I have signal(s) (of a person climbing stairs.) of the following nature. This is a signal worth 38K + samples over a period of 6 minutes of stair ascent. The parts where there is some low frequency noise are the times when the person would take a turnabout to get to the next flight of stairs (and hence does not count as stair ascent.)
Figure 1
This is why I need to get rid of it for my deep learning model which only accepts the stair ascent data. Essentially, I only need the high frequency regions where the person is climbing stairs. I could do eliminate it manually, but it would take me a lot of time since there are 58 such signals.
My approach for a solution to this problem was modulating this signal with a square wave which is 0 for low frequency regions and 1 for high frequency regions and then to multiply the signals together. But the problem is how to create such a square wave signal which detects the high and low frequency regions on its own?
I tried enveloping the signal (using MATLAB's envelope rms function) and I got the following result:
Figure 2
As you can see the envelope rms signal follows the function quite well. But I am stuck as to how to create a modulating square wave function off of it (essentially what I am asking for a variable pulse-width modulating waveform.)
PS: I have considered using high-pass filter but this won't work because there are some low frequency signals in the high frequency stair-climbing region which I cannot afford to remove. I have also thought of using some form of rising/falling edge detection(for the envelope rms function) but have found no practical way of implementing it.) Please advise.
Thank you for your help in advance, Shreya
Thanks to David for his thresholding suggestion which I did on my dataset I have these results... though I am again stuck with trying to get rid of the redundant peaks between zeros (see image below) What do I do next?
Figure 3
I think I have been able to solve my problem of being able to isolate the "interesting" part of the waveform from the entire original waveform successfully using the following procedure (for the reader's future reference:)
A non-uniform waveform such as Figure 1 can have the "envelope(rms)" MATLAB function applied to obtain the orange function such as the one in Figure 2. Subsequently, I filtered this enveloperms waveform using MATLAB's very own "idfilt" function. This enabled me to get rid of the unwanted spikes (between zeroes) that were occurring between the "interesting" parts of the waveform. Then, using thresholding, I converted this waveform to be equal to 1 at the "interesting" parts and 0 at the "uninteresting" parts giving me a pulse-width modulated square wave form that follows ONLY the "interesting parts of the original waveform (in Figure 1) I then multiplied my square waveform with the original function and was able to filter out the "uninteresting" parts as demonstrated in Figure 4.
Figure 4
Thank You all for your help! This thread is now resolved!
data(envelope<threshold) = [](in Matlab). – David