I was wondering about this machine learning problem: Take a large sample of data from rides that includes whether a cyclist was standing, what the cadence, power, (or perhaps even torque?) and gradient are. Is it possible to predict using machine learning (ML) techniques whether a cyclist is standing or not? I would assume that this would be of some use for Zwift or other racing apps to show whether a cyclist is standing or sitting (and perhaps even determine air resistance based on that). Right now their algorithm is a bit too simplistic (with a fixed gradient/cadence rule).
My idea is roughly that the power distribution over a single stroke should look different for a standing cyclist and a sitting cyclist. Of course this would all need to be conditioned on personal variables of the rider (weight, trainer model, power meter) but those are issues that ML is really good at. But I wonder if there are any reasons why this would be completely impossible (for example, if power meters can only measure power over 1 second at sufficient precision).
Update: Indeed most power meters measure torque at a higher frequency and transmit this data to the cycling computer/trainer app/etc.. However, these cycling computers usually only provide files at a data frequency of one datapoint per second. Therefore it seems that only cycling computer specific solutions would be possible.