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- \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 $Overlap = 64 - 64 \times (\frac{D_s}{2 \times D_b \times tan(\frac{F}{2})})$
- \end{enumerate}
-
- \subsection{Pose determination}
-
- We train a SRCNN model by the fused Grideye data and downscaled Lepton 3 image,
- and use it to upscle all following frames to SR frames. We lebeled some SR frames
- to two categories. One is lay on back and the other is lay on side. Since the input
- data is very small, we use a neural network consist one 2D convolution layer, one
- 2D max pooling, one flatten and one densely-connected layer. The possibility of
- output has a very large various just after turning over because the model cannot
- distinguish the residual heat on bed and the person as Figure???? showen. This
- situation will slowly disappear after one or two minutes.
-
- To determination the pose, first we use a median filter with a window size of five
- to filter out the noise. Than, find the curvex hull line of the upperbound and
- lowerbound of the data. Finally calculate the middle line of upperbound and lowerbound.
- Figure??? and ??? shows the data and these lines.
-
- We divide every 10 seconds data into a time window. If the middle line of the time window
- is at the top one fifth, it is a lay on back. If it is at the bottom one fifth,
- it is a lay on side. If the trend of line is going up, it is lay on back. Otherwise, it
- is lay on side.
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