Even Xfinity has motion detection in homes using this technique now:
Off the top of my head, I bet body composition combined with gait analysis would be enough to uniquely identify an individual.
> Researchers in Italy have developed a way to create a biometric identifier for people based on the way the human body interferes with Wi-Fi signal propagation.. can re-identify a person in other locations most of the time when a Wi-Fi signal can be measured. Observers could therefore track a person as they pass through signals sent by different Wi-Fi networks – even if they’re not carrying a phone.. their technique makes accurate matches on the public NTU-Fi dataset up to 95.5 percent.
Let's say you visit a friend in a different city, the same ISP controlling their router, can use your mac, but even if you turn off your wifi or leave your phone in your car, your volume profile and gait can betray you. how you sit, how you lean, how you turn. I'd wager, if 6-10 distinct "points" can be made out and associated with a person, that's all that's needed to uniquely identify that person after enough analysis of their motion, regardless of where they go in the world.
Imagine if they're not using one AP, but using your neighbors AP as well, two neighbor APs and your own can triangulate and refine much better.
Your resolution limit is about 30mm as a result.
WiFi presence detection is a completely different problem. If the WiFi environment is changing past a threshold, return a boolean yes or no. It can't actually tell if someone is present or if the environment is just changing, such as a car driving close enough to reflect signals back in a certain way.
Doing mass surveillance where you detect individual people in a random home environment isn't the same thing at all. All of these "could" claims are trying to drawn connections between very different problems.
With gait analysis for example, it's only looking at a handful of data points, the way we walk is very unique. lip-reading, i can see how that's a stretch, but out movement patterns and gait are disturbances in radio waves. If you're using just one person's wifi, that sounds difficult, but if you're collecting signal from multiple adjacent wifi access points, it's more realistic to build a very coarse motion representation, perhaps with a resolution no finer than 1 cubic ft, but even with more coarse representations, gait can be observed.
Even gait aside, the volume profile of a person and their location in the house alone are important data points, couple that with the unique wifi identifier or IP, you can make a really good guess at who the person is, and what room they're in.
there is a working group at 3gpp, an EU-funded research group (6th sense, Open6GHub), universities (NCSU, Bristol), and many companies working very hard right now on proposals to include "integrated/joint sensing and communication" (ISAC/JCAS) in the 6G spec.
ISAC means adding mmWave to 6G (ostensibly for speed, but also) to build a high-fidelity 3d realtime "digital twin" of the real world that can see through walls, owned and operated by your telecom provider.
> A very exciting innovation that 6G will bring to the table would be its ability to sense the environment. The ubiquitous network becomes a source of situational awareness, collating signals that are bouncing off objects and determining type and shape, relative location, velocity and perhaps even material properties. With adequate 6G solutions for privacy and trust, such a mode of sensing can help create a “mirror” or digital twin of the physical world in combination with other sensing modalities.
https://www.nokia.com/about-us/newsroom/articles/nokias-visi... https://www.bell-labs.com/institute/blog/building-network-si...
there's been a testbed deployment in a German hospital for "non-invasive" monitoring of vitals; which sounds to me like it can literally see a heartbeat.
https://www.nokia.com/about-us/news/releases/2024/12/17/noki...
truth is, this is the nature of wireless radios. we can't keep improving bandwidth and latency without also turning the radio into a camera. i'm disturbed by the inevitability.
"See through walls"
There used to be a great video on youtube of a very high power 60GHz signal being blocked by a door. Sad I can never find it. E Band isn't much better.
IIRC the 60GHz radio is being left out of a lot of 5G deployments because the slight benefits don't outweigh the cost.
This is a pretty common thing for mmWave (or near mmWave) to be deployed with massive fanfare and then be slowly phased out of existence. I am decidedly not writing this on a WiGig docking station.
I dont see telcos wanting to constantly broadcast extra mmWave for little to no added benefit, especially not in all directions. Likewise, regulators are going to choke on that. And the class/band license schemes would have to be updated, to remove interference from devices already using those bands as they are about to have a constant background level of interference. E-Band PTP users, of which there are many, wont give up their high capacity links to weird 6G omni broadcasts without a fight.
I tell you what however, having a button you can press that would map the environment for alignment sounds like a maybe use case here. Better than a camera for detecting new obstructions when links go down.
They might also add more bands to the whole automatic MIMO backhaul trick they have been pursuing.
Right, that's what your eyes do. Radio is much longer wavelength than visible light (~5-10cm). So at best it offers extremely crappy resolution unless - you're doing something clever with second order information.
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.
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.
LLMs were useless back in 2021.
And based on that I could imagine with a combination of a camera and this method, you could train the model on data where both the camera and this method is seeing the individual and then continue to track them with the wifi sensing + the trained model even where the camera cannot see them anymore.
But yea real world is noisy, so it could be very challenging.
Would not be surprised to see this get more traction right now due to the political climate.
We've seen it before with things like taking photos around corners.
And no, it isn't like the Wright flyer and a bit crap now but in 40 years we have jet planes. This will never get significantly better.