\section{Method name}
\label{sec:design}

\subsection{System Architecture}
We designed a thermal-box to collect the data. It has four Grideye sensors on the
corners of a 10 cm square and a Lepton 3 at the central. Our method is made by
three parts. The first part is to train the SRCNN model with fused Grideye image
as low-resolution and downscaled Lepton 3 image. The second part, we use the
Super-resolution image to train a neural network model to recognize current pose
is lay on back or lay on side. The third part, because of noise and the residual
heat on bed after turn over, it is difficult to figure out the current pose. We
remove the noise by median filter, and determine the current pose according to
the trend of the possibility from recognition network.

\subsection{Grideye Data Fusion}

On the thermal-box, there are four Grideye sensors. At the beginning, we let
the thermal-box faces to an empty bed and records the background temperature.
All the following frames will subtract this background temperature. After that,
we resize four $8 \times 8$ Grideye images to $64 \times 64$ by bilinear
interpolation and than merge them dependence on the distance between thermal-box and
bed, width of sensor square and the FOV of Grideye sensor.

\begin{enumerate}
\item $D_b$ is the distance between bed and thermal-box.
\item $D_s$ is the width of sensor square also the distance between adjacent sensors.
\item $F$ is the FOV of Grideye sensor which is about 60 degree.
\item $Overlap = 64 - 64 \times (\frac{D_s}{2 \times D_b \times tan(\feac{F}{2})})$
\end{enumerate}