Given the following signal, I would like to keep only the deceleration phase in orange.
I have no idea whether there exists techniques to do that or not. I imagine that I can smooth the signal, compute its derivative. But, I am not sure how to decide correctly when to cut the signal. In a perfect world, I would just consider the sign of the derivatives, but in ours, the smoothing may lead to some random punctual acceleration phases that are negligible in the deceleration phase. That makes the problem more difficult...
Any help will be appreciated ! Thanks.
N.B. I would like to add that I cannot filter using thresholds as I have to process several signals looking like this one but remaining different. I can try to do some statistics but I would like to know if there is signal processing based method that can deal with this problem efficiently.
EDIT Every signals I have slightly differ in the way they decreases (see derivatives amplitudes) and thus the time it takes for them to reach the low level you can see on the right side of the above curve.
I smoothed each signal using a Savitzky-Golay filter with the following parameter :
- window size : 31
- polynomial order : 3
This enables me to find a trade-off for smoothing my signals. Precisely, I smoothed the signals from the tip as suggested. Given those smoothed signals, I am able to compute nice first order derivatives and look at their sign. As a result, I am able to isolate the deceleration phase for all of them. But, my concern is about the methodology I used so far. Do you think it is a reasonable/good way to proceed ? I am wondering whether there are signal processing techniques that showed efficiency in this kind of problem. If so, it would be wise to use them :)