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78 lines
3.7 KiB
78 lines
3.7 KiB
\section{System Architecture}
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\label{sec:design}
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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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data from Grideye sensors into a low-resolution image, since the resolution of a
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single Grideye sensor too low to make a decision. 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, to reduce the noise and effect cause by
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the residual heat on bed after turning over. 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}[tbp]
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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 then merge them dependence on the distance between thermal-box and
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bed, distance between sensors and the FOV of Grideye sensor. In our case, $D_B$ is
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150 cm, and $D_s$ is 10 cm.
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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{Turning Over Determination}
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We train a SRCNN model by the fused Grideye image and downscaled Lepton 3 image,
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and use it to enhance all following Grideye frames to SR frames. We labeled some SR frames
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into two categories, lay on back and 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 turn 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. Then, 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
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lower bound, and regrad it as the trend of the pose changing. Figure~\ref{fig:trend}
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shows the filitered 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, or the trend is going up, it is a lay on back. If it is at the
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bottom one fifth, or the trend is going down, it is a lay on side. If there are three
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continuously same poses, and different from the last turning over, it will be count as
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another turning over.
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\begin{figure}[tbp]
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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_2.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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