Update design.

git-svn-id: http://newslabx.csie.ntu.edu.tw/svn/Ginger@74 5747cdd2-2146-426f-b2b0-0570f90b98ed

trunk/RTCSA_SS/03Design.tex

@@ -1,17 +1,17 @@
-\section{Method name}
+\section{System Architecture}
 \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. Figure~\ref{fig:method} shows
 the system of our method. It consists four parts. The first part is to fuse multiple
-Grideye image, since the resolution of single Grideye sensor only has 64 pixels. The
+data from Grideye sensors into a low-resolution image, since the resolution of a
+single Grideye sensor too low to make a decision. The
 second part, we train the SRCNN model with fused Grideye image
 as low-resolution and downscaled Lepton 3 image as high-resolution image.
 The third part, we use the
 Super-resolution image to train a neural network model for recognizing current pose
-is lay on back or lay on side. The last part, because of noise and the residual
-heat on bed after turn over, it is difficult to figure out the current pose. We
+is lay on back or lay on side. The last part, to reduce the noise and effect cause by
+the residual heat on bed after turning over. We
 remove the noise by median filter, and determine the current pose according to
 the trend of the possibility from recognition network.
 
@@ -30,7 +30,8 @@ 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 then merge them dependence on the distance between thermal-box and
-bed, width of sensor square and the FOV of Grideye sensor.
+bed, distance between sensors and the FOV of Grideye sensor. In our case, $D_B$ is
+150 cm, and $D_s$ is 10 cm.
 
 \begin{enumerate}
 \item $D_b$ is the distance between bed and thermal-box.
@@ -39,27 +40,28 @@ bed, width of sensor square and the FOV of Grideye sensor.
 \item $Overlap = 64 - 64 \times (\frac{D_s}{2 \times D_b \times tan(\frac{F}{2})})$
 \end{enumerate}
 
-\subsection{Pose determination}
+\subsection{Turning Over Determination}
 
-We train a SRCNN model by the fused Grideye data and downscaled Lepton 3 image,
-and use it to upscale all following frames to SR frames. We labeled some SR frames
-to two categories. One is lay on back and the other is lay on side. Since the input
+We train a SRCNN model by the fused Grideye image and downscaled Lepton 3 image,
+and use it to enhance all following Grideye frames to SR frames. We labeled some SR frames
+into two categories, lay on back and 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
+output has a very large various just after turn over because the model cannot
 distinguish the residual heat on bed and the person as Figure~\ref{fig:residual_heat} shown. 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. Then, find the curve hull line of the upper bound and
-lower bound of the data. Finally, calculate the middle line of upper bound and lower bound.
-Figure~\ref{fig:trend} shows the data and these lines.
+lower bound of the data. Finally, calculate the middle line of upper bound and
+lower bound, and regrad it as the trend of the pose changing. Figure~\ref{fig:trend}
+shows the filitered data and these lines.
 
 We divide every data into 10 second time windows. 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. To guarantee the confidence of result, we will only trust the pose if
-there are three continuously same output.
+is at the top one fifth, or the trend is going up, it is a lay on back. If it is at the
+bottom one fifth, or the trend is going down, it is a lay on side. If there are three
+continuously same poses, and different from the last turning over, it will be count as
+another turning over.
 
 \begin{figure}[ht]
   \centering