Could one predict from power data whether a cyclist is standing or sitting?

A few thoughts on the feasibility and reliability.

1. Placement of sensors. As the key characteristic of "sitting" is using a surface to sit on, it only makes sense to put the sensor into the seat. The farther away the sensor placement is from the saddle (handlebars, pedals, tires etc.), the more work it is to tell the outcomes apart and more noise of unrelated nature to deal with. It may require having and simultaneously analyzing multiple sensors (incline, acceleration etc.) to make up for the suboptimal placement of the main sensor. For example, you can use a videocamera in a car riding alongside the cyclist and filming the ride as a sensor. Then you are tasked with classifying the human's posture from the recording, which is a lot of data to process.

2. Fuzzy border between activities. While there are situations clearly described by humans and potentially detectable by computers as "sitting" or "standing", whatever machinery and algorithms are used, there will always be a grey zone of "don't know". There are always transitions from sitting to standing and back. There are also times of incompletely or skewedly loading the saddle, not even touching the bike etc. Compared to the two "positive" situations, how much of the "unknown" state will be present in the data for an average ride? Ultimately, it depends on the intended usage of such data. For some applications having the "don't know" answer 5% of the time would be unacceptable, while in other cases even 50% of unclassified input would be tolerable as long as the remaining 50% of the time is correctly classified into the two categories.

3. Individual calibration. Regardless of the approach, it will require some (or a lot) tuning of its algorithmic parameters to tell the outcomes apart. Finesse in this strongly affects the quality of the results. For example, for a single saddle pressure sensor, the calibration process may be limited to finding a single pressure threshold to cut the most of the noise. For a machine learning algorithm, you'll have to feed it thousands of manually marked-up traces of trainings in a hope to teach it something, but not too much. For more classic algorithmic approaches, you will most likely have to provide several numbers describing specific cyclist's and used bicycle geometry and weight characteristics.

There is a problem here in that there is currently no sensor on the market (that i'm aware of) to measure sitting/standing. And unless i'm misunderstanding ML, we would need that input into our data for it to learn how this correlates to the cyclists power/cadence etc.

However, i'll ignore that and look at a couple of options that might give promising results.

1. Power curve analysis: Maple Panda suggested in comments that no-one does 1000W in the saddle, and whilst this isn't true (track sprinters), it may be fair to say that if a user is within 20% of their peak 1s power then they are out of the saddle. This wouldn't translate to zwift very well though since riders are very rarely hitting these very high powers.

2. Power phase analysis: Some power meters transmit phase data. This tells us how much torque is being applied at different parts of the pedal stroke. In general when people get out of the saddle, the pedal stroke becomes less 'round' with more pronounced dead spots. The problem with this approach is that I don't believe all power meters support this data.