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Sub-sampling the minimum number of points to accurately reproduce a curve

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Michael Granatosky
Michael Granatosky on 8 Jan 2020
Edited: David Goodmanson on 8 Jan 2020
Hello,
Currently some students of mine are working on a bioinspired robot. We are using poistion data collected directly from a moving animal and putting those values onto servo motors. Recently, we have come across a bit of an over-sampling issue that is casuing some computational issues.
Anyway, when I collect data from an animal I can get kinematic movements of position versus time. For this question, let's assume 100 data points. (Sorry about the sideways orientation of the photos).
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Together these 100 data points can be graphed as a continuous curve
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Now my problem is that if I make our robot hit all 100 data points the movement is highly accurate, but causes the servo motors to be very jerky and a lot of computational power. So my hope is to downsample/ subsample the number of points I have, but still retain the shape of that curve. For example:
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If I reduce the data down to two points I get an abysmal representation of the original curve. What about 3 points?
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Still not great...
However, by the time I hit 9 points, we're doing pretty good.
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Essentially, I'd like some advice on 1) what this form of downsampling/ subsampling is even called; and 2) any ideas on some MATLAB code that can help me maximize accuracy while limiting data.
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Any advice is greatly appreciated.

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Answers (1)

David Goodmanson
David Goodmanson on 8 Jan 2020
Edited: David Goodmanson on 8 Jan 2020
Hi Michael, take a look at the interp1 function. There are several choices for the type of interpolation, and assuming a curve that looks like your example, not too wild, the spline option (cubic spline) is one of the most accurate.

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