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76 lines
3.6 KiB
76 lines
3.6 KiB
\section{Method name}
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\label{sec:design}
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\subsection{System Architecture}
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We designed a thermal-box to collect the data. It has four Grideye sensors on the
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corners of a 10 cm square and a Lepton 3 at the central. Figure~\ref{fig:method} shows
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the system of our method. It consists four parts. The first part is to fuse multiple
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Grideye image, since the resolution of singel Grideye sensor only has 64 pixels. The
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second part, we train the SRCNN model with fused Grideye image
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as low-resolution and downscaled Lepton 3 image as high-resolution image.
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The third part, we use the
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Super-resolution image to train a neural network model for recognizing current pose
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is lay on back or lay on side. The last part, because of noise and the residual
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heat on bed after turn over, it is difficult to figure out the current pose. We
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remove the noise by median filter, and determine the current pose according to
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the trend of the possibility from recognition network.
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\begin{figure}[htb]
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\begin{center}
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\includegraphics[width=1\linewidth]{figures/method.pdf}
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\caption{Illustration of Proposed Method.}
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\label{fig:method}
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\end{center}
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\end{figure}
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\subsection{Grideye Data Fusion}
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On the thermal-box, there are four Grideye sensors. At the beginning, we let
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the thermal-box faces to an empty bed and records the background temperature.
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All the following frames will subtract this background temperature. After that,
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we resize four $8 \times 8$ Grideye images to $64 \times 64$ by bilinear
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interpolation and than merge them dependence on the distance between thermal-box and
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bed, width of sensor square and the FOV of Grideye sensor.
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\begin{enumerate}
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\item $D_b$ is the distance between bed and thermal-box.
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\item $D_s$ is the width of sensor square also the distance between adjacent sensors.
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\item $F$ is the FOV of Grideye sensor which is about 60 degree.
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\item $Overlap = 64 - 64 \times (\frac{D_s}{2 \times D_b \times tan(\frac{F}{2})})$
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\end{enumerate}
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\subsection{Pose determination}
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We train a SRCNN model by the fused Grideye data and downscaled Lepton 3 image,
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and use it to upscale all following frames to SR frames. We labeled some SR frames
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to two categories. One is lay on back and the other is lay on side. Since the input
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data is very small, we use a neural network consist one 2D convolution layer, one
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2D max pooling, one flatten and one densely-connected layer. The possibility of
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output has a very large various just after turning over because the model cannot
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distinguish the residual heat on bed and the person as Figure~\ref{fig:residual_heat} shown. This
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situation will slowly disappear after one or two minutes.
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To determination the pose, first we use a median filter with a window size of five
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to filter out the noise. Than, find the curve hull line of the upper bound and
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lower bound of the data. Finally calculate the middle line of upper bound and lower bound.
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Figure~\ref{fig:trend} shows the data and these lines.
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We divide every data into 10 second time windows. If the middle line of the time window
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is at the top one fifth, it is a lay on back. If it is at the bottom one fifth,
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it is a lay on side. If the trend of line is going up, it is lay on back. Otherwise, it
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is lay on side. To guarantee the confidence of result, we will only trust the pose if
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there are three continuously same output.
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\begin{figure}[ht]
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\centering
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\minipage{0.3\columnwidth}
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\includegraphics[width=\linewidth]{figures/Lepton_residual_heat.png}
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\caption{Residual heat on bed.}
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\label{fig:residual_heat}
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\endminipage
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\minipage{0.65\columnwidth}
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\includegraphics[width=\linewidth]{figures/MinMax.pdf}
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\caption{Trend of pose.}
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\label{fig:trend}
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\endminipage
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\end{figure}
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