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ohne standing...

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toni
2018-10-20 18:14:48 +02:00
parent ce94cfc417
commit 1901e4ed47
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tex_review/chapters/experiments.tex
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@@ -409,25 +409,31 @@ Ironically, this is again some type of sample impoverishment, caused by the afor
\label{sec:eval:act}
\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.
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 \SI{28}{} measurement series as section \ref{sec:exp:loc}.}
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.}
This would bias an overall detection rate.
We have also found out, that the standing activity could hardly be recognized, because the test persons turned around while standing 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 of the method, we first determined the average (time) lag within a single walk and then shifted the calculated data accordingly.
The lag is given as the 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 measurements series results in an overall detection rate of \SI{}{\percent}, with an average lag over all walks of \SI{}{\seconds} and a standard deviation of \SI{}{}.
\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 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
%overall weg lassen... weil standing so mega schlecht ist...
standing is very bad, as most pedestrian were befohlen sich umzusehen und bilder anzusehen. und da schlägt das acc natürlich aus... hier braucht es eine besser lösung.
\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 the duration of ...}
%duration?!
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.