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I have data samples for an EMG signal of three arm reps. In the data I have 1 signal that is flat then fluctuates in voltage then becomes flat again shows three pulses for each rep. I want to separate the three reps from the signal (chop the signal into 3 parts). I can manually do this by plotting the signal then zooming in and finding the index before the variance in voltage fluctuates and after it flat lines again. I wanted to know if there is an automated way to do this. Some sort of greedy algorithm or a built in Matlab function. Any ideas?

One example:

[12,-23,1,-2,-40,-45,80,85,14,-28,10,-22,-100,-105,80,85,14,-2,
 16,-2,-200,-45,305,85,1,-2,1,-2]

So I would want:

1 [-40,-45,80,85]
2 [-100,-105,80,85]
3 [-200,-45,305,85]

Usually the flat line values vary from zero.

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  • $\begingroup$ Do you have any model for what the three pulses look like? Is it different from how the rest of the signal behaves? $\endgroup$ – Peter K. May 31 '16 at 18:37
  • $\begingroup$ I only have the raw data. Each pulse corresponds to one arm rep. $\endgroup$ – wwjdm May 31 '16 at 18:46
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This is a little hacky, but it uses the three models you have to find an "eigen"-pulse and then takes successive inner products of a normalized signal segment with this "eigen"-pulse to give a measure between -1.0 and 1.0.

The red line in the image is your data. The black line is this normalized measure. You'll have to sanity check it (no detections within 2 samples of a previous detection ?), but other than that selecting anything above 0.8 should let you detect the start of the pulses.

enter image description here


R Code Below

  #31201

  arm <- c(12,-23,1,-2,-40,-45,80,85,14,-28,10,-22,-100,-105,80,85,14,-2, 16,-2,-200,-45,305,85,1,-2,1,-2)

  #1 [-40,-45,80,85]
  #2 [-100,-105,80,85]
  #3 [-200,-45,305,85]
  rep1 <- c(-40,-45,80,85)
  rep2 <- c(-100,-105,80,85)
  rep3 <- c(-200,-45,305,85)

  repMat <- rep1 %*% t(rep1) + rep2 %*% t(rep2) + rep3 %*% t(rep3);
  mean1 <- (rep1+rep2+rep2)/3

  eigs <- eigen(repMat)

  plot(rep1, ylim=c(-350, 350), col="red", lwd=6, type="l")
  lines(rep2, col="green", lwd=4)
  lines(rep3, col="blue", lwd=2)
  lines(mean1)
  lines(eigs$vectors[,1]*sqrt(eigs$values[1]), col="purple", lwd=1)

  eigs$vectors[,1] %*% rep1

  similarity <- rep(0,length(arm))
  for (idx in seq(1,length(arm)-3))
  {
    patch <- arm[idx + seq(0,3)]
    patch <- patch / sqrt(sum(patch ^ 2))
    similarity[idx] <- (eigs$vectors[,1] %*% patch)
  }

  plot(similarity, type="l")
  lines(arm/max(arm), col="red")
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