Simple linear relations ruined

Monday, February 16th, 2015 at 8:49 pm

Quick report on the misery of trying to get any good data out of these fancy good-for-nothing data sensors.

I did a bike ride on Sunday around Yorkshire, from Barbon to Dent, up the valley, down to the very fine Cafe Nova in Sedburg and then back to Kirby Lonsdale via a bridge over the river Lune where Becka and I went kayaking in January and got scared (it looked a bit tame in these water levels). This was the day after the day before where I broke my caving famine and did a nine hour Easegill traverse from Pippikin to Top Sink while the hard people (incl Becka) did the reverse route and went out Bye George to celebrate Tom’s birthday. (This was the same Tom who drove out to Austria with me last May so I could go hang-gliding when Becka stood me up to go on a caving holiday.) Hint: for my birthday I will not be going caving.

So, anyway, you’d think something as simple as the GPS-altitude and barometric readings would be somewhat related.

This is what the plot looks like of barometric pressure along the X-axis (zeroed at 990Mb) vs altitude, which ranges from 102m to 305m. Yellow is the first hour, where we went over the hill, and blue is the second hour peddling up the valley from Dent.

Not a great correlation. Here’s the same picture zoomed in. The squiggles are predominantly left and right accounting for the noise of the barometer readings.

Suppose I take a rolling average of sequences of 7m and plot the same here without all the noise, getting the yellow line.

Still pretty wobbly. The cyan is the plot of the barometric forumla which is:

101325*(1 – 2.25577e-5 * altitude)5.25588

This is near as damnit a straight line of slope -0.08488715682448557. Applying simple linear regression to the slope gives -0.08992119168062143, which is not a great match.

Maybe I ought to work out a way to do this calculation in run-time on the device itself to give a measure of how rubbish the altitude-barometer agreement is during operation so I don’t have to bring it back here and run these complicated python programs on the data.

Then I could see if it’s responsive to the mode of travel, eg bike vs walking up and down the hill.

The next correlation to look at from this data is tilt of the bike frame registered from the accelerometer vs the slope climb according to the GPS. I’ve got very little hope this will work, so have put it off. I’m already sure that the temperature vs altitude signal is completely lost in the noise, probably due to the proximity to the ground on which the sun was shining.

I hope to see something better if I ever get this thing in the air. Right now I’m 3D printing enclosures to grip on to the base bar and am gathering a desk full of lots of bits of useless bits of plastic. Got to push on and not be distracted.