Merge branch 'master' of https://git.frank-ebner.de/FHWS/IPIN2018

tex_review/chapters/abstract.tex

@@ -8,7 +8,7 @@ Instead of using time-consuming approaches like classic fingerprinting or measur
 %
 \add{This work provides three major contributions to the system.}
 \add{The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building's walkable areas.}
-\add{The localization system is further updated by replacing the previous activity recognition with a threshold-based algorithm using barometer and accelerometer readings, allowing for continuous and smooth floor changes.}
+\add{The localization system is further updated by incorporating a threshold-based activity recognition using barometer and accelerometer readings, allowing for continuous and smooth floor changes.}
 \add{Within the scope of this work,} we tackle \del{advanced} problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures \del{, leading to a more robust localization}. 
 \add{For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter.}
 %

tex_review/chapters/experiments.tex

@@ -408,8 +408,34 @@ Ironically, this is again some type of sample impoverishment, caused by the afor
 \subsection{Activity Recognition}
 \label{sec:eval:act}
 
-\commentByToni{Wie gut ist die Activity...}
+\add{In order to evaluate the activity recognition, a test person had to press a button according to their current state of motion, namely standing, walking, stairs up, stairs down, elevator up and elevator down (cf. fig. \ref{fig:simple}). 
+As the building does not have an elevator, this state is ignored in the following. 
+Whether a state needs to be changed was indicated by small symbols on the ground truth markers. 
+This experiment is based on the same measurement series as section \ref{sec:exp:loc}.}
 
+\add{As the activity recognition uses moving windows, the detection suffers from a certain lag, depending on their size. 
+Thus, comparing each activity that is newly calculated with incoming barometer measurements with the ground truth at the current timestamp would result in a rather low detection rate for the respective activities.
+In addition, only a fraction of a test path consists of the change of an activity, since the testers were walking most of the time.  
+This would bias an overall detection rate.
+It was also investigated, that the standing activity could hardly be recognized, because the test persons constantly moved and turned around to look at the exhibits. 
+As a result, a proper evaluation of this activity could not be carried out, so we only evaluate the floor changes.} 
+%Grafik die das zeigt. 
+
+\add{In order to be able to make a statement about the quality, we first determined the average (time) lag within a single conducted walk and then shifted the calculated data accordingly. 
+The lag is given as the (absolute) difference between the timestamp, the activity changes in ground truth and the first timestamp of an interval, given by the size of $\vec{\omega}_\text{s}$, holding the same activity.  
+Applying this to the measurement series results in an overall floor detection rate of \SI{}{\percent}, with an average lag over all walks of \SI{}{\second} and a standard deviation of \SI{}{}. 
+The single activities ....} %einzelne werte
+
+\add{The main reason to utilize such a method was to detect floor changes.
+Independent of the detection rate above, the method is able to detect all floor changes of the conducted walks.
+This was quantified by comparing ob in JEDEM ground truth interval min. 75 prozent korrekt erkannte aktivitäten sind. }
+
+
+
+\add{In average, there are \SI{xx}{\percent} false detected activity changes per tested walk. 
+This might seem a lot, however they only had an average duration of \SI{}{\second} ($\approx$ a single barometer update).}
+
+%Ende...
 
 
 %%estimation

tex_review/review/03_review.txt

@@ -76,7 +76,7 @@ Ln 258 -  This equation needs revision. Should it be "p(s_i|p)  ~ N(u_i,p , std
 as this can be achieved without costly intersection tests. We also pointed out, that including walls would be more accurate, but is costly during runtime (intersection-tests).
 
 Ln 271-272: The authors mention that their WiFi fingerprinting approximation process is faster than classical fingerprinting, but they fail to provide a reference for an example of the latter or significant metrics such as the average time per square meter for fingerprinting a whole building. Furthermore, the authors should also take into account that while there are approaches where reference measurements are recorded on small grids between 1 to 2m, there are also approaches able to record reference measurements using faster methods. One example is walking by the building while registering ground truth points and using dead reckoning techniques (see Guimarães, V. et al. A motion tracking solution for indoor localization using smartphones. In Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)).
--> TODO: vielleicht den satz hier entfernen und im related work darauf hinweisen, dass es auch andere schnelle ansätze gibt? Wobei wir im related work schon [20] gecited haben, der genau das macht! vielleicht erwähnen wir seinen noch, damit er zufrieden ist? Oder wir zeigen das kleine fingerprints schneller ist als laufen? was vermutlich nicht der fall ist. 
+-> Die Kritik ist berechtigt, weswegen diese Anmerkung entfernt wurde. 
 
 Ln 275 - Equation 9 The d0 parameter of eq.8 shpuld also be presented in eq.9.
 -> Fixed in line xxx. (Is usually assumed to be 1 and thus omitted)