Likely uses pulse transit time and the shape of the pulse wave to infer changes in blood pressure. I wrote a bit about how this works (in the context of Apple Watch's hypertension notifications) here: https://www.empirical.health/blog/apple-watch-blood-pressure...
How does it transform blood speed to pressure? Doesn't it require a different calibration for each person? Your article says something about deep learning, but IIUC it's to detect unusual peaks in one person.
The speed of the pressure wave is one signal that correlates with blood pressure. It's a bit like a string being pulled taut -- waves travel faster with higher pressure. The shape of the wave also gives clues. For example, the rise time of the wave tells you something about the resistance encountered, which is a function of blood pressure.
Rather than hand-engineering these features, most modern systems are built on a wearable foundation model that's been trained reconstruct the signal (similar to how an LLM is trained to predict the next word). Those foundation models are picking up on these signals and likely others.
You're right that calibration with a cuff is required of all systems currently on the market.
The Signal Ring folks' claim they can do a blood pressure number without calibration, which is quite novel and seems to be their "secret sauce". They did run a clinical study as well, so presumably more details will come out whenever that's published.
> The speed of the pressure wave is one signal that correlates with blood pressure.
Can it distinguish 200/180 from 120/100? I expect it to depend more on the difference than the absolute value, but fluid dynamic inside flexible tubes is a nightmare.
> They did run a clinical study as well, so presumably more details will come out whenever that's published.