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Right now I have code that takes data from two IMU's attached to each side of a knee joint and finds the angle in the knee while someone performs a squat. The Output is the angle on each plane between the two IMUs (the main one I am concerned with is the blue line which is the angle assuming the knee can only rotate on one axis). Plot attached.

I am wondering how I can identify and divide the data so I can analyze each "Repitition" of the exercise on its own. In the plot attached there would be three seperate repetitions I would want to look at.

Mark Sherstan
on 13 Dec 2018

Edited: Mark Sherstan
on 13 Dec 2018

Look at the function islocalmin and islocalmax. You can specify minimum prominence to filter out some of the noise at the bottom of your data to get just one minimum or one maximum for each of the trials. Your data looks fairly good so you might be able to just find the time each minimum happens and divide it by 2 to get that range and analyze accordingly.

If you post a .mat file with your data I could help to implment the functions.

Mark Sherstan
on 14 Dec 2018

Here you go :) Let me know if you have any questions (code is commented).

% Load in data and clear out start and end noise manually

load('Joint Angle and Time.mat')

jointAngle(1:600) = 0;

jointAngle(3560:end) = 0;

% Locate only the minimums (start and end of a cycle)

jointAngle2 = jointAngle;

jointAngle2(jointAngle2>10) = NaN;

idx = islocalmin(jointAngle2,'FlatSelection','all','MinSeparation',100);

idx = find(idx);

% Seperate out the data

ii = 1;

for jj = 1:2:length(idx)-1

out.jointAngle{ii} = jointAngle(idx(jj):idx(jj+1));

out.time{ii} = time(idx(jj):idx(jj+1));

ii = ii + 1;

end

% Plot the results

figure(1)

hold on

plot(time,jointAngle)

plot(time(idx),jointAngle(idx),'*r')

legend('Data','Seperation Points')

hold off

figure(2)

for ii = 1:length(out.time)

subplot(2,2,ii)

plot(out.time{ii},out.jointAngle{ii})

title(num2str(ii),'FontSize',16)

end

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Chris Turnes
on 17 Dec 2018

Mark's answer is a great way of doing this. In addition, you might consider looking at the ischange function:

% Look for at most 9 changes, since there are 4 segments of interest and 5 other segments

tf = ischange(jointAngle, 'SamplePoints', time, 'MaxNumChanges', 9);

plot(time, jointAngle, time(tf), jointAngle(tf), '*r')

This doesn't give you exactly the same results, as it basically finds the middle points of the rising and falling edges. But if all you need is to analyze the widths relative to each other, then it might also work.

To make it more robust, you could instead use the Threshold option from ischange:

% Use the threshold factor to find changes instead

tf = ischange(jointAngle, 'SamplePoints', time, 'Threshold', 1e5);

It's a bit harder to intuitively guess that the value for Threshold should be, but if you find a good one that works, it will (hopefully) work across multiple traces.

Chris Turnes
on 7 Jan 2019

Chris Turnes
on 8 Jan 2019

You can try telling ischange to look for piecewise linear changes rather than piecewise constant changes. This gets a bit closer, I think:

tf = ischange(jointAngle, 'linear', 'SamplePoints', time, 'Threshold', 1e4);

If that's not quite precise enough, then Mark's approach may be the better option for you. Or, perhaps you could mix the two; but I don't think there's anything built-in that makes this task particularly simple.

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