less activity rec

tex/chapters/experiments.tex

@@ -20,7 +20,7 @@ When passing a marker, the pedestrian clicked a button on the smartphone applica
 Between two consecutive points, a constant movement speed is assumed.
 Thus, the ground truth might not be \SI{100}{\percent} accurate, but fair enough for error measurements.
 The approximation error is then calculated by comparing the interpolated ground truth position with the current estimation.
-Especially in the context of smoothing, it is also very interesting to exclude the temporal delay from the error calculations and measure only the positional difference between estimated and ground truth path. 
+Especially in the context of smoothing, it is also very interesting to exclude the temporal delay and measure only the positional difference between estimated and ground truth path. 
 This provides a statement about the extent to which the smoothed path superficially improves compared to the filtered one. 
 
 All walks start with a uniform distribution (random position and heading) as prior for $\mStateVec_0$. 
@@ -55,26 +55,27 @@ Walking upstairs sets $ \mu_{\text{step}} = \SI{0.4}{\meter}$, $ \sigma_{\text{s
 
 
 %kurz zeigen das activity recognition was bringt
-\begin{figure}
+%\begin{figure}
-	\input{gfx/activity/activity_over_time}
+%	\input{gfx/activity/activity_over_time}
-	\caption{The activities recognized for path 4. The misdetection in seg. 2 is cause by faulty pressure readings.}
+%	\caption{The activities recognized for path 4. The misdetection in seg. 2 is cause by faulty pressure readings.}
-	\label{fig:activityRecognition}
+%	\label{fig:activityRecognition}
-\end{figure}
+%\end{figure}
-By adding the activity recognition the approximation error of the filter decreases by an average of \SI{XX}{\centimeter} for all 4 paths.
+%By adding the activity recognition the approximation error of the filter decreases by an average of \SI{XX}{\centimeter} for all 4 paths.
-Due to this additional knowledge, the state transition samples mostly depending upon the current activity and therefore limits the possibility of false floor changes.
+%Due to this additional knowledge, the state transition samples mostly %depending upon the current activity and therefore limits the possibility of false floor changes.
-Fig. \ref{fig:activityRecognition} shows the recognized activities for path 4 using the Nexus 6. 
+%Fig. \ref{fig:activityRecognition} shows the recognized activities for path 4 using the Nexus 6. 
-Despite a short misdetection in seg. 2, caused by faulty pressure readings, the recognition can be considered to be very robust and accurate.
+%Despite a short misdetection in seg. 2, caused by faulty pressure readings, the recognition can be considered to be very robust and accurate.
 
 %Fixed Interval Smoothing
-At first, both FBS and BS are compared in context of fixed-interval smoothing.
-As a reminder, fixed-interval smoother are using all observations until time $T$ therefore run offline, after the filtering procedure is finished.
-Thus, we calculate only the positional error between estimation and ground truth, since timely information are negligible. 
 %
 \begin{figure}
 	\input{gfx/particles/particles}
 	\caption{Comparison between the filtered and FBS particle set. Both have identical positions and are recorded at the same time step on path 4. The black dot indicates the estimation using the weighted arithmetic mean of all particles. The different colours represent the current weight of a certain particle.}
 	\label{fig:particles}
 \end{figure}
+At first, both FBS and BS are compared in context of fixed-interval smoothing.
+As a reminder, fixed-interval smoother are using all observations until time $T$ therefore run offline, after the filtering procedure is finished.
+Thus, we calculate only the positional error between estimation and ground truth, since timely information are negligible. 
+%
 In contrast to BS, the FBS is not able to improve the results using the weighted arithmetic mean for estimating the current position.
 Fig. \ref{fig:particles} illustrates the filtered and smoothed particle set at a certain time step on path 4. 
 It can be seen that the estimated position (black dot) for filtering and FBS is identical although the particle weights are highly different. 

tex/chapters/introduction.tex

@@ -61,7 +61,7 @@ For example, most mobile devices restrict the \docWIFI{} module to update only e
 	\caption[An example of the occurrence of a multimodal distribution.]{
 	An example of the occurrence of a multimodal distribution.
 	At time $t-1$ the floor is separated by a wall and the distribution (coloured circle), splits apart.
-	The most likely position (green line) is estimated somewhere in-between. After a right turn, the distribution slowly starts to recover its unimodality.}
+	The approximated position (green line) is estimated somewhere in-between. After a right turn, the distribution slowly starts to recover its unimodality.}
 	\label{fig:multimodalPath}
 \end{figure}
 %
@@ -86,7 +86,7 @@ forward-backward smoothing \cite{doucet2000} and backward simulation \cite{Godsi
 
 Within this work, we investigate the benefits and drawbacks of those techniques using a conventional localisation system \cite{Ebner-16}. 
 We provide both fixed-lag and fixed-interval smoothing as well as a novel approach for incorporating them easily within the localisation procedure. 
-Additionally, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs.
+%Additionally, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs.
 The main goal is to solve the above-mentioned problems and to investigate new possibilities for even more advanced systems.
 All of our contributions are supported by an extensive experimental evaluation.
 

tex/chapters/relatedwork.tex

@@ -64,7 +64,7 @@ Since humans with a specific destination in mind do not tend to change their dir
 The herein presented approach will use two different smoothing algorithms, both implemented as fixed-interval and fixed-lag versions. 
 Further, our localisation system presented in \cite{Ebner-16} enables us to walk stairs and thus go into the third dimension.
 Therefore, a regularly tessellated graph is utilised to avoid walls, detecting doors and recognising stairs. 
-Within this work, this is additionally supported by a simple classification that detects the activities unknown, standing, walking and walking stairs. 
+This is additionally supported by a simple classification that detects the activities unknown, standing, walking and walking stairs. 
 %Finally, we incorporate prior navigation knowledge by using syntactically calculated realistic human walking paths \cite{Ebner-16}. 
 %This method makes use of the given destination and thereby provides a more targeted movement.