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[projects] update of assignment text

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Jan Grewe 2014-11-01 23:36:36 +01:00
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commit a5c9365a54
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projects/project_eyetracker/eyetracker.tex
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@ -37,14 +37,15 @@ of targets that can be only learned with active gaze shifts. The eye
movements during training and test are recorded.
\begin{questions}
\question In the accompanying dataset you find five variables. (i)
\textit{gaze\_eye\_found} a logical array indicating whether the eye
was found by the tracker. (ii, iii) \textit{gaze\_x} and
\question In the accompanying dataset you find six variables. (i)
\textit{eye\_found} a logical array indicating whether the eye was
found by the tracker. (ii, iii) \textit{gaze\_x} and
\textit{gaze\_y} containing the x- and y-position of the gaze. They
relate to a screen with 1280x1024 pixel resolution. (iv)
\textit{gaze\_time} containing time stamps for each frame. (v)
\textit{marker\_time} containing the time of the last marker. All
data with the same marker-time belong to the same trial.
\textit{frame\_time} containing time stamps for each frame. (v)
\textit{marker\_time} containing the time of the last marker. (vi)
\textit{marker\_count} the count of the markers. All entries with
the same marker belong to the same trial.
\begin{parts}
\part Cut the data in chunks belonging to the same trial.
\part Characterize the eye movements statistically; eye
@ -52,8 +53,8 @@ movements during training and test are recorded.
\part Detect and correct the eye traces for instances in which the
eye was not correctly detected. Interpolate linearily in these sections.
\part Create a 'heatmap' plot that shows the eye trajectories
for one or two trials.
\part (Bonus) Use the \verb+kmeans+ clustering function to
for one or two (nice) trials.
\part Use the \verb+kmeans+ clustering function to
discriminate different types of eye-movements. Try clustering
using eye velocitiy and acceleration.
\end{parts}