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@@ -347,19 +347,22 @@ from the camera with similar performance.
 
 \subsection{Reducing the noise} 
 
-For the final experiment, we study what happens when the noise is reduced on
-the Kinect.  
+For the final experiment, we explore the potential of skeleton recognition with
+a higher resolution depth camera, as has been speculated for the Kinect
+2~\footnote{\url{http://www.eurogamer.net/articles/2011-11-25-kinect-2-so-accurate-it-can-lip-read}}.
+Since a higher resolution camera is not readily available, we simulate a higher
+resolution by artificially reducing the noise from our Kinect dataset.
 %Predicting potential improvements of the prediction rate of our
 %algorithm is straightforward. The algorithm relies on 9 features only.
 %\xref{sec:uniqueness} shows that 6 of these features alone are sufficient to
 %perfectly distinguish two different skeletons at a low noise level. Therefore,
 %the only source of classification error in our algorithm is the dispersion of
 %the observed limbs' lengths away from the exact measurements.
-To simulate a reduction of the noise level, the dataset is modified as
-follows: we compute the average profile of each person, and for each frame we
-divide the empirical variance from the average by 2. Formally, using
-the same notations as in Section~\ref{sec:mixture of Gaussians}, each
-observation $\bx_i$ is replaced by $\bx_i'$ defined by:
+To simulate a reduction of the noise level, the dataset is modified as follows:
+we measure the average skeletal profile of each person, and for each frame
+we divide the empirical variance from the average by 2. Formally, using the
+same notations as in Section~\ref{sec:mixture of Gaussians}, each observation
+$\bx_i$ is replaced by $\bx_i'$ defined by:
 \begin{equation}
   \bx_i' = \bar{\bx}_{y_i} + \frac{\bx_i-\bar{\bx}_{y_i}}{2}
 \end{equation}