projets:plim:20142015:gr9
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Ci-dessous, les différences entre deux révisions de la page.
| Les deux révisions précédentesRévision précédenteProchaine révision | Révision précédente | ||
| projets:plim:20142015:gr9 [2014/11/23 15:47] – palma | projets:plim:20142015:gr9 [2014/11/23 18:08] (Version actuelle) – palma | ||
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| == Data filtering == | == Data filtering == | ||
| - | In order to remove the high frequency noise occurring on the accelerometer axis measurements in the real conditions, we implemented a Butterworth | + | In order to remove the high frequency noise occurring on the accelerometer axis measurements in the real conditions, we implemented a [[http:// |
| - | Also, walking and running activities generates a periodic pattern on the accelerometer axis data within a frequency range from 2Hz to 6Hz while resting and driving a car activities do not match any periodic pattern data on the accelerometer axis. The periodic pattern frequency feature cannot be measured in the time domain hence the use of a Fast Fourier Transform (FFT) applied on the accelerometer sensor raw (or filtered) in order to extract the pattern frequency feature. | + | Also, walking and running activities generates a periodic pattern on the accelerometer axis data within a frequency range from 2Hz to 6Hz while resting and driving a car activities do not match any periodic pattern data on the accelerometer axis. The periodic pattern frequency feature cannot be measured in the time domain hence the use of a [[http:// |
| == Features extraction == | == Features extraction == | ||
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| == Classification == | == Classification == | ||
| - | The k-means unsupervised classification algorithm has been used to cluster features into categories mapping onto user activities. It is well suited for our purpose since it is fast and we know upfront the amount of clusters which corresponds to the user activities we want to track (resting, walking, running and driving a car). | + | The [[http:// |
| The k-means clustering algorithm computes the mean value of a ten dimension vector (the ten features defined earlier) and computes the Euclidian distance in between this value and the values of each cluster mean value (cluster’s centroid). The vector is assigned to the nearest cluster (with the lowest Euclidian distance). Then, the nearest cluster’s centroid is updated to take into account the new vector it has been assigned to. | The k-means clustering algorithm computes the mean value of a ten dimension vector (the ten features defined earlier) and computes the Euclidian distance in between this value and the values of each cluster mean value (cluster’s centroid). The vector is assigned to the nearest cluster (with the lowest Euclidian distance). Then, the nearest cluster’s centroid is updated to take into account the new vector it has been assigned to. | ||
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| == Project zip file == | == Project zip file == | ||
| + | [[https:// | ||
| == Installation == | == Installation == | ||
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| Once started, you will get the following user interface: | Once started, you will get the following user interface: | ||
| - | * [[https:// | + | * [[https:// |
| This interface is mainly designed for a debug purpose. It displays real time data gathered for each measurement window on both accelerometer and GPS. Some sliders are also made available to modify some parameters (although we do not recommend to modify these parameters). | This interface is mainly designed for a debug purpose. It displays real time data gathered for each measurement window on both accelerometer and GPS. Some sliders are also made available to modify some parameters (although we do not recommend to modify these parameters). | ||
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| So, at the end of the day, you can open-up the statistics window to get a status about your daily activities: | So, at the end of the day, you can open-up the statistics window to get a status about your daily activities: | ||
| + | * [[https:// | ||
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| === PUBLICATION ==== | === PUBLICATION ==== | ||
| - | A preliminary publication of this study can be found here after. This publication would still need to be updated with real sensors results gathered from the cell phone. | + | A preliminary publication of this study can be found here after. This publication would still need to be updated with real sensors results gathered from the cell phone. |
| [[https:// | [[https:// | ||
projets/plim/20142015/gr9.1416757675.txt.gz · Dernière modification : 2014/11/23 15:47 de palma