment.

It Never tracks

abnormal breathing

under different sleep

positions and change

of sleep positions.

Large body

movements cause

severe disruptions in

the signal strength.

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Author Title Year & Pub

Findings Limitations

F. Adib,

H. Mao,

Z.

Kabelac,

D. Katabi

& R. C.

Miller

Smart Homes

that Monitor

Breathing and

Heart Rate

2015

ACM

Vital-Radio uses a radar

technique called FMCW to

separate the reflections

arriving from objects into

different buckets depending

on the distance between

these objects and the device.

Natural environments have a

large number of reflectors.

To address this issue, Vital-

Radio’s operation consists of

three steps:

Step1: Isolate Reflections

from Different Users and

Eliminate Reflections off

Furniture and Walls.

Step2: Identifying

Reflections Involving

Breathing and Heart Rate.

Step3: Extracting Breathing

and Heart Rate.

Non-human motion:

identify the presence

of a pet and output its

breathing and heart

rate assuming it is

another user in the

environment.

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Author Title Year & Pub

Findings Limitations

N.

Patwari,

L.

Brewer,

and et al.,

Breathfinding

: A Wireless

Network That

Monitors and

Locates

Breathing in a

Home

2014

IEEE

This paper explores using

standard wireless devices

which measure only RSS to

monitor and localize the

breathing of a person in a

building, without any prior

calibration. The core of the

idea of RSS-based breathing

localization is that a link’s

RSS measurements are

sensitive to breathing when

the breathing person is near

the link line and there is no

other motion occurring

nearby. From many links’

RSS data, breathing rate is

estimated and localize a

breathing but otherwise

motionless person.

It doesn’t track

multiple breathing

people in the same

deployment area.

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Author Title Year & Pub

Findings Limitations

N.

Patwari,

L.

Brewer,

and et al.,

Non-invasive

Respiration

Rate

Monitoring

Using a

Single COTS

TX-RX Pair.

IPSN

2014

The work in this paper

addresses two major

problems that are faced when

conducting breathing

monitoring using RSS

measurements of a single

TX-RX pair. First, the

breathing signal is not

observable in the RSS

measurements. This problem

can be addressed by

increasing the signal-to-noise

ratio (SNR) and utilizing

frequency diversity to enrich

the information content of

noisy RSS measurements.

The Second problem to be

addressed is, other

movements of the person (a

posture change) which we

refer to as motion

It is expected that for

small children

breathing monitoring

is considerably harder.

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interference, dominate the

frequency content of the RSS

measurement. A hidden

Markov model (HMM) is

developed to identify motion

interference.

Author Title Year & Pub

Findings Limitations

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H.

Abdelnas

ser, K.A.

Harras,

and M.

Youssef

UbiBreathe:

A Ubiquitous

non-Invasive

WiFi-based

Breathing

Estimator

ACM

2015

UbiBreathe is a software-

only solution that can work

with any WiFi-enabled

device without the need of

any special hardware, can

monitor multiple persons in

parallel, detect breathing

anomalies, and display the

full breathing signal in real-

time. The basic idea

UbiBreathe leverages is that

the chest/lungs are large

organs, and the inhaling and

exhaling motion of a

breathing person causes a

dominant periodic

component in the received

WiFi signal at a receiver

positioned on the user’s

chest. This “modulated”

WiFi signal due to the

breathing process can be

analyzed to extract different

useful information about the

person’s breathing pattern.

It doesn’t work when

the sleepers are under

the change of sleep

positions.

Author Title Year Findings

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& Pub

Jian Liu,

Yan

Wang,

Yingying

Chen,

JieYan

g∗,Xu

Chen,

Jerry

Cheng

Tracking

Vital Signs

During Sleep

Leveraging

Off-the-shelf

WiFi

ACM

2015

In this paper, a system is proposed to track the vital

signs of both breathing rate and heart rate during

sleep by using off-the-shelf WiFi without any

wearable or dedicated devices. This system re-uses

existing WiFi network and exploits the fine-grained

channel information to capture the minute

movements caused by breathing and heartbeats.

Our system thus has the potential to be widely

deployed and perform continuous long-term

monitoring. The developed algorithm (peak finding

algorithm) makes use of the channel information in

both time and frequency domain to estimate

breathing and heart rates, and it works well when

either individual or two persons are in bed.

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Author Title Year & Pub

Findings Limitations

X. Liu, J.

Cao, S.

Tang,

Jiaqi

Wen and

Peng Guo

Contactless

Respiration

Monitoring

via Off-the-

shelf WiFi

Devices

2015

IEEE

This system relies on the

COTS WiFi devices, is able

to track abnormal breathing

accurately, and works well

when the sleepers are at

different sleeping positions

or even change of sleeping

positions. This system

continuously collects the

fine-grained wireless channel

state information (CSI)

around a person. From the

CSI, the rhythmic patterns

associated with respiration

and abrupt changes due to

the body movement are

identified.

It doesn’t identify

activities like getting-

ups or hand

movements using CSI.

It doesn’t track the

respiration of a person

in the presence of

these activities.

It doesn’t monitor

multiple persons

simultaneously.

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3.2 Inference from Literature Survey

According to the sensors adopted, existing respiration monitoring systems

which are able to track human respiration can be largely divided into three

categories:

systems based on pressure sensor arrays,

systems using camera visions, and

systems based on RF signals.

Compared with the existing works that rely on RSS measurements, this

system can track un-normal breathing (e.g. sleep apnea) and can also provide

breathing information when the person is under different sleeping positions by

applying CSI. The existing system detects the presence of a person or identifies

human gestures and also monitors the breath. However, these systems rely on

special hardware devices attached to a human body.

In the proposed system, under different sleeping positions fine-grained

respiration information of a person can be extracted with off-the-shelf WiFi

devices by introducing a breath monitoring system which is based on WiFi

signals.

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CHAPTER 4

DISCUSSION

4.1 Methodologies

4.1.1 Channel State Information (CSI)

Channel state information or channel status information (CSI) is

information that estimates the channel by representing the channel properties of a

communication link. More specifically, CSI describes how a signal propagates

from the transmitter(s) to the receiver(s) and reveals the combined effect of, for

instance, scattering, fading, and power decay with distance.

CSI needs to be estimated at the receiver and usually quantized and fed

back to the transmitter. Therefore, the transmitter and receiver can have different

CSI. The CSI at the transmitter and the CSI at the receiver are sometimes referred

to as CSIT and CSIR, respectively.

According to the definition of CSI, only Orthogonal Frequency Division

Multiplexing (OFDM)-based WLAN systems can demonstrate the frequency

diversity in CSI since they use multiple subcarriers for data transmission.

The CSI information represents signal strength and phase information for

OFDM subcarriers. The received signal can be modeled as,

y = H · x + n

where y is the received signal, x is the transmitted signal, n is the channel

noise and H is the CSI which is a complex number matrix that indicates the

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channel frequency response of each individual subcarrier for every spatial stream.

This way, CSI for all subcarriers and all spatial streams is a m × n × w matrix,

where m is the number of transmitter antennas, n is the number of receiver

antennas and w is the number of subcarriers. Such a fine-grained matrix can

accurately capture the temporal and spectral conditions of the channel and changes

caused by small-scale multipath effects.

Thus, CSI of all subcarriers can be estimated according to the received signal

equation as H= yx , which is a fine-grained value from the PHY layer that

describes the channel gain from TX baseband to RX baseband.

CSI of a single subcarrier is mathematically represented as,

ℎ = ∣ℎ∣e j sin {∠h}, where ∣ℎ∣ is the amplitude and ∠ℎ is the phrase of each subcarrier.

4.2 Comparing the wireless features

• Channel response vs Received energy

The CSI is the channel response reflecting the state of wireless channels

between transmitter and receiver, while the RSS is only the received energy power

at the receiver side. The scattering and reflecting effects caused by minute

movements of breathing will directly affect the channel state rather than the

received energy.

• Higher Frequency Granularity

The scattering and reflecting effects caused by breathing can have different

effect on different sub-carriers. Analyzing sub-carriers of CSI thus provides higher

opportunity to capture the minute movements than using the RSS which can be

regarded as the averaged power over the whole channel bandwidth.

• Higher Amplitude Granularity

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The RSS is an integer with low granularity (±1dB). The minute movement of

breathing generally causes little changes on the RSS data. On the other hand, the

CSI can provide much higher resolution in amplitude.

4.3 Data

4.3.1 Topography Database

The topographic database is used as source material for map products. In

addition, the database is applicable to be used, for instance, in various GPS-based

applications for positioning, route search, data collection and maintenance.

The topographic database is also used in planning of buildings and land use

and in different research and monitoring functions associated with environment.

4.4 Performance Metrics

Quantization of CSI

Here, it is well known that different frequency domain components of a

signal have an uncorrelated fading, so it is called as frequency selective fading.

This means 30 subcarrier groups are occurred different fading effect. If we use

only the maximum amplitude of subcarrier groups, distance estimation errors will

be taken place irregularly. In our study, we average the 30 subcarrier groups to

reduce the error caused by the frequency selective fading, that is, CSI quantization.

We give a different weight to each other sub-carrier because signal attenuation

effect is frequency-related. For CSI value of the high frequency group is much

weighted that that of the low one.

The quantization of CSI is calculated as an equation above,

CSI eff = 1K ∑

k −1

k f k

f 0 H k

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where f 0 is the central frequency, f k and |H k | are the frequency and

amplitude of the f th subcarrier of CSI.

Scaling of CSI

The measured CSI from a commodity NIC is provided in a normalized

form to be directly utilized for channel control. This means there is no relationship

between the distance and the measured CSI value. So, the measured one should be

compensated by Automatic Gain Control (AGC) which is also obtained from the

NIC. AGC is the gain to maintain a constant range of the amplitude of the signal

input to the A-D converter. Usually formula to obtain the RSSI is provided with

the below equation.

Prssi = RSSI – 44 – agc

where Prssi is the received signal strength in dBm, RSSI is received signal

strength which is relative to an internal reference in Db and arc is an indicator for

the compensation.

The quantization CSI and arc are applied to scale the measured CSI

in order to reflect the received signal strength as below equation; it is a conversion

from the normalized one to the radio propagation dependent one.

wherePcsi is the CSI strength in dBm and arc is an indicator for the

compensation.

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Path loss propagation model

Radio frequency delivery is modeled with a free-space loss which is the

signal strength attenuation in proportion to the propagation distance. The path loss

propagation model is a formula that shows how the signal strength is decreased on

increasing the distance. This formula provides a basement to estimate the distance

with RF properties as RSSI or CSI. The most common model is represented as

follow.

where P0 is the signal strength at distance of unit distance d0, n is the path

loss fading exponent, and d is the measured distance from the transmitter. Path

loss fading exponent is a quantification of environmental factors such as RF gain,

antenna gain, refraction, shadowing and propagation loss that affect the

propagation of electromagnetic wave. It is known that an environment having

complicated structures; such as office, has a value of about 4 or more. On the other

hand, it has a value of about 2 in simple environment; such as corridor.

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CHAPTER 5

CONCLUSION

5.1 CONCLUSION

By using off-the-shelf WiFi devices deployed in bedrooms, some fine-

grained information associated with sleep including the person’s respiration,

sleeping postures and activities like rollovers, can be continuously tracked using

the channel state information. From the CSI, rhythmic patterns are extracted

associated with respiration and abrupt changes due to the body movement.

Compared to existing sleep monitoring systems that usually require special

devices attached to human body (i.e. probes, head belt, and wrist band), “THE

CONTACTLESS RESPIRATION MONITORING VIA WiFi DEVICES” system

is completely contactless. In addition, different from many vision-based sleep

monitoring systems, it is robust to low-light environments and does not raise

privacy concerns. It can reliably track a person’s respiration and sleeping postures

and rollovers in different conditions.

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