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 \section{Experiment design}
 
-A real-life uncontrolled experiment using the kinect was conducted to
-test to the algorithm. 
+We conduct a real-life uncontrolled experiment using the Kinect to test to the
+algorithm.  First we discuss the signal outputs of the Kinect.  Second we
+describe the environment in which we collect the data. Finally, we interpret
+the data.
 
-\subsection{Kinect}
-
-Signals:
-\begin{itemize}
-\item audio
-\item video
-\item depth map
-\end{itemize}
-
-Skeleton fitting:
-\begin{itemize}
-\item OpenNI (calibration needed)
-\item Microsoft SDK
-\end{itemize}
+\subsection{Kinect} The Kinect outputs three primary signals in real-time: a
+color image stream, a depth image stream, and microphone output.  For our
+purposes, we focus on the depth image stream.  As the Kinect was designed to
+interface directly with the Xbox 360~\cite{xbox}, the tools to interact with it
+on a PC are limited.  Libfreenect~\cite{libfreenect} is a reverse engineered
+driver which gives access to the raw depth images from the Kinect.  This raw
+data could be used to implement the algorithms \eg of
+Plagemann~\etal{}~\cite{plagemann:icra10}.  Alternatively,
+OpenNI~\cite{openni}, a framework sponsored by PrimeSense~\cite{primesense},
+the company behind the technology of the Kinect, offers figure detection and
+skeleton fitting algorithms on top of raw access to the data streams.  However,
+the skeleton fitting algorithm of OpenNI requires each individual to strike a
+specific pose for calibration.  More recently, the Kinect for Windows
+SDK~\cite{kinect-sdk} was released, and its skeleton fitting algorithm operates
+in real-time without calibration.  Given that the Kinect for Windows SDK is the
+state-of-the-art, we use it to perform our data collection.
 
 \subsection{Environment}