diff --git a/Report/Images/ConfusionMatrix.png b/Report/Images/ConfusionMatrix.png
new file mode 100644
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diff --git a/Report/main.aux b/Report/main.aux
index 75752b25d54d897ed2e871568cf4b0a613b14004..ea7846c148e14870b0d0ba43f3dceef4e3d08294 100644
--- a/Report/main.aux
+++ b/Report/main.aux
@@ -28,21 +28,27 @@
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\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {2}Technical Approach}{1}{section.2}}
\newlabel{sec:technical}{{2}{1}{Technical Approach}{section.2}{}}
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-\newlabel{sec:alarm}{{2.1}{1}{Alarm Clock}{subsection.2.1}{}}
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-\newlabel{sec:model}{{2.2}{1}{AI Model}{subsection.2.2}{}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Alarm Clock}{2}{subsection.2.1}}
+\newlabel{sec:alarm}{{2.1}{2}{Alarm Clock}{subsection.2.1}{}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}AI Model}{2}{subsection.2.2}}
+\newlabel{sec:model}{{2.2}{2}{AI Model}{subsection.2.2}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {3}Implementation Details}{2}{section.3}}
\newlabel{sec:implementation}{{3}{2}{Implementation Details}{section.3}{}}
-\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Alarm Clock Class}{2}{subsection.3.1}}
+\abx@aux@cite{WithingReview}
+\abx@aux@segm{0}{0}{WithingReview}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Alarm Clock Class}{3}{subsection.3.1}}
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-\newlabel{sec:future}{{4.4}{3}{Future Work}{subsection.4.4}{}}
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+\newlabel{fig:matrix}{{1}{4}{The above is a confusion matrix provided by~\cite {WithingReview}}{figure.1}{}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {4}Results}{4}{section.4}}
+\newlabel{sec:results}{{4}{4}{Results}{section.4}{}}
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+\newlabel{sec:outcome}{{4.1}{4}{Outcomes}{subsection.4.1}{}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Issues}{4}{subsection.4.2}}
+\newlabel{sec:issue}{{4.2}{4}{Issues}{subsection.4.2}{}}
+\abx@aux@refcontextdefaultsdone
+\abx@aux@defaultrefcontext{0}{WithingReview}{nty/global//global/global}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Takeaways}{5}{subsection.4.3}}
+\newlabel{sec:takeaway}{{4.3}{5}{Takeaways}{subsection.4.3}{}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Future Work}{5}{subsection.4.4}}
+\newlabel{sec:future}{{4.4}{5}{Future Work}{subsection.4.4}{}}
diff --git a/Report/main.bbl b/Report/main.bbl
index 28c7383bbfd6fa7e2cf5708a2ef83b0c0249b12b..6da3eb9429512c6cb736712bce293359bd07f60a 100644
--- a/Report/main.bbl
+++ b/Report/main.bbl
@@ -16,5 +16,41 @@
{}
\endgroup
+
+\refsection{0}
+ \datalist[entry]{nty/global//global/global}
+ \entry{WithingReview}{online}{}
+ \name{author}{1}{}{%
+ {{hash=fb3372b58ee6d1751d393d15c0b8f4ab}{%
+ family={Horst},
+ familyi={H\bibinitperiod},
+ given={Robter},
+ giveni={R\bibinitperiod}}}%
+ }
+ \list{organization}{1}{%
+ {Youtube, The Quantified Scientist}%
+ }
+ \strng{namehash}{fb3372b58ee6d1751d393d15c0b8f4ab}
+ \strng{fullhash}{fb3372b58ee6d1751d393d15c0b8f4ab}
+ \strng{bibnamehash}{fb3372b58ee6d1751d393d15c0b8f4ab}
+ \strng{authorbibnamehash}{fb3372b58ee6d1751d393d15c0b8f4ab}
+ \strng{authornamehash}{fb3372b58ee6d1751d393d15c0b8f4ab}
+ \strng{authorfullhash}{fb3372b58ee6d1751d393d15c0b8f4ab}
+ \field{sortinit}{H}
+ \field{sortinithash}{5f15a7bc777ad49ff15aa4d2831b1681}
+ \field{labelnamesource}{author}
+ \field{labeltitlesource}{title}
+ \field{title}{Withings Sleep Analyzer: Science Test (2021 Review)}
+ \field{year}{2021}
+ \field{dateera}{ce}
+ \verb{urlraw}
+ \verb https://www.youtube.com/watch?v=Nh2J_cP-C0E
+ \endverb
+ \verb{url}
+ \verb https://www.youtube.com/watch?v=Nh2J_cP-C0E
+ \endverb
+ \endentry
+ \enddatalist
+\endrefsection
\endinput
diff --git a/Report/main.bcf b/Report/main.bcf
index 76c28be1a7bf69bba3f0a095684ceeed4dea0b14..86a26774be138ee05aa1824f300dbd56689ad5b5 100644
--- a/Report/main.bcf
+++ b/Report/main.bcf
@@ -1974,6 +1974,7 @@
<bcf:datasource type="file" datatype="bibtex">main.bib</bcf:datasource>
</bcf:bibdata>
<bcf:section number="0">
+ <bcf:citekey order="1">WithingReview</bcf:citekey>
</bcf:section>
<!-- SORTING TEMPLATES -->
<bcf:sortingtemplate name="nty">
diff --git a/Report/main.bib b/Report/main.bib
index 4fa87223d872ef2d3a24864ccb226ce9a2ccae33..d73e8a3cc318af2b03deca40c7b63685b19ca640 100644
--- a/Report/main.bib
+++ b/Report/main.bib
@@ -1,5 +1,5 @@
%%%%%%%%%%%%%%%%%%%%%
-@online{Withing-Review,
+@online{WithingReview,
title = {Withings Sleep Analyzer: Science Test (2021 Review)},
date = {2021},
organization = {Youtube, The Quantified Scientist},
diff --git a/Report/main.blg b/Report/main.blg
index 80a543110429f6461576cf21ade921231df8d5f5..6d8d2afe7f7addc2f838e8795db4d569f4e84138 100644
--- a/Report/main.blg
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@@ -1,9 +1,15 @@
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-[133] Biber.pm:857> INFO - Found 0 citekeys in bib section 0
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-[149] bbl.pm:720> INFO - Output to main.bbl
-[150] Biber.pm:110> INFO - WARNINGS: 1
+[1] Config.pm:305> INFO - Logfile is 'main.blg'
+[42] biber-darwin:313> INFO - === Sat May 14, 2022, 04:56:26
+[63] Biber.pm:371> INFO - Reading 'main.bcf'
+[147] Biber.pm:857> INFO - Found 1 citekeys in bib section 0
+[161] Biber.pm:3976> INFO - Processing section 0
+[173] Biber.pm:4136> INFO - Looking for bibtex format file 'main.bib' for section 0
+[175] bibtex.pm:1500> INFO - LaTeX decoding ...
+[194] bibtex.pm:1316> INFO - Found BibTeX data source 'main.bib'
+[207] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
+[207] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
+[208] Biber.pm:3804> INFO - Sorting list 'nty/global//global/global' of type 'entry' with template 'nty' and locale 'en-US'
+[208] Biber.pm:3810> INFO - No sort tailoring available for locale 'en-US'
+[213] bbl.pm:617> INFO - Writing 'main.bbl' with encoding 'UTF-8'
+[213] bbl.pm:720> INFO - Output to main.bbl
diff --git a/Report/main.fdb_latexmk b/Report/main.fdb_latexmk
index adb692d54af0156e9268e7b96df1c5d09dd4b0c0..ed4b11640aed574315603d6007c1c5d2ba13b3be 100644
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@@ -1,28 +1,38 @@
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"main.blg"
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- "/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/Report/main.tex" 1652523039 8093 e1faf1d0fc48ebe5265acc9db11ef7d7 ""
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"/usr/local/texlive/2018/texmf-dist/fonts/vf/adobe/times/ptmr7t.vf" 1480098758 1380 0ea3a3370054be6da6acd929ec569f06 ""
+ "/usr/local/texlive/2018/texmf-dist/fonts/vf/adobe/times/ptmrc7t.vf" 1480098758 1948 7330aeef3af211edff3b35fb2c12a0fd ""
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"/usr/local/texlive/2018/texmf-dist/tex/latex/psnfss/omlztmcm.fd" 1480098837 411 12564a37a279e4e0b533cdf5e03eeb7c ""
+ "/usr/local/texlive/2018/texmf-dist/tex/latex/psnfss/omsptm.fd" 1480098837 576 95c2c0baea9c719a4be29a3d4122498b ""
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index 43190284fdb592279f5a3e46b9812cf586e372e3..a391e8870e5fb320016547622ce76bbcf3061bed 100644
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@@ -26,37 +26,64 @@
\maketitle
+
\section{Introduction}\label{sec:intro}
+
\subsection{Topic}\label{sec:topic}
The goal for this project is to create an alarm clock that takes into account a user's sleep cycle.
This will allow the alarm to be more effective as it will be capable of waking up the user at during moments close to wakefulness.
\subsection{Motivation}\label{sec:motive}
Sleep is an essential activity we ideally spend on every night.
-However, with the multitude of stress that occur in modern life, it can be hard to get adequate sleep.
-This can lead to a build up of sleep debt and compound health issues.
-There are many avenues for tackling the problem of sleep deprivation, from creating an application for developing health sleep hygiene to [].
+However, with the multitude of attention and stresses that occur in modern life, it can be hard to get adequate sleep.
+This can lead to a build up of sleep debt and compound health issues, particularly cognitive impairments.
+There are many avenues for tackling the problem of sleep deprivation, from habit development apps to blocking timeout that stops the user from using their smart devices after some time.
The pathway I choose to focus on is augmenting the capabilities of alarm clocks.
The alarm clock is a simple device that is designed to wake up the user at a set time.
-Within this simplicity there is a problem, which is that [].
-The problems with traditional alarm clocks are that they don't alway follow the user's [].
+Within this simplicity there is a problem though, which is that alarm clocks have no idea about the user to wake up.
+Traditional alarm clocks only have a way to set an alarm time, and maybe a snooze option, which prevents them from being fully tailored to a user.
+What we would ideally want is for the alarm clock to know exactly what stage of.
+This can allow the alarm to more gently wake the user up and can help them rely less on snoozing the alarm.
+
There have been application that have applied similar, such as Sleep Cycle for iOS and Android.
My application focuses more on brining this functionality to non-smartphone devices by using the Withing Sleep Mat and Raspberry Pi.
+
\section{Technical Approach}\label{sec:technical}
The overall approach to the project can be split into two primary parts: the alarm clock and the AI model.
The alarm clock functionality should be provided by a simple script that can be run by the Raspberry Pi.
-For the AI model, the initial hope was to have the device work on .
+For the AI model, the initial hope was to have the Raspberry Pi host the model and do the calculation from itself.
+I shifted to using my laptop instead do to the application run (see Section~\ref{sec:issue}).
+There was also once a third part to the project, dealing with the coding for the Withing API, but that was dropped in favor of focusing primarily on training the models on the data.
+This meant that rather than have the device load the data from the API, I just downloaded the data directly and used that instead.
+
+
\subsection{Alarm Clock}\label{sec:alarm}
-The first is the alarm clock script.
-This is a Python script which handles getting the user [].
+The alarm clock is a Python script to handles taking user information and determining the best time to sound the alarm.
+To apply this, I modeled the alarm clock as a Finite State Machine (FSM) class with extendable subclasses.
+This allowed me to modularize the code for the alarm clock and easily slot in the model later on.
+
\subsection{AI Model}\label{sec:model}
-The model for this project was trained on Withing Sleep data [].
+The model for this project was trained on Withing Sleep data.
+The general flow of the model works as follows:
+\begin{enumerate}
+ \item Initialize the model's memory, which holds a Deque (double-ended queue) of numpy arrays (each containing information like current time, minutes since beginnning, and the probability of each sleep state),
+ \item Add the current timestep and previous prediction to the model's memory,
+ \item If the Deque is not full filled, repeat step 2 (this warms up the memory to prevent the model from reading in variable length data),
+ \item Pass the current memory to the model and calculate the next prediction,
+ \item Repeat back to the step 2.
+\end{enumerate}
+
I primarily focused on developing models that had recurrent cells, such as Long Short-Term Memory (LSTM) models, Gated Recurrent Unit (GRU) models, and attention-based mechanism models.
-As a .
-Since the data was simple, I also looked at different baseline models such as Markov Chains and Feed-forward networks without recurrence.
+Generally, Recurrent Neural Networks (RNNs) are useful model when fitted to time-series data, as the structure is able to keep some memory of prior events.
+
+I also looked at different baseline models such as Markov Chains and Feed-forward networks without recurrence.
+This would allow me to verify if recurrence was able to help with the mode.
+To control for variance among all the trained models, and to work within a constrained environment, I only allowed the model to have a single hidden layer with no more than 32 units.
+While this is very constrained, the data was simple enough that the models were able to be trained to a reasonable degree.
+
\section{Implementation Details}\label{sec:implementation}
Below is a list of external Python libraries I used in the project:
@@ -72,7 +99,7 @@ Below is a list of external Python libraries I used in the project:
On the hardware, I did primary development and testing on my MacBook Pro Laptop.
I also attempted to get this project working on the Raspberry Pi, but I ran into some issues discussed more in Section~\ref{sec:issue}.
-During development, [].
+
\subsection{Alarm Clock Class}
For the alarm clock itself, I represent that as a Finite State Machine (FSM).
This was developed in three scripts: alarm.py, simple\_alarm\_clock.py, and smart\_alarm\_clock.py.
@@ -87,20 +114,41 @@ In alarm.py, the file contained an enum with 5 states:
The file also contains the BaseAlarmClock, implements the state changing functionality and holds the reference to the wake-up time.
Next, the SimpleAlarmClock in simple\_alarm\_clock.py extends the BaseAlarmClock, adding functionality for keyboard inputs, providing print messages for alarm clock state changes, promoting the user for information about what time to wake up.
It also adds a function called `simple\_alarm\_mode', which handles running the alarm clock from start to end.
-Moving into the smart\_alarm\_clock.py script, the code structure for is kept mostly the same as simple\_alarm\_clock.py.
-added two additional part:
+Moving into the smart\_alarm\_clock.py script, the code structure for is kept mostly the same as simple\_alarm\_clock.py, primarily the getting user input and the flow of the data from alarm activation to deactivation.
+Beyond that, I added two new sections of code:
\begin{enumerate}
- \item a function for loading and running the model,
- \item and a VLC instance for playing sound from the script.
+ \item a function for loading and running the model (includes the softmax and model prediction helper functions),
+ \item and a VLC instance for playing sound from the script (this allowed it to sound like an regular alarm clock).
\end{enumerate}
+
\subsection{AI Models}
-I used a Jupyter Notebook for [].
+Before going into the AI models themselves, I will first discuss how the data was created.
+The initial data came from Withing Sleep Mat.
+This had about 3 years of data, with each sleep event getting the time started (in UTC time), the stages, and the duration.
+There was also extra data on heart rate and respiration, however I did not use them for the current model training.
+From this, I extracted the data into a minute-by-minute format.
+I also derived some more information, such as how many minutes the data point was from the start.
+\begin{figure}[h]
+ \centering
+ \includegraphics[width=0.75\textwidth]{Images/ConfusionMatrix.png}
+ \caption{The above is a confusion matrix provided by~\cite{WithingReview}.}\label{fig:matrix}
+\end{figure}
+One additional step I took was adding random noise to the sleep data, which I used to make the models more resilient to noisy data (See Figure~\ref{fig:matrix}).
+After the data is organized, I have a class called WindowGenerator that splits the data into training, validation, and testing datasets, as well as pushing the data into a time-series format, where the input is a specified chunck of time, and the output is the next timestep.
+
+On to the models, for experimenting, I used a Jupyter Notebook to create the models and play around with setting up the parameters.
+During the experiment, I look at how good I can make the model with as few parameters.
+Once each of the models were finished experimenting with, I pushed them into their own Python script to allow a more parallel way of training.
+Once trained, the model and its training+validation history is saved.
+As a check, I also evaluate it on the testing data.
\section{Results}\label{sec:results}
\subsection{Outcomes}\label{sec:outcome}
-Based on the training results from the Jupyter Notebook, .
+Based on the training results from the Jupyter Notebook, all the RNN-variant models were very close in terms of categorical loss entropy and accuracy.
+Based on this, I used the attention-based RNN model for the main model for testing, as it had the least number of training parameters compared to units, which made the fastest to train on the data.
+
\subsection{Issues}\label{sec:issue}
One slight issue I ran into for the AI model was the inability for the model to work load properly on the Raspberry Pi.
@@ -120,17 +168,25 @@ I did try to see if running OMXPlayer from the Python subprocess would work, but
These issues prevented me from running the working application on the Raspberry Pi.
Had I more time to debug the issues, I may have gotten the TF model and VLC player to work on the Pi.
+
+
\subsection{Takeaways}\label{sec:takeaway}
+Some key takeaways from this project are:
+\begin{itemize}
+ \item Simple models for simple data
+ \item Work on machines as close to the target device as possible
+ \item Allow the data to be randomized to make models more resistant to noise, especially if that is fed back into the model.
+\end{itemize}
\subsection{Future Work}\label{sec:future}
In this final discussion, I will discuss where the project can go from here.
-Given additional time, the first priority would be on getting the application to successful work on the Raspberry Pi.
-The tools and scripts are there for it to work, with the current issue just being able to fix bugs with the setup process.
+Given additional time, the first priority would be on getting the application to successful work from start to end on the Raspberry Pi.
+The tools and scripts are there for it to work, with the current issue just being able to fix bugs during the setup process.
The next extension on that would be to connect up the Withing API using Withing Python.
While there isn't a way to stream sleep data as it is being collect, I can still use it to gather the sleep data after the user wakes up.
This can allow the model to be trained on new data daily.
-I can also hook into the notification system provided by the API for user in-bed and user out-of-bed to automate starting and stopping the alarm.
-After that, there might be ways to extend it to other devices which provide sleep data tracking, such as FitBit, as well as move the AI computation to the edge.
+Additionally, I can also hook into the notification system provided by the API for user in-bed and user out-of-bed to automate starting and stopping the alarm.
+Moving to a more general idea, there might be ways to extend it to other devices which provide sleep data tracking, such as FitBit, as well as move the AI model to the edge computing, which can allow for more complex and larger models.
\printbibliography