my final zwei euro

tex_review/chapters/relatedwork.tex

@@ -67,8 +67,8 @@ As modern smartphones become more and more powerful, classical approaches of pat
 Nevertheless, in context of detecting activities in indoor environments, such approaches might be to much of a good thing. 
 For example, \cite{moder15} provide very promising results, but their approach requires an extensive training-phase using a set of previously recorded training data.
 Acquiring such data can not only be time-consuming, but often opens the need for a high diversity, to model multiple different movement patterns and thus prevent an overadaption of the classification to a small set of testers.  
-Because of this, we present a threshold-based activity recognition, that can cover a very general setting without much prior knowledge. 
-This is a very straightforward approach and can be found especially in early literature on activity detection using wearable sensors \cite{lee2002activity, sekine2000classification, veltink1996detection}. 
+Because of this, we present a threshold-based activity recognition, that can cover a general setting without much prior knowledge. 
+This is a very straightforward approach, what can be found especially in early literature on activity detection using wearable sensors \cite{lee2002activity, sekine2000classification, veltink1996detection}. 
 In contrast to recent state-of-the-art methods, which try to incorporate many different activities, we are only interested in finding four activities, namely standing, walking, walking up and down. 
 This limitation allows to consider the present scenario in the best possible way. 
 It should be noted, that our approach cannot necessarily keep up with the more advanced methods mentioned above, but it shows suitable results in the context of indoor localization.}

tex_review/chapters/transition.tex

@@ -112,9 +112,9 @@
 		\add{Just as before, the navigation mesh can be \emph{automatically}
 		generated from the building's floor plan, based on
 		various algorithms \cite{navMeshAlg1, kallmann2010navigation, van2011navigation}.}
-		\addy{In contrast to the grid, the number of triangles depend on the size and shape of the 		building as well as the used algorithm. 
-		Increasing the density of triangles intentionally, does not improve the accuracy, as would be 			the case for grid cells of the graph, due to aforementioned continues movement characteristic. 
-		This also removes the need of defining some kind of initial polygon density for the mesh, 			like the spacing of the grid cells, what makes it more flexible.}
+		\addy{In contrast to the graph, the number of polygons depend on the size and shape of the 		building as well as the used algorithm. 
+		Increasing the density of polygons intentionally, does not improve the accuracy, as would be 			the case for grid cells of the graph, due to aforementioned continues movement characteristic. 
+		This also removes the need of defining some kind of initial density for the mesh, 			like the spacing of the grid cells, what makes it more flexible.}
 
 	Using variably shaped/sized elements instead of rigid grid-cells
 	provides both, higher accuracy for reaching every corner, and a reduced