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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.}