# PATHOS: Pervasive at Home Sleep Monitoring

Authors: Ian Obermiller; Sheikh I. Ahamed
Journal: International Journal of Telemedicine and Applications
 (2008)

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## Abstract

Sleeping disorders affect a large percentage of the population, and many of them go undiagnosed each year because the method of diagnosis is to stay overnight at a sleep center. Because pervasive technologies have become so prevalent and affordable, sleep monitoring is no longer confined to a permanent installation, and can therefore be brought directly into the user home. We present a unique solution to the problem of home sleep monitoring that has the possibility to take the place of and expand on the data from a sleep center. PATHOS focuses not only on analyzing patterns during the night, but also on collecting data about the subject lifestyle that is relevant and important to the diagnosis of his/her sleep.
PATHOS means “evoking emotion.” Here, we mean Pathos will help us to keep healthy: both mentally and physically.
Our solution uses existing technology to keep down cost and is completely wireless in order to provide portability and be easily to customize. The daytime collection also utilizes existing technology and offers a wide range of input methods to suit any type of person. We also include an in-depth look at the hardware we used to implement and the software providing user interaction.
Our system is not only a viable alternative to a sleep center, it also provides functions that a static, short-term solution cannot  provide, allowing for a more accurate diagnosis and treatment.


---

## Body

## 1. Introduction

Sleep problems affect
more than 70 million people in the United States alone [1]. Yet, the public as
a whole is largely unaware of the causes and consequences of the disorder that
one in four people have. Additionally, the majority of sleep disorders go
undiagnosed and untreated. Sleep disorders range from snoring to insomnia, and
can be as serious as obstructive sleep apnea, when a person stops breathing
multiple times during the night. Because sleep problems cost Americans over
$100 billion a year in lost productivity and medical expenses, it is imperative
that more people are diagnosed and treated for such a common illness.Sleeping disorders are usually diagnosed
only when a person goes to a sleep center to have diagnostics taken over night.
During such a study, known as a polysomnogram, a patient goes to a sleep
center, spends around 45 minutes getting hooked up to a machine via electrodes,
sensory belts, a microphone, and various other sensors. After the process,
which is painless and made to be as nonintrusive as possible, the patient goes
to sleep and the numbers are gathered. There is no doubt that this method of
sleep monitoring works, or that it is the main mode of diagnosing many
important sleeping disorders. However, sleep centers have several drawbacks,
including cost, time, and environment.In order for a truly universal system
for monitoring sleep and diagnosing disorders to become a reality, it must be
moved from the center to the home. Although this is not a new idea, the
technology has only recently caught up with the proposal. There is a continual
research to improve the sensors used in polysomnography. Researchers at the
University of Aizu [2], for example, have created an under-pillow sensor to
measure respiratory rhythm and pulse during sleep. A more complete system, with
more sensors, has been designed at the University of Cairo [3] and is presented
with a detailed hardware design. The University of Washington [4] has
researched using inexpensive multimodal sensors to detect sleep conditions in a
completely noninvasive manner.The majority of previous research in the
area of polysomnography technology has been directed toward the development
hardware designs that would be more practical in a sleep center than in a home.
The aim of the project at the University of Cairo [3], however, was a home
monitoring solution, but its purpose was mainly to decide whether or not a
person should go through with a full evaluation at a dedicated sleep center.Our approach to the field of
polysomnography combines several aspects of the previous research and seeks to
provide an all-encompassing solution that negates the need for a person to ever
spend the night at a costly sleep center. The three main goals of PATHOS are to
be (1) simple and inexpensive, using existing technology, (2) performed in a
natural environment, namely, the subject's home, and (3) a continuous solution,
not limited to gathering data during the night. For night monitoring, we
propose using a mesh network of simple sensors that wirelessly transmit data to
a subject’s mobile device or pc. The sensors are inexpensive and require
interpretation which can be performed by small motes which form the nodes of
the network. Additionally, data regarding factors that affect sleep is
collected throughout the day using the subject’s cell phone. Because the
solution is contained entirely at the person’s home, the steep cost and
unfamiliar location of a sleep center can be bypassed. Furthermore, data can be
collected over a longer period of a time, producing a more accurate diagnosis.Our paper is laid out as follows: Section2 describes the motivation for our choice of design; Section 3 details the
characteristics of our approach; Section 4 explains our approach; Section 5 focuses
on our implementation; Section 6 contains information collected from a user
survey and evaluation of our proposal; Section 7 discusses in greater depth
related research; and finally, Section 8 presents the conclusion or our plan
and the direction of our future work.


## 2. Motivation

Consider the following
scenarios.


### 2.1. Scenario One

Arin is an older woman who has never
suspected that she had a sleeping disorder. Because an overnight sleep center
is unpractical and far too expensive, she is never diagnosed for a disorder
even though she is frequently tired during the day and never feels fully rested.


### 2.2. Scenario Two

Grahame is a middle aged man who suspects he may have a sleeping
disorder. However, he is uncomfortable staying overnight in an unfamiliar place
and does not have the time due to his busy work week, so he refuses to go to a
sleeping center for a diagnosis. Years later, his wife convinces him to go, and
he finds that he has sleep apnea, a dangerous disorder that could have been
treated long ago and has resulted in many days and nights of needless symptoms.


### 2.3. Scenario Three

Finally, consider Joye, a young woman
who also suspects she may have a sleeping disorder. Because there are no other
options, she goes to a sleep center for diagnosis three nights in a row. During
the night, she is more restless than usual. The next day, she is asked a series
of questions about her lifestyle, and the doctors conclude that she has a minor
sleep disorder. In reality however, she has a more major sleep disorder, and
her lifestyle is making it progressively worse. The short amount of time spent
monitoring her sleep and the survey that reflected her opinions about herself
more than how she actually acts misrepresented her situation and caused a
misdiagnosis.


### 2.4. Interpretation

Our motivation for PATHOS stems from the
previous scenarios. First, a sleep center is not the most practical way for a
person to be diagnosed. They are costly due to sophisticated equipment and the
trained professionals who must operate the equipment. Second, not everyone has
time to stay overnight somewhere multiple times a week. Third, it may be very
uncomfortable for someone to sleep in a foreign place, which may lead them to
sleep differently than usual because of nerves. And, if the readings at a sleep
center are sufficiently different than they would otherwise be at home, a
misdiagnosis could occur.


## 2.1. Scenario One

Arin is an older woman who has never
suspected that she had a sleeping disorder. Because an overnight sleep center
is unpractical and far too expensive, she is never diagnosed for a disorder
even though she is frequently tired during the day and never feels fully rested.


## 2.2. Scenario Two

Grahame is a middle aged man who suspects he may have a sleeping
disorder. However, he is uncomfortable staying overnight in an unfamiliar place
and does not have the time due to his busy work week, so he refuses to go to a
sleeping center for a diagnosis. Years later, his wife convinces him to go, and
he finds that he has sleep apnea, a dangerous disorder that could have been
treated long ago and has resulted in many days and nights of needless symptoms.


## 2.3. Scenario Three

Finally, consider Joye, a young woman
who also suspects she may have a sleeping disorder. Because there are no other
options, she goes to a sleep center for diagnosis three nights in a row. During
the night, she is more restless than usual. The next day, she is asked a series
of questions about her lifestyle, and the doctors conclude that she has a minor
sleep disorder. In reality however, she has a more major sleep disorder, and
her lifestyle is making it progressively worse. The short amount of time spent
monitoring her sleep and the survey that reflected her opinions about herself
more than how she actually acts misrepresented her situation and caused a
misdiagnosis.


## 2.4. Interpretation

Our motivation for PATHOS stems from the
previous scenarios. First, a sleep center is not the most practical way for a
person to be diagnosed. They are costly due to sophisticated equipment and the
trained professionals who must operate the equipment. Second, not everyone has
time to stay overnight somewhere multiple times a week. Third, it may be very
uncomfortable for someone to sleep in a foreign place, which may lead them to
sleep differently than usual because of nerves. And, if the readings at a sleep
center are sufficiently different than they would otherwise be at home, a
misdiagnosis could occur.


## 3. Characteristics

The characteristics that make our approach to polysomnography novel and unique are the
following.


### 3.1. Cost Effective

Polysomnography is typically a very costly process, especially when done
at a sleep center. Even portable systems available today are not inexpensive
enough for the average person to be compelled to verify whether or not she has
a sleeping disorder, especially if it is a minor one. Our system uses
inexpensive, multimodal sensors and takes advantage of the existing technology
a user would already have in her home, such as computer, cell phone, or PDA.  In addition, the system can handle multiple
users, if, for instance, a couple wanted to monitor both of their sleeping
habits but only wanted to have one unit. They could trade-off each night, indicating
who is wearing the sensors, and send in separate data during the day.


### 3.2. Accurate

In order to be comparable to the diagnosis a sleep center would give, a
portable solution must keep accuracy as an important goal. To balance cost and
accuracy, the chosen sensors are in some cases multifunctional (the pulse
oximeter) and in all cases deliver the best results for the price. Also,
because our approach can be used as a long-term monitoring solution, extreme
sensor readings will average out over the longer period of time, producing a
more accurate assessment.


### 3.3. Reliable

Because we have designed our sleep monitor with long-term monitoring as
an objective, the system itself must be reliable. It should be able to be used
consistently for weeks or even months at a time without degrading in accuracy
or  requiring any maintenance on the part of the user. Although it is impossible
to prepare for all situations that may occur, the system is easily customizable
for any age of the user, allowing it to respond to unique needs more
effectively than a “one size fits all” solution could.


### 3.4. Privacy Aware

Because there is
a system that monitors sleeping habits and collects lifestyle data, it is
natural that users would want to control the use of their confidential
information. Our solution allows the user to customize how their information is
shared and who can have access to it. By default, only the doctor who will
diagnose the user will have access to the information, and data on the system
itself will be restricted to the specific user who collected it.


### 3.5. Simple Yet Meaningful GUI

One of the major target age groups of an easy-to-use, portable sleep monitoring system is
the elderly. As time progresses, the older age groups will become more and more
familiar with computers and mobile devices, but to accommodate everyone now and
in the future, the user interface of our application is very simple. Text is
easy to read, and most importantly a new user should have little problems
utilizing the full potential of the system. For the completely technophobic, it
can be nearly autonomous, requiring the user to simply wear the sensors and
then visit the doctor when collection is complete. However, it will also cater
to the other end of the spectrum by being very customizable and revealing more
options to more experienced users.


### 3.6. Easy and Noninvasive to Setup and Use

A user will be expected to use the system every night in order to get an
accurate assessment of her sleeping patterns. For this reason, the device must
be easy to setup each night and must not interfere with a good night’s rest.
Pervasive technologies make this especially easy, but present some challenges
as well. The communication between devices is wireless, so the user does not
need to worry about many wires being tangled up during sleep. However, the
number of wireless motes needs to be balanced as well, because the more parts a
system contains the less easy it is to setup and use. Because of the
expandability of the system, it is easy to incorporate many wireless nodes or
stick to fewer nodes with more sensors attached to each one. Figure1 shows one
such configuration.Figure 1
Diagram of the sensor network used for nighttime monitoring.In addition to the nighttime sensor network, the daytime data collection
must be quick and simple as well. By allowing the user multiple ways to enter
information, she can choose the most convenient way and is therefore more
likely to report throughout the day.


### 3.7. Portable

Portability is the key aspect that draws users to our system. Besides the
advantages of allowing a user to sleep in her own bed each night and not be
bothered by traveling to and from a sleep center; the system is also readily
portable to be taken to a different sleeping location. For instance, when the
user goes on vacation or temporarily is staying at another place, the system
can be brought along; data collection does not have to stop.


### 3.8. Customizable

For a system to be truly universal, it must be easy to customize for
different users. In our system, this is accomplished in two ways. First, as
explained in Section3.6, our wireless sensor network allows nodes to be easily added and
removed. Sensors that are not applicable to a certain users diagnosis can be
left out, saving time and money. Further, if a doctor believes a patient needs
more advanced monitoring, additional sensors can be added to the system to
provide a more detailed report. Second, the main aspects of the system can be
used in several different ways to achieve the same end result. See Section 6B
for an example of multiple input methods.Such a customizable system would lead to separate base configuration for
different user groups, for example, divided by age.On the one hand, an elderly person may be technophobic, and would want a
system that requires very little interaction on his part. His system could
include a passive infrared video monitor that could be setup once and then used
every night. Also, instead of entering data throughout the day, he could enter
a simple phone survey once each night. All the data collected would be
automatically sent to the doctor and analyzed without any interaction.A middle aged person, on the other hand, would perhaps want a great deal
of control over his system. He would choose the most accurate sensors that
attach directly to his body, such as the accelerometer and thermistors for
airflow, even though for other people they may be more difficult to setup.
Although he does not use texting for cell phone data entrance, he periodically
takes a quick survey from his work or home computer to enter daytime
information. Every morning when he wakes up he checks out his sleeping and
daytime scores, and looks to see how he can improve his sleep by changing his
habits.There is also the case of a younger person, who does not mind entering her
data throughout the day via simple text messaging. At night however, she
prefers a similar setup to the elderly person: easy to setup and quick to
activate. Also like the elderly person, she prefers to have her doctor look at the data
and thus has it sent directly to him.The preceding customizations are only a few ways by which the system could be configured. It
is adaptable to any circumstances, and can be updated with new sensors just as
easily as old ones can be removed.


## 3.1. Cost Effective

Polysomnography is typically a very costly process, especially when done
at a sleep center. Even portable systems available today are not inexpensive
enough for the average person to be compelled to verify whether or not she has
a sleeping disorder, especially if it is a minor one. Our system uses
inexpensive, multimodal sensors and takes advantage of the existing technology
a user would already have in her home, such as computer, cell phone, or PDA.  In addition, the system can handle multiple
users, if, for instance, a couple wanted to monitor both of their sleeping
habits but only wanted to have one unit. They could trade-off each night, indicating
who is wearing the sensors, and send in separate data during the day.


## 3.2. Accurate

In order to be comparable to the diagnosis a sleep center would give, a
portable solution must keep accuracy as an important goal. To balance cost and
accuracy, the chosen sensors are in some cases multifunctional (the pulse
oximeter) and in all cases deliver the best results for the price. Also,
because our approach can be used as a long-term monitoring solution, extreme
sensor readings will average out over the longer period of time, producing a
more accurate assessment.


## 3.3. Reliable

Because we have designed our sleep monitor with long-term monitoring as
an objective, the system itself must be reliable. It should be able to be used
consistently for weeks or even months at a time without degrading in accuracy
or  requiring any maintenance on the part of the user. Although it is impossible
to prepare for all situations that may occur, the system is easily customizable
for any age of the user, allowing it to respond to unique needs more
effectively than a “one size fits all” solution could.


## 3.4. Privacy Aware

Because there is
a system that monitors sleeping habits and collects lifestyle data, it is
natural that users would want to control the use of their confidential
information. Our solution allows the user to customize how their information is
shared and who can have access to it. By default, only the doctor who will
diagnose the user will have access to the information, and data on the system
itself will be restricted to the specific user who collected it.


## 3.5. Simple Yet Meaningful GUI

One of the major target age groups of an easy-to-use, portable sleep monitoring system is
the elderly. As time progresses, the older age groups will become more and more
familiar with computers and mobile devices, but to accommodate everyone now and
in the future, the user interface of our application is very simple. Text is
easy to read, and most importantly a new user should have little problems
utilizing the full potential of the system. For the completely technophobic, it
can be nearly autonomous, requiring the user to simply wear the sensors and
then visit the doctor when collection is complete. However, it will also cater
to the other end of the spectrum by being very customizable and revealing more
options to more experienced users.


## 3.6. Easy and Noninvasive to Setup and Use

A user will be expected to use the system every night in order to get an
accurate assessment of her sleeping patterns. For this reason, the device must
be easy to setup each night and must not interfere with a good night’s rest.
Pervasive technologies make this especially easy, but present some challenges
as well. The communication between devices is wireless, so the user does not
need to worry about many wires being tangled up during sleep. However, the
number of wireless motes needs to be balanced as well, because the more parts a
system contains the less easy it is to setup and use. Because of the
expandability of the system, it is easy to incorporate many wireless nodes or
stick to fewer nodes with more sensors attached to each one. Figure1 shows one
such configuration.Figure 1
Diagram of the sensor network used for nighttime monitoring.In addition to the nighttime sensor network, the daytime data collection
must be quick and simple as well. By allowing the user multiple ways to enter
information, she can choose the most convenient way and is therefore more
likely to report throughout the day.


## 3.7. Portable

Portability is the key aspect that draws users to our system. Besides the
advantages of allowing a user to sleep in her own bed each night and not be
bothered by traveling to and from a sleep center; the system is also readily
portable to be taken to a different sleeping location. For instance, when the
user goes on vacation or temporarily is staying at another place, the system
can be brought along; data collection does not have to stop.


## 3.8. Customizable

For a system to be truly universal, it must be easy to customize for
different users. In our system, this is accomplished in two ways. First, as
explained in Section3.6, our wireless sensor network allows nodes to be easily added and
removed. Sensors that are not applicable to a certain users diagnosis can be
left out, saving time and money. Further, if a doctor believes a patient needs
more advanced monitoring, additional sensors can be added to the system to
provide a more detailed report. Second, the main aspects of the system can be
used in several different ways to achieve the same end result. See Section 6B
for an example of multiple input methods.Such a customizable system would lead to separate base configuration for
different user groups, for example, divided by age.On the one hand, an elderly person may be technophobic, and would want a
system that requires very little interaction on his part. His system could
include a passive infrared video monitor that could be setup once and then used
every night. Also, instead of entering data throughout the day, he could enter
a simple phone survey once each night. All the data collected would be
automatically sent to the doctor and analyzed without any interaction.A middle aged person, on the other hand, would perhaps want a great deal
of control over his system. He would choose the most accurate sensors that
attach directly to his body, such as the accelerometer and thermistors for
airflow, even though for other people they may be more difficult to setup.
Although he does not use texting for cell phone data entrance, he periodically
takes a quick survey from his work or home computer to enter daytime
information. Every morning when he wakes up he checks out his sleeping and
daytime scores, and looks to see how he can improve his sleep by changing his
habits.There is also the case of a younger person, who does not mind entering her
data throughout the day via simple text messaging. At night however, she
prefers a similar setup to the elderly person: easy to setup and quick to
activate. Also like the elderly person, she prefers to have her doctor look at the data
and thus has it sent directly to him.The preceding customizations are only a few ways by which the system could be configured. It
is adaptable to any circumstances, and can be updated with new sensors just as
easily as old ones can be removed.


## 4. Our Approach

We have focused on
making PATHOS low cost, suitable for home use, and most importantly a
continuous, long-term system. Our approach can be split into three major
sections: nighttime data collection, daytime data collection, and analysis.


### 4.1. Nighttime Data Collection

In order to make a sensor-based system
that is user friendly and not too invasive during sleep, we will use a network
of sensors and motes that communicate wirelessly using the Zigbee protocol.
Figure1 details a possible setup for the patient during nighttime monitoring.
Three of the sensors are connected via wires to the main mote located on the
person arm. The nasal and oral airflow sensors are thermistors that measure the
patient’s breathing, a characteristic especially important when diagnosing
sleep apnea. There is also a microphone near the base of the subject’s neck to
record sound such as snoring. The pulse and blood oxygen sensor uses a simple
finger clip-on, and is then connected to the main mote by a wire running up the
arm. Because of their proximity to the main mote, it would have been more
cumbersome for the sensors to
communicate wirelessly via their own motes. The main mote itself contains an
accelerometer to measure arm movement and a thermometer to measure the
subject’s temperature. The leg movement sensor (accelerometer) is connected to
its own mote and transmits data to the main mote wirelessly. Additional sensors
could be added wirelessly to the network very easily. For example, a simple
passive infrared camera could be setup to record nighttime sleep movement
patterns with more detail than the accelerometers could.The setup that we have depicted is one of
many. The system can be adapted to include more sensors in different ways,
depending on the age group of the user, and what and how they want their sleep to
be monitored.Not pictured in the diagram is the
receiving mote connected to the person’s personal computer. If the pc is in the
same room, the mote connected to it could be used to record the ambient
pressure and humidity of the sleeping environment. If not, another mote could
easily be added to the network and placed on a bedside desk. The main mote on
the patients arm cannot be used to measure ambient temperature and humidity
because it may be covered by blankets or moved frequently during sleep.


### 4.2. Daytime Data Collection

The major aspect that differentiates our approach from the others is that
we seek to continuously monitor the person’s lifestyle and habits that factor
into the condition of her sleep. This allows us to make a more accurate
assessment of factors that influence sleep and weigh the data collected during
the night accordingly. Since many sleep assessments rely on survey data to
collect habits and lifestyle information from patients, most of which are less
than accurate, an easy to use system that gathers information as it happens
could be a valuable resource for doctors diagnosing a sleep disorder. Some of
the factors that will be monitored throughout the day are eating and drinking
patterns (caffeine, alcohol, heavy/light meals, snacking, drinking before bed),
exercise patterns (frequency, time of day, difficulty), and lifestyle (smoking,
relaxing or stimulating activities before bedtime, sleeping schedule, amount of
fatigue, and stress level). This data could be entered in a variety of ways,
but must be convenient for the user or it will not get entered at all.
Therefore, the system will rely on everyday technology that the user already
has access to. For instance, she could enter information through a text message
or voice automated prompt on a cell phone. Or she could go to a website and
fill out a quick survey form. If none of the above is a viable option, the user
could keep track in a paper log and enter the data manually later in the day.


### 4.3. Data Analysis

All of the data collected throughout the day and night is relayed to a
central location, which could be on the user’s pc or at a central database
accessed through the internet. The central database would allow access to the
patient and valid doctor’s only to insure user privacy. Because data is stored
on a computer or server, there is plenty of hard drive space for many weeks
worth of data. Thus instead of data from only three nights at a sleep center, a
user could get a diagnosis from her last month of sleeping habits. This
long-term solution is also viable because the technology is noninvasive and
would be practical for a patient to use every night for a month.Additionally, the system itself can give recommendations and customized
reports based on the data it collects. This is discussed in further detail in architecture—software design Section5.2.


## 4.1. Nighttime Data Collection

In order to make a sensor-based system
that is user friendly and not too invasive during sleep, we will use a network
of sensors and motes that communicate wirelessly using the Zigbee protocol.
Figure1 details a possible setup for the patient during nighttime monitoring.
Three of the sensors are connected via wires to the main mote located on the
person arm. The nasal and oral airflow sensors are thermistors that measure the
patient’s breathing, a characteristic especially important when diagnosing
sleep apnea. There is also a microphone near the base of the subject’s neck to
record sound such as snoring. The pulse and blood oxygen sensor uses a simple
finger clip-on, and is then connected to the main mote by a wire running up the
arm. Because of their proximity to the main mote, it would have been more
cumbersome for the sensors to
communicate wirelessly via their own motes. The main mote itself contains an
accelerometer to measure arm movement and a thermometer to measure the
subject’s temperature. The leg movement sensor (accelerometer) is connected to
its own mote and transmits data to the main mote wirelessly. Additional sensors
could be added wirelessly to the network very easily. For example, a simple
passive infrared camera could be setup to record nighttime sleep movement
patterns with more detail than the accelerometers could.The setup that we have depicted is one of
many. The system can be adapted to include more sensors in different ways,
depending on the age group of the user, and what and how they want their sleep to
be monitored.Not pictured in the diagram is the
receiving mote connected to the person’s personal computer. If the pc is in the
same room, the mote connected to it could be used to record the ambient
pressure and humidity of the sleeping environment. If not, another mote could
easily be added to the network and placed on a bedside desk. The main mote on
the patients arm cannot be used to measure ambient temperature and humidity
because it may be covered by blankets or moved frequently during sleep.


## 4.2. Daytime Data Collection

The major aspect that differentiates our approach from the others is that
we seek to continuously monitor the person’s lifestyle and habits that factor
into the condition of her sleep. This allows us to make a more accurate
assessment of factors that influence sleep and weigh the data collected during
the night accordingly. Since many sleep assessments rely on survey data to
collect habits and lifestyle information from patients, most of which are less
than accurate, an easy to use system that gathers information as it happens
could be a valuable resource for doctors diagnosing a sleep disorder. Some of
the factors that will be monitored throughout the day are eating and drinking
patterns (caffeine, alcohol, heavy/light meals, snacking, drinking before bed),
exercise patterns (frequency, time of day, difficulty), and lifestyle (smoking,
relaxing or stimulating activities before bedtime, sleeping schedule, amount of
fatigue, and stress level). This data could be entered in a variety of ways,
but must be convenient for the user or it will not get entered at all.
Therefore, the system will rely on everyday technology that the user already
has access to. For instance, she could enter information through a text message
or voice automated prompt on a cell phone. Or she could go to a website and
fill out a quick survey form. If none of the above is a viable option, the user
could keep track in a paper log and enter the data manually later in the day.


## 4.3. Data Analysis

All of the data collected throughout the day and night is relayed to a
central location, which could be on the user’s pc or at a central database
accessed through the internet. The central database would allow access to the
patient and valid doctor’s only to insure user privacy. Because data is stored
on a computer or server, there is plenty of hard drive space for many weeks
worth of data. Thus instead of data from only three nights at a sleep center, a
user could get a diagnosis from her last month of sleeping habits. This
long-term solution is also viable because the technology is noninvasive and
would be practical for a patient to use every night for a month.Additionally, the system itself can give recommendations and customized
reports based on the data it collects. This is discussed in further detail in architecture—software design Section5.2.


## 5. Architecture

### 5.1. Hardware

The hardware we have chosen for PATHOS includes the following.(i)
Tmote Sky [5]: every sensor node consists of a Tmote Sky with either
embedded sensors or sensors attached through analog output. The motes provide
the Zigbee wireless connection and are battery powered, allowing complete
freedom of placement.(ii)
BCI Micro Power Oximeter Board [6]: an extension for the Tmote Sky
that allows a finger pulse oximeter to be connected directly to the mote. This
sensor checks blood oxygen level and also records pulse.(iii)
EasySen SBT80—Multi Modality sensor board [7]: another extension
board for the Tmote Sky, this board provides additional sensors to the mote, the
most important of which are two dual-axis accelerometers which, when combined,
can measure motion in three dimensions.(iv)
Handheld device or personal computer: forming the base of the network
and is responsible for managing the collected data. The program will be
implemented so that it is deployable on the user’s existing hardware, cutting
down costs and the amount of devices present.(v)
Cellular phone: used for collecting data during the day. The daytime
collection software can be implemented as either a program that runs on the
cell phone or a program running server-side that collects information through
text messages or an automated phone system.


### 5.2. Software Design

Our software design consists of two separate programs: one running on the
nodes and one running on the base station. Figure2 provides an overview of the
programs and how they interact with each other and the user.Figure 2
Diagram of the software architecture.The program running on the motes is responsible for collecting data from
the sensors and processing the data before sending it to the base. The sensors
are either built into the nodes (temperature, humidity, movement), or are
provided via a wired external sensor (pulse oximeter, thermistors, acoustic). The
nodes run the TinyOS operating system and as a result are quite capable of
preprocessing the data and transforming the analog voltage input to a format
that can be interpreted easily by the base. As soon as the data is processed,
it is sent wirelessly to the base. Nodes also have the ability to relay signals
from another node that is too far away to connect to the base. Because the
Tmote Sky’s ZigBee wireless has a range of over 50 meters (indoor), a node will
typically never be out of reach of the base, unless the wireless is scaled back
in order to ensure a more secure connection.The software running on the base is more important from the user’s point
of view because it is the only aspect of the system that he interacts with
regularly. The most important part of the base software is the communication
module, which is responsible for gathering the data from multiple incoming motes,
receiving data throughout the day from a cell phone or internet source, and
sending collected data to the doctor for review. Because very few computers or
handheld devices implement the ZigBee wireless protocol, a node must be plugged
into the handheld device or computer via a USB port. That node then relays all
of the information from the incoming nodes by UART to the base.The interpretation module is responsible for identifying outlying data,
for example if a sensor falls off during the night, or becomes disconnected the
data will not be included in calculations, but it will still be stored for
future reference. The interpretation module is also important because it computes
and stores data results as it is collected. For instance, every day the module
will calculate averages, maxes, and minimums, and store this data so that it
does not have to be calculated again when the user wants to run longer, more
detailed reports.A separate storage module is necessary because it must decide where
exactly to store the collected information. Because the system is collecting
data all night for up to many months at a time, the amount of space needed can
easily exceed the memory on a small handheld device. Normal computer should
have little problem storing the data, however in both cases the storage module
will utilize data compression techniques to remove statistical redundancy and
efficiently store data in the smallest possible space.The display and interaction module is visible in the screenshots in
Figure3. Every morning when a user awakes she has the option to look at a
daily assessment of the last night’s sleep. Figure 3(a) shows this function,
where the program  assigns a sleep
quality score based on the collected data. Screenshot 3(a) also shows how the
daytime activities affected sleep and a summary of the entire page. The results
of the screenshot are simulated to develop such a score would require extensive
processing of the data which we have not developed yet. However, it is possible
for a doctor to receive a week’s worth of data and then send the patient the
information listed in screenshots 3(a) and 3(b). The doctor would make the
observations using his own judgment, and when the data is sent to the base it
informs the user that a new assessment has been delivered. This would
effectively allow a doctor to make a remote diagnosis, and if the condition is
severe enough the diagnosis could be done in person as well.Some screenshots of our implementation.
(a)
Assessing a night’s sleep(b)
In-depth look at a night’s sleep(c)
Iconic, easy to use menu(d)
Detailed graph and summaryScreenshots3(c) and 3(d) are fully implemented, as they rely on
collected data. The summary section in 3(d) is implemented using a predefined
set of threshold values that take into account the users long-term and short-term
average scores. Screenshot 3(c) displays the program’s simple, iconic,
point-and-click interface (or one style tap, in this case).Also, in screenshot A the large numbers allow a user to quickly identify
how well they slept according to the sensors and what factors during the day
influenced their sleep at night. The numbers themselves are clickable, leading
to the in-depth assessment found in screenshot3(b).


## 5.1. Hardware

The hardware we have chosen for PATHOS includes the following.(i)
Tmote Sky [5]: every sensor node consists of a Tmote Sky with either
embedded sensors or sensors attached through analog output. The motes provide
the Zigbee wireless connection and are battery powered, allowing complete
freedom of placement.(ii)
BCI Micro Power Oximeter Board [6]: an extension for the Tmote Sky
that allows a finger pulse oximeter to be connected directly to the mote. This
sensor checks blood oxygen level and also records pulse.(iii)
EasySen SBT80—Multi Modality sensor board [7]: another extension
board for the Tmote Sky, this board provides additional sensors to the mote, the
most important of which are two dual-axis accelerometers which, when combined,
can measure motion in three dimensions.(iv)
Handheld device or personal computer: forming the base of the network
and is responsible for managing the collected data. The program will be
implemented so that it is deployable on the user’s existing hardware, cutting
down costs and the amount of devices present.(v)
Cellular phone: used for collecting data during the day. The daytime
collection software can be implemented as either a program that runs on the
cell phone or a program running server-side that collects information through
text messages or an automated phone system.


## 5.2. Software Design

Our software design consists of two separate programs: one running on the
nodes and one running on the base station. Figure2 provides an overview of the
programs and how they interact with each other and the user.Figure 2
Diagram of the software architecture.The program running on the motes is responsible for collecting data from
the sensors and processing the data before sending it to the base. The sensors
are either built into the nodes (temperature, humidity, movement), or are
provided via a wired external sensor (pulse oximeter, thermistors, acoustic). The
nodes run the TinyOS operating system and as a result are quite capable of
preprocessing the data and transforming the analog voltage input to a format
that can be interpreted easily by the base. As soon as the data is processed,
it is sent wirelessly to the base. Nodes also have the ability to relay signals
from another node that is too far away to connect to the base. Because the
Tmote Sky’s ZigBee wireless has a range of over 50 meters (indoor), a node will
typically never be out of reach of the base, unless the wireless is scaled back
in order to ensure a more secure connection.The software running on the base is more important from the user’s point
of view because it is the only aspect of the system that he interacts with
regularly. The most important part of the base software is the communication
module, which is responsible for gathering the data from multiple incoming motes,
receiving data throughout the day from a cell phone or internet source, and
sending collected data to the doctor for review. Because very few computers or
handheld devices implement the ZigBee wireless protocol, a node must be plugged
into the handheld device or computer via a USB port. That node then relays all
of the information from the incoming nodes by UART to the base.The interpretation module is responsible for identifying outlying data,
for example if a sensor falls off during the night, or becomes disconnected the
data will not be included in calculations, but it will still be stored for
future reference. The interpretation module is also important because it computes
and stores data results as it is collected. For instance, every day the module
will calculate averages, maxes, and minimums, and store this data so that it
does not have to be calculated again when the user wants to run longer, more
detailed reports.A separate storage module is necessary because it must decide where
exactly to store the collected information. Because the system is collecting
data all night for up to many months at a time, the amount of space needed can
easily exceed the memory on a small handheld device. Normal computer should
have little problem storing the data, however in both cases the storage module
will utilize data compression techniques to remove statistical redundancy and
efficiently store data in the smallest possible space.The display and interaction module is visible in the screenshots in
Figure3. Every morning when a user awakes she has the option to look at a
daily assessment of the last night’s sleep. Figure 3(a) shows this function,
where the program  assigns a sleep
quality score based on the collected data. Screenshot 3(a) also shows how the
daytime activities affected sleep and a summary of the entire page. The results
of the screenshot are simulated to develop such a score would require extensive
processing of the data which we have not developed yet. However, it is possible
for a doctor to receive a week’s worth of data and then send the patient the
information listed in screenshots 3(a) and 3(b). The doctor would make the
observations using his own judgment, and when the data is sent to the base it
informs the user that a new assessment has been delivered. This would
effectively allow a doctor to make a remote diagnosis, and if the condition is
severe enough the diagnosis could be done in person as well.Some screenshots of our implementation.
(a)
Assessing a night’s sleep(b)
In-depth look at a night’s sleep(c)
Iconic, easy to use menu(d)
Detailed graph and summaryScreenshots3(c) and 3(d) are fully implemented, as they rely on
collected data. The summary section in 3(d) is implemented using a predefined
set of threshold values that take into account the users long-term and short-term
average scores. Screenshot 3(c) displays the program’s simple, iconic,
point-and-click interface (or one style tap, in this case).Also, in screenshot A the large numbers allow a user to quickly identify
how well they slept according to the sensors and what factors during the day
influenced their sleep at night. The numbers themselves are clickable, leading
to the in-depth assessment found in screenshot3(b).


## 6. Implementation

We have partially implemented the
aforementioned hardware and software architecture of PATHOS. The flow of data
can be seen in Figure4. The sensor node collects data and sends it over the radio;
the base receives and processes the data for sending to the server, which in
turn stores it in a database.Figure 4
Flow diagram of the implementation.All of the sensors we have implemented are accessed via the Tmotes
external ADC ports. They return a voltage that can be converted into the
specified units for the type of data the sensor collects. This conversion is
performed in the base station in order to keep the mote software simple, so
that the formula’s can be modified as necessary.The base implements a queue for the sensor data it receives in order to
process them efficiently and without worry of losing data. After the threshold
limit for the queue has been reached, the base program dequeues the data,
averages each reading, and sends it to the server. Since the sensor nodes send
in data twice a second, an optimal threshold limit is 120 data packets, or one
minute’s worth of data. One minute is enough time to average out any sensor
hiccups and also a small
enough time span in which graphs and analysis can have fine grain data
control. Larger limits can be used as well, and we will have to do more
research to find the optimal limits for different sensor types.The server side programming is currently implemented with a PHP file. The
base posts the sensor data to the website, and the server then stores the data
in a flat file xml system. When the site is accessed by the user in a browser,
the sensor data collected over the last hour is displayed. The completed system
will have authentication by the base and user account for each individual user. They will be
able to login to the website and view statistics for the last day, month, or even
year. The website will be the user’s primary means for accessing analysis of
the data and doctor’s recommendations, and the data will also be sent to the
base for easier display.


## 7. Evaluation

To help us evaluate
the idea of a home-based sleep monitoring system and continuous information
gathering, we took a survey of 20 participants. Our survey confirmed the
statement that a home-based system is a better alternative for many people than
an overnight sleep center. Figure5 details the results of our survey, where
the subjects were asked if how likely they were to seek professional opinion,
if they suspected they had a minor or major sleeping disorder, and then how
likely they would go to
a sleep center or stay at home for a diagnosis. The results show that the
participants were 10% more likely to receive home monitoring than go
to a sleep center if they suspected they had a major sleeping disorder. The
difference increased dramatically when a minor sleeping disorder was concerned:
home monitoring was favored 29% more than a sleep center. Our survey also
showed that the participants were twice as likely to seek professional opinion
if they suspected they had a major sleeping disorder rather than a minor  sleeping disorder.Figure 5
Graph of user response.Finally, in a question where
participants were asked outright which they would prefer, only 10% chose the
sleeping center, versus 75% who would prefer to have the monitoring done at
home. Figure6 shows the distribution of participants who would prefer at home
diagnosis or a sleep center. The majority of the participants would prefer to
be monitored at home, which could be due to several reasons, including
increased comfort and convenience. This survey data confirms that there is a
large need for inexpensive, easy to use home sleep monitoring.Figure 6
Chart of preference for home monitoring or sleep center.Also, over 70% of the participants said
they would be willing to enter data throughout the course of the day if it were
convenient and helped the
diagnosis of a
sleep disorder. Although this figure is favorable, it could be higher, which
means a prototype system must be very convenient and easy to use. Another
possibility is that the user is asked a short series of questions once or twice
per day, which would cut down on the number of times a user has to remember to
interact with the system.Finally, the participants were asked whether
they have, suspect they have, are
unsure, or do indeed have a sleeping disorder, the results of which are shown
in Figure7. Even from this small sample of participants, it is clear that
there is a need for a simple, at-home sleep monitoring solution because around
40% of participants were unsure  whether
or not they have a sleeping disorder. If disorders were easier to diagnose, that number
would be much lower.Figure 7
Chart of the participant’s knowledge of their own conditions.


## 8. Related Works

Many projects are underway that focus on
general health monitoring. A
long term monitoring system known as Terva [8] has been implemented to collect
critical health data such as blood pressure, temperature, sleeping conditions,
and weight. The problem with Terva is that although it is self contained, it is
housed in a casing about the size of a suitcase, which seriously dampers
mobility. As a result, Terva is only practical inside the home. IST VIVAGO is a
system used to remotely monitor activity and generate alarms based on received
data [9]. In contrast with Terva, our system is small and completely wireless,
allowing it to easily adapt to new situations.Another system, wireless wellness
monitor (WWM), is built
specifically to manage obesity [10]. The system has measuring devices, mobile
terminals (handheld devices), and a base station home server with a database.
It uses Bluetooth and Jini network technology and everything is connected through
the internet. The MobiHealth project [11] is similar to WWM as it monitors a person’s health data
using small medical sensors which transmit the data via a powerful and
inexpensive wireless system. A combination of these sensors creates a body area
network (BAN), and the project utilizes cell phone networks to transmit a
signal on the fly from anywhere the network reaches.Students at Duke University [5], as part of their DELTA Smart House
design, described a system for monitoring sleeping patterns that is easy to use
and inexpensive. In order to gather detailed sleep data, they used a pulse
oximeter to record the user’s heart rate and respiratory rate, a watch style
actigraph to measure movement, in-bed thermistors for body temperature, and a microphone
for audio. Their system achieves a low cost by using multifunctional sensors,
but their choice of an actigraph adds a considerable amount to cost. Their
approach depends on a computer for data interpretation, and the sensors
themselves are not actually integrated. For instance, the watch actigraph must
be plugged into a computer to transfer data; collection is not seamless.As part of the SENSATION Project [12] researchers have put together a
system using the latest technology to detect sleep and sleepiness. They
proposed using a ring that detects heart rate and wirelessly transmits the
data, pressure sensitive film to measure chest and limb movement, a microcamera
to make sure a driver’s eyes are on the road, and (BAN) technology to have all
the parts communicate wirelessly. As  apparent by the choice of sensors, the
consortium is more focused on preventing driver from falling asleep at the
wheel than collecting data for diagnosis of sleeping disorders.Taking a different, completely noninvasive approach to sleep monitoring,
researchers at the University of Tokyo [13] have used the “surrounding sensor
approach.” Instead of placing sensors on the subject’s body, they are using
motion sensors, cameras, and microphones placed in the surrounding environment
to provide noninvasive monitoring. The downside to this approach, although it
is meant for home use, is that it is not very portable and therefore must be
semipermanent.Another approach to inexpensive sleep monitoring has been implemented by
the University of Washington Seattle with the use of multimodal sensors. As
opposed to the expensive actigraph, they investigated the possibility of using
a passive infrared camera to record motion during sleep, a decision which
carries the same consequences as the surrounding sensor approach, and may be
more difficult to setup than sensors that simply attach to the body.The last system we will review is the FPGA-based sleep apnea screening
device for home monitoring developed by researchers at the University of Cairo.
The purpose of their system is to determine whether or not a patient should
undergo a full polysomnography exam, instead of being used in place of a sleep
center. Also, differing from our system, the data is recorded on a Secure Digital
card to be processed later by the doctor.


## 9. Conclusion and Future Research

In this paper,
we have presented the details of PATHOS, a hardware- and software-based
implementation for monitoring sleeping conditions and lifestyle habits related
to sleeping conditions from the comfort of the user’s home. It has been
designed to break the field of polysomnography away from the sleep center and
bring it into the patient’s home by using wireless connectivity and existing
hardware. By doing so, we hope that a more inviting system will lead to the
diagnosis of more sleeping disorders and increase the comfort of many people.In the future, we will be finishing the implementation in order to test
the system with a real patient, collecting data and displaying output on a
handheld device. We will also be running more extensive test of the graphical
user interface. These analyses will allow us to assess the strengths and
weaknesses of our design and modify it accordingly. Additionally, we would like
to produce algorithms for calculating sleep quality scores and look at how
perceived sleep quality matches up with actual sleep quality.



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*Source: 290431-2008-04-17.xml*