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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. There is a univariate version in the archive |
also. |
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 AsphaltRegularity has two classes based on the comfort |
felt by the driver according to the condition of the pavement was |
considered. Regular: when the pavement is regular and the driver |
comfort is very little changed over time; |
Deteriorated: when is observed some irregularities and roughness in |
a deteriorated pavement that are responsible for transferring |
vibrations to the cabin of the vehicle, reducing the comfort of the |
driver. |
There are 1502 cases: regular (762) and Deteriorated (740 cases). |
Data is variable length, minimum 66 observations, maximum 4201. The |
data is split randomly into 50/50 default train series. The data |
can be resampled without bias. |
The best reported accuracy using combining rules with the XYZ data |
was achieved by the distance measure LCSS combined with the |
Complexity Invariant Distance (CID-LCSS) which achieved 98.48\% |
accuracy (measured with a 5x2 CV). See Table 5 in [1] |
The best reported accuracy with this univariate data was achieved |
by the distance measure LCSS combined with the Complexity Invariant |
Distance (CID-LCSS) which achieved 96.48\% accuracy (measured with |
a 5x2 CV, see Table 5 of [1]). |
[1] Souza V.M.A. Asphalt pavement classification using smartphone |
accelerometer and Complexity Invariant Distance. Engineering |
Applications of Artificial Intelligence Volume 74, pp. 198-211. |
https://www.sciencedirect.com/science/article/pii/S0952197618301349 |
[2] Souza V.M.A., Cherman E.A., Rossi R.G., Souza R.A. Towards |
automatic evaluation of asphalt irregularity using smartphones |
sensors International Symposium on Intelligent Data Analysis |
(2017), pp. 322-333 |
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