fixed all todos and change requests
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@@ -183,7 +183,7 @@ The larger the window, the slower changes become noticeable and vice versa.
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Of course, the above suggested values are dependent upon the particular requirements and used sensors.
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However, they should be valid for many modern commercially available smartphones.
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\commentByFrank{hier hast du quotes um die activitites. in der intro noch nicht. vlt einheitlich machen ueber macros?}
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%\commentByFrank{hier hast du quotes um die activitites. in der intro noch nicht. vlt einheitlich machen ueber macros?}
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The activity is now evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{act}$ by providing a probability based on whether the 3D location $\mPosVec$ of the state-in-question is on a staircase, in an elevator or on the floor.
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If the current activity $\mObsActivity$ is recognized as "standing", a $\mPosVec$ located on the floor results in a probability given by $\kappa$, otherwise $1 - \kappa$.
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The same applies to "walking up" and "walking down", here a $\mPosVec$ located on one of the possible staircases or elevators provides $\kappa$ and those who remain on the floor $1 - \kappa$.
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@@ -193,7 +193,7 @@ In most cases, $\kappa = 0.75$ provides good results by remaining enough room fo
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A significant higher value like $\kappa = 0.99$ could cause the system to be stuck on a staircase or to be unable to change floors.
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\commentByToni{Wir haben im related work schon von particeln gesprochen. hier in der eval nehm ich aber wieder viel state und state-in-question. wie wollen wir es machen?}
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%\commentByToni{Wir haben im related work schon von particeln gesprochen. hier in der eval nehm ich aber wieder viel state und state-in-question. wie wollen wir es machen?}
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\commentByToni{warum wir die große treeppe so schwer ist: wlan model zieht JEDE decke ab, nicht nur die sichtbaren, weil das model einfach so gebaut wurde. }
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%\commentByToni{warum wir die große treeppe so schwer ist: wlan model zieht JEDE decke ab, nicht nur die sichtbaren, weil das model einfach so gebaut wurde. }
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