Beyond Sensor Data: Foundation Models of Behavioral Data From Wearables

Neat, though the paper and the Github repo have nothing to do with Apple's VO2Max estimations. It's related to health, and touches on VO2Max and health sensors, but the only source claiming any association at all is that Empirical site. And given that this research came out literally years after Apple added VO2Max estimates to their health metrics, it seems pretty conclusive that it is not the source of Apple's calculations. Neat research related to predicting heart rate response to activity (which might come into play for filling in measurement gaps which happen during activity when a device isn't tight enough, etc).

>What’s your source on Apple not using the neural network for VO2Max estimation?

You're asking me to prove a negative. Apple never claims that they do any complex math or deep neural networks to derive VO2Max, and from my own observations of its estimates of mine, it seems remarkably trivial.

Trivial can still be accurate. But it hardly seems complex. Like, guess people's A1c based upon age, body fat percentage, demographic and you'll likely be high-90s accurate with trivial algebra.

>even for seemingly simple metrics like heart rate

Deriving heart rate from a green light imperfectly reflecting off skin, watching for tiny variations in colour change, is actually super complex! Doing it accurately is actually pretty difficult, which is why wearable accuracy is all over the place, though Apple is one of the leaders and has been for years. Guessing a number based upon HR and activity level isn't quite as complex.