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toni
2018-10-21 14:42:47 +02:00
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tex_review/chapters/experiments.tex
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@@ -65,7 +65,7 @@ This took \SI{85}{\minute}, as all measurements were conducted using the same sm
The optimized Wi-Fi model and the mesh can be created automatically within a negligible amount of time directly on the smartphone, which then enables the pedestrian to start the localization.
Of course, for the experiments conducted below several additional knowledge was obtained to evaluate the quality of the proposed methods and the overall localization error.
Thus the above provided times were measured for a pure localization installation, as for example a customer would order, while the experiments were performed in a 2-day period.
Nevertheless, we believe that an on-site setup-time of less than \SI{120}{\minute} is a big step for the practicability of localization systems. \commentByMarkus{big step is komisch}
Nevertheless, we believe that an on-site setup-time of less than \SI{120}{\minute} improves the practicability of the localization system, especially in commercial scenarios.
In addition, the above steps do not require a high level of thoroughness in their execution or special knowledge about the details of the system, which should also allow unbiased persons to set up the system.}
\begin{figure}[t]
@@ -161,7 +161,6 @@ Due to the included sensor noise, they covered a too short distance for several
Going straightforward to \SI{180} steps, this phenomenon has multiplied for the graph (cf. fig. \ref{fig:transitionEval:d}), but not for the mesh (cf. fig. \ref{fig:transitionEval:c}).
This is due to the above-mentioned strategy for the mesh.
Compared to this approach, the graph is not able to remove any particles and thus they walk according to the recognized steps and heading changes, even if they theoretically hit a wall several times.
The resulting effects are obvious. \commentByMarkus{ausformulieren was hier obvious ist}
After walking up and down twice, several particle groups have formed, which no longer allows an accurate position estimation.
Of course, a similar strategy could be developed for a graph.
@@ -453,9 +452,8 @@ As a result, they often turned around or a took a few small steps within the sta
In addition, using only acceleration for detection might be a bad choice in the first place, as moving the phone, e.g. by putting it in the trouser pocket, will exceed the threshold.
At the end, this leads to the general question, on how to define standing.
Is it a complete standstill or should it allow for a certain degree of freedom?
The answer is always the same, it depends.
As for this museum scenario, the results for detecting the standing activity are not satisfying and a more advanced approach should be considered.}
\commentByMarkus{Letzten zwei Sätze wissenschaftlicher machen}
The answer of this question often depends on the respective scenario.
As for the museum, in which visitors often stand in front of exhibits or only move within a small area, the results for detecting the standing activity are not sufficient and a more advanced approach should be considered.}
\add{In contrast, the detection rates for walking up or down are clearly better.
With only a single exception in walk 3 (cf. chapter \ref{sec:exp:loc}), the approach makes it possible to direct particles smoothly over stairs.