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fixed comments

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toni
2016-05-05 13:40:03 +02:00
parent f7b50a17ce
commit 19bca6b5b9
9 changed files with 84 additions and 166 deletions
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54
tex/chapters/filtering.tex
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@@ -1,7 +1,4 @@
%\section{Filtering}
\commentByFrank{eval und transition tauschen von der reihenfolge?}
\subsection{Evaluation}
\label{sec:eval}
@@ -89,7 +86,7 @@
directly within the transition step provides a more robust posterior distribution. Adding them to the evaluation
instead, would lead to sample impoverishment due to the used Monte Carlo methods \cite{Isard98:CCD}.
\commentByFrank{ist das verstaendlich oder schon zu kurz?}
%\commentByFrank{ist das verstaendlich oder schon zu kurz?}
%\subsubsection{Pedestrian's Destination}
We assume the pedestrian's desired destination to be known beforehand. This prior knowledge is incorporated
@@ -138,21 +135,30 @@
%\subsubsection{Activity-Detection}
Additionally we perform a simple activity detection for the pedestrian, able to distinguish between several actions
$\mObsActivity \in \{ \text{unknown}, \text{standing}, \text{walking}, \text{stairs\_up}, \text{stairs\_down} \}$.
Likewise, this knowledge is evaluated when walking the grid: Edges $\mEdgeAB$ matching the currently detected
activity are favoured using $p(\mEdgeAB)_\text{act} = 0.8$ and $0.2$ otherwise:
%
%\commentByFrank{bei mir ueberlappt aktuell nix, muessten mal testen was besser ist. beim ueberlappen ist das delay halt kuerzer. denke das schon ok.}
%
For this, the sensor signals are split in sliding windows. Each window has a length of one second and overlaps 500 ms with its prior window.
We use a naive Bayes classifier with two features. The first one is the variance of the accelerometer's magnitude within a window.
The second feature is the difference between the last and first barometer measurement of the particular window.
Based on these features the classifier assigns an activity to each of the sliding windows.
%
Similarly to the above, this knowledge is then evaluated when walking the grid: Edges $\mEdgeAB$ matching the currently detected
activity are favoured using $p(\mEdgeAB)_\text{act} = 0.8$ and $0.2$ otherwise.
If no information of the current activitiy could be obtained, no influence is exerted on the edges.
\begin{equation}
p(\mEdgeAB)_\text{act} =
\footnotesize{
\begin{cases}
1.0 & \mObsActivity = \text{unknown} \\
0.8 & \mObsActivity = \text{stairs\_up} \land \fPos{\mVertexB}_z > \fPos{\mVertexA}_z \\
0.2 & \mObsActivity = \text{stairs\_up} \land \fPos{\mVertexB}_z \le \fPos{\mVertexA}_z \\
\cdots
\end{cases}
}\enskip .
\end{equation}
\commentByFrank{das switch ist wahrscheinlich unnoetig und der text reicht}
% \begin{equation}
% p(\mEdgeAB)_\text{act} =
% \footnotesize{
% \begin{cases}
% 1.0 & \mObsActivity = \text{unknown} \\
% 0.8 & \mObsActivity = \text{stairs\_up} \land \fPos{\mVertexB}_z > \fPos{\mVertexA}_z \\
% 0.2 & \mObsActivity = \text{stairs\_up} \land \fPos{\mVertexB}_z \le \fPos{\mVertexA}_z \\
% \cdots
% \end{cases}
% }\enskip .
% \end{equation}
% \commentByFrank{das switch ist wahrscheinlich unnoetig und der text reicht}
% Activity Recognition
@@ -161,17 +167,11 @@
% Zeitintervall für das die Merkmale berechnet werden
\commentByFrank{weg mit diesem absatz? das hatte ich ja schon beschrieben}
The transition model includes a simple recognizer of different locomotion modes like normal walking or ascending/descending stairs. The reasoning behind this is to favour paths that correspond with the detected locomotion mode.
% \commentByFrank{weg mit diesem absatz? das hatte ich ja schon beschrieben}
% The transition model includes a simple recognizer of different locomotion modes like normal walking or ascending/descending stairs. The reasoning behind this is to favour paths that correspond with the detected locomotion mode.
\commentByFrank{bei mir ueberlappt aktuell nix, muessten mal testen was besser ist. beim ueberlappen ist das delay halt kuerzer. denke das schon ok.}
\commentByFrank{satzreihenfolge war komisch -> angepasst}
For this, the sensor signals are split in sliding windows. Each window has a length of one second and overlaps 500 ms with its prior window.
\commentByFrank{navies: naive?}
We use a Naives Bayes classifier with two features. The first one is the variance of the accelerometer's magnitude within a window.
The second feature is the difference between the last and first barometer measurement of the particular window.
Based on these features the classifier assigns an activity to each of the sliding windows.
%\todo{Was passiert wenn ein überlappendes Fenster zwei verschiedene Aktivitäten zugewiesen bekommt? Sliding windows evtl. weglassen?}