Pressure sensors don't really give a good measure of altitude from one sample. For starters weather can drastically change the pressure in a region. If a sensor package could somehow filter that out with a 4 minute sample rate (doubtful) then the sensor is still giving you altitude relative to sea level which means the data would have to have a GPS component to differentiate between on the ground on a tall hill or flying over a flat lowlands area.
Mostly a nitpick, but pressure sensors don't give a good measure of absolute altitude ever, regardless of the number of samples. You must calibrate it using pressure measurements from a ground station nearby in space and time, or else it tells you roughly nothing.
Weather matters a lot, as you say. The variation caused by weather can be equivalent to several thousand feet of altitude. Worse, changes due to weather are long-term. You can't filter them out, period. Samples taken four minutes apart can tell you changes in altitude, since the weather won't change that much in such a short time, but there's no way to backtrack to absolute altitude without calibration, no matter how many samples you take.
True. However if we're looking for very brief landings followed by periods of flight you might see that in the pressure data.
You'd expect some flat "flight level" pressure over a day, with V pressure changes to correlate with landings (or swoops in mid-air). You would need to correlate these with other sensors for it to mean anything concrete but it could corroborate or disprove other theories.
Right, but you couldn't distinguish a V that was a brief landing from a V that was a dive and climb, nor could you distinguish a flat line that was cruising from a flat line that was sitting on the ground.
I don't doubt that pressure data would be useful, it just doesn't give you altitude data.
You're right. So how else could this problem be solved given the power constraints?
(P.S. I do have to mention - It's very likely that my critique is completely invalid because they may have performed measurements of these birds in flight Vs. these birds on ground, and the accel data may show completely different characteristics for both. It's also likely that they may have chosen the sampling rate and data duration based on these experiments, but since this is HN, it's fun to speculate on how this experiment could be improved.)
With fish they have tags that record light intensity, pressure and temperature which is enough to give a low res position estimate [1]. Not sure how well this would transfer to this problem but it is cool tech.
At 5km up the difference is miniscule, back of the napkin says the difference is ~-0.01 m/s^2. Which is pretty small to use as a classification filter. All the way up to 40km it's still only ~1.2% weaker.
