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?