Feature extraction of accelerometer data for machine learning 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:


PACF along with a moving average to combat the noise
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?
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).