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Currently, I am doing a project with the aim of classifying potholes through machine learning. The data collected is from an accelerometer in which the z-axis measures the "vertical" acceleration of the car, when a pothole is struck.

I have tried to deconstruct the signals and create features using two methods:

  1. PACF along with a moving average to combat the noise

  2. Calculated a periodogram for spectral analysis

Is there any other ways to differentiate a pothole from "rough" road surfaces, as I am unsure which approach to take?

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  • $\begingroup$ did you find any solution to processing the accelerometer signal? $\endgroup$ – Manas Gupte Oct 19 '17 at 20:16
  • $\begingroup$ Perform SSA to preprocess your signal. $\endgroup$ – Emre Oct 19 '17 at 22:34
  • $\begingroup$ hey so i see that this was 2 years ago, have you completed it? can i have some references on it....THANKS $\endgroup$ – Nikhil mahajan Oct 4 '19 at 5:59
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Actually you could treat your acceloremeter signal like normal audio signals. There are endless possibilities for processing audio data (e.g. chroma features).

Another way would be to directly process your raw signal with the help of a neural network (1D-convolution or LSTM).

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Instead of spectral features and moving average, I would recommend wavelet features. You could either do a continuous wavelet transform (CWT) or a Short Wavelet Transform (SWT) and identify the peaks / drop where the potholes show up. The advantage with wavelet is that it is well resistant to noise and you can also preserve the time axis information to pin point the location of peaks.

The trick is just to choose an appropriate wavelet. Those which have spike shapes like Daubechies and Symlets would be ideal.

With proper feature extraction, you can even do the detection without machine learning.

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  • $\begingroup$ Thankyou for your help. I have applied DWT with the Daubechies filter using the wavlet package on R, unfortunately, the classification did not improve. $\endgroup$ – lfs Aug 4 '17 at 13:55
  • $\begingroup$ Curious to know what features did you extract with DWT? Because simply applying DWT and using the coefficients directly will not help. $\endgroup$ – Arun Aniyan Aug 9 '17 at 13:51

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