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This dataset was used in [1] and donated by the authors of that
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paper. Accelerometer data was collected on a smartphone installed
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inside a vehicle using a flexible suction holder near the
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dashboard. An expert was responsible for driving the vehicle while
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the device ran an Android application called Asfault [2], developed
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specifically to store the current asphalt condition continuously
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over time. Asfault stores the time-stamp of the collected data,
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acceleration forces in along the three physical axes, latitude,
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longitude, and velocity.
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The acceleration forces are given by the accelerometer sensor of
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the device and are the data used for the classification task.
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Latitude, longitude, and velocity are given by the GPS and are used
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to locate the collected/classified data. Each one of this
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information is a different continuous time series. A sampling rate
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of 100 Hz was used for each time series. It is important to note
|
that the Android system does not guarantee a perfect precision on
|
the interval between readings. This means that when we choose a
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sampling rate of 100 Hz, the device should output between 95 and
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105 observations per second.
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For this data, the (X,Y,Z) acceleration time series are converted
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into a univariate time series that represents the acceleration
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magnitude.
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In order to obtain labeled data, the Asfault application allows the
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expert to inform the pavement condition before collecting the data.
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To guarantee the integrity of data, Asfault also records videos of
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the road over the data collection using the built-in camera. Thus,
|
it is possible to perform the analysis of these videos to confirm
|
the class labels assigned by the expert.
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The datasets were collected in the Brazilian cities of Sao Carlos,
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Ribeirao Preto, Araraquara, and Maringa using a medium sized
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hatchback car (Hyundai i30) and two different devices (Samsung
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Galaxy A5 and Samsung S7).
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The problem AsphaltObstacles involves the identification of four
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common obstacles in the region of data collection. It has the
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following class labels: raised_crosswalk (160 cases);
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raised_markers (187 cases); speed_bump (212 cases); and
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vertical_patch (222 cases). Data is variable length, minimum 111
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observations, maximum 736. The data is split randomly into 50/50
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default train series. The data can be resampled without bias.
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The best result for the Asphalt-Obstacles dataset was achieved by
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DTW distance with 81.13\% accuracy (Table 9 of [1])
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[1] Souza V.M.A. Asphalt pavement classification using smartphone
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accelerometer and Complexity Invariant Distance. Engineering
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Applications of Artificial Intelligence Volume 74, pp. 198-211.
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https://www.sciencedirect.com/science/article/pii/S0952197618301349
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[2] Souza V.M.A., Cherman E.A., Rossi R.G., Souza R.A. Towards
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automatic evaluation of asphalt irregularity using smartphones
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sensors International Symposium on Intelligent Data Analysis
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(2017), pp. 322-333
|
This dataset was used in [1] and donated by the authors of that
|
paper. Accelerometer data was collected on a smartphone installed
|
inside a vehicle using a flexible suction holder near the
|
dashboard. An expert was responsible for driving the vehicle while
|
the device ran an Android application called Asfault [2], developed
|
specifically to store the current asphalt condition continuously
|
over time. Asfault stores the time-stamp of the collected data,
|
acceleration forces in along the three physical axes, latitude,
|
longitude, and velocity.
|
The acceleration forces are given by the accelerometer sensor of
|
the device and are the data used for the classification task.
|
Latitude, longitude, and velocity are given by the GPS and are used
|
to locate the collected/classified data. Each one of this
|
information is a different continuous time series. A sampling rate
|
of 100 Hz was used for each time series. It is important to note
|
that the Android system does not guarantee a perfect precision on
|
the interval between readings. This means that when we choose a
|
sampling rate of 100 Hz, the device should output between 95 and
|
105 observations per second.
|
For this data, the (X,Y,Z) acceleration time series are treated as
|
separate dimensions. A univariate version that combines the data is
|
also in the archive.
|
In order to obtain labeled data, the Asfault application allows the
|
expert to inform the pavement condition before collecting the data.
|
To guarantee the integrity of data, Asfault also records videos of
|
the road over the data collection using the built-in camera. Thus,
|
it is possible to perform the analysis of these videos to confirm
|
the class labels assigned by the expert.
|
The datasets were collected in the Brazilian cities of Sao Carlos,
|
Ribeirao Preto, Araraquara, and Maringa using a medium sized
|
hatchback car (Hyundai i30) and two different devices (Samsung
|
Galaxy A5 and Samsung S7).
|
The problem AsphaltObstacles involves the identification of four
|
common obstacles in the region of data collection. It has the
|
following class labels: raised_crosswalk (160 cases);
|
raised_markers (187 cases); speed_bump (212 cases); and
|
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