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Merge branch 'master' of https://git.frank-ebner.de/toni/IPIN2016

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kazu
2016-04-23 21:27:39 +02:00
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tex/chapters/filtering.tex
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@@ -140,6 +140,7 @@
\subsection{Activity-Detection}
Additionally we perform a simple activity detection for the pedestrian, able to distinguish between
standing, walking, walking stairs upwards and downwards. Likewise, this knowledge
is evaluated when walking the grid: Edges $\mEdgeAB$ matching the currently detected
@@ -159,3 +160,18 @@
% Activity Recognition
% Naives Bayes als Klassifikator
% Features -> 1: Variance of mean 2: Differenz zwischen Barometer
% Zeitintervall für das die Merkmale berechnet werden
The transition model includes a simple recognizer of different locomotion modes like normal walking or ascending/descending stairs. The reasoning behind this is to favour paths that correspond with the detected locomotion mode.
We use a Naives Bayes classifier with two features. For this, the sensor signals are split in sliding windows. Each window has a length of one second and overlaps 500 ms with its prior window.
The first feature is the variance of the accelerometer's magnitude during a window and the second feature is the difference between the last and first barometer measurement of the particular window.
Based on these features the classifier assigns an activity to each sliding window.
\todo{Was passiert wenn ein überlappendes Fenster zwei verschiedene Aktivitäten zugewiesen bekommt? Sliding windows evtl. weglassen?}