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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 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.
2
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
3
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.
4
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.
5
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.,
6
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
7
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.
8
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.
9
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.
10
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
11
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
12
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.
13
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
14
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.
15
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]:
17
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
20
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.
21
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
22
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
23
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
24
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.
25
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.
26
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.
27
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.
28
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.
29
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.
30
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.
31
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
32
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
33
& 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.
34
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.
35
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.
36
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
37
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
38
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
39
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.
40
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.
41
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.
42
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