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first stuff activity..

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toni
2018-10-20 16:51:13 +02:00
parent 837963b4e8
commit ce94cfc417
2 changed files with 26 additions and 2 deletions
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tex_review/chapters/abstract.tex
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@@ -8,7 +8,7 @@ Instead of using time-consuming approaches like classic fingerprinting or measur
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\add{This work provides three major contributions to the system.}
\add{The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building's walkable areas.}
\add{The localization system is further updated by replacing the previous activity recognition with a threshold-based algorithm using barometer and accelerometer readings, allowing for continuous and smooth floor changes.}
\add{The localization system is further updated by incorporating a threshold-based activity recognition using barometer and accelerometer readings, allowing for continuous and smooth floor changes.}
\add{Within the scope of this work,} we tackle \del{advanced} problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures \del{, leading to a more robust localization}.
\add{For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter.}
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tex_review/chapters/experiments.tex
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@@ -408,8 +408,32 @@ Ironically, this is again some type of sample impoverishment, caused by the afor
\subsection{Activity Recognition}
\label{sec:eval:act}
\commentByToni{Wie gut ist die Activity...}
\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.
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}.}
\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.}
%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{}{}.
The single activities ....} %einzelne werte
\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?!
\add{In average, there are \SI{xx}{\percent} false detected activity changes per tested walk.
This might seem a lot, however they only had an average duration of \SI{}{\second} ($\approx$ a single barometer update).}
%Ende...
%%estimation