Exactly. All of these stories using WiFi to detect things with high accuracy are just extreme machine learning demos.
Given a tightly controlled environment and enough training data, you can use a lot of things as sensors.
These techniques are not useful for general purpose sensing, though. The WiFi router in your home isn't useful for this.
WiFi AP's already do a lot of tracking and measurement just to improve signal fidelity and effective throughput. Why wouldn't those same techniques be useful for more general object tracking? Of course using a single AP to attempt to track movement in real-time is unlikely to have great results, but with several APs and enough compute triangulation should improve results.
> Why wouldn't those same techniques be useful for more general object tracking?
These demos use machine learning to train against a known environment.
Basically, pattern matching changes in the signals against a very controlled set of training data.
You can use WiFi signals to detect that something is changing in the environment, but without the machine learning with controlled input data you don't know what it actually means. This is how WiFi presence detection works, but it won't tell you if it's a person moving through the house or your cat walked in front of the router.
today's tech demos are tomorrow's everyday
Exactly. People are wrong to dismiss this as they're doing in this thread.
LLMs were useless back in 2021.