The truth is that it's cool research that currently has zero use-case. But a) journalists would not write about that and b) researchers may try to use examples to explain what their research does. Probably researchers are tempted to find a cool use-case of course, because it's better for them if journalists write about their research.
This sounds like cool research that is not remotely close to becoming an invisible mass surveillance system.
> To allow for an unobstructed gait recording, participants were instructed not to wear any baggy clothes, skirts, dresses or heeled shoes.
> Due to technical unreliabiltities, not all recordings resulted in usable data. For our experiments, we use 170 and 161 participants for CSI and BFI, respectively. [out of 197]
I wish they had explained what the technical unreliabilities were.
Highly unlikely and would be a waste of effort and resources. In the real world we are already well surveilled by cameras, microphones, satellites, cell phones, televisions, modern vehicles with a large number of cameras, web enabled doorbell cameras, refrigerators, AirTags, robot vacuum cleaners that map our home and monitor us, anything bluetooth enabled and that is even before actual spy devices like laser microphones that can turn most windows into a giant microphone.
All of these methods are far more attainable without trying to recreate microwave imaging that has been used by the feds for ages and the feds use a handheld device vs. this complex lab setup and this is even before we talk about advanced high resolution milspec FLIR which some companies have managed to get into serious trouble for selling to sanctioned countries for ITAR violations.
"Xfinity using WiFi signals in your house to detect motion", 500 comments, https://news.ycombinator.com/item?id=44426726#44427986
"Wi-fi signal tracks heartbeat without wearables", 80 comments, https://news.ycombinator.com/item?id=45488908
2022 laptop demo of respiration sensing, https://www.intel.com/content/www/us/en/research/respiration... | https://community.intel.com/t5/Blogs/Tech-Innovation/Client/...
2025 biometric signature, https://www.theregister.com/2025/07/22/whofi_wifi_identifier...
> Researchers.. developed.. a biometric identifier for people based on the way the human body interferes with Wi-Fi signal propagation.. CSI in the context of Wi-Fi devices refers to information about the amplitude and phase of electromagnetic transmissions.. interact with the human body in a way that results in person-specific distortions.. processed by a deep neural network, the result is a unique data signature.. [for] signal-based Re-ID systems
That a super impressive! I wonder how that would be at scale, with a few millions people. I’m don’t think that would remain as accurate
The concept sounds not unlike like the multispectral imaging produced by Geordi's visor in TNG.
Seems conceptually possible, but likely too much computing power and observing time (to build up and learn each individual's pattern in that part of the RF band), at least in current times.
I'm sure it could be developed to work in the field, but what is the use case where it pays off to make the silly-money investment to make it happen? Especially so when it's far easier to simply notice pings and get better data when approximately everyone always carries their mobile phone.
Nothing says "out of touch with reality" like 'murcan media.
The devices that reported BFI information were also stationary, and there were no extra devices transmitting information that would be conflicting.
A single camera would be much more effective.
Heck, even Ecobee remote temperature sensors can do this.
Reminds me of the story about how the Google Nest smoke detector had a microphone in it. [1]
0 - https://www.amazon.com/b?node=23435461011&tag=googhydr-20&hv...
1- https://www.reddit.com/r/privacy/comments/asmusq/google_says...
> By collecting this data, images of people can be generated from multiple perspectives, allowing individuals to be identified. Once the machine learning model has been trained, the identification process takes only a few seconds.
> In a study with 197 participants, the team could infer the identity of persons with almost 100% accuracy – independently of the perspective or their gait.
So what's the resolution of these images, and what's visible/invisible to them? Does it pick up your clothes? Your flesh? Or mosty your bones?
Any sub-meter precision or presence detection does not really matter, if these companies have all your other questions, queries, messages, calendars, browse history, app usage, and streaming behaviour as well.
Has tech changed. I'd use it over my wifi setup if its was fast.
Already is and widely used for exactly what the article worries about
Would not be surprised to see this get more traction right now due to the political climate.
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.
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.
The paper says, in a somewhat contrived scenario, with dozens of labelled walkthroughs per person, they can identify that person from their gait based on CSI and other WiFi information.
This is a long way from identifying one person in thousands or tens of thousands, being able to transfer identifying patterns among stations (the inference model is not usable with any other setup), etc.
All the talk of "images" and "perspectives" is journalistic fluffery. 2.4Ghz and 5Ghz wavelengths (12cm & 6cm) are too long to make anything a layperson call an "image" of a person.
What creepy thing could you actually do with this? Well, your neighbor could probably record this information and tell how many and which people are in your home, assuming that there is enough walking to do a gait analysis. They might be able to say with some certainty if someone new comes home.
That same neighbor could hide a camera and photograph your door, or sniff your WiFi and see what devices are active or run an IMSI catcher and surveil the entire neighborhood or join a corporate surveillance outfit like Ring. Using the CSI on your WiFi and a trained ML model is mostly cryptonerd imaginiation.
Even Xfinity has motion detection in homes using this technique now:
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.
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.
Not even the biggest privacy issue of using Alexa devices. I think listening you 24/7 is a bigger potential issue.
Not sure if Alexa has this, but cheap mm-wave wideband multi-GHz sensors(or radars more accurately) now enable more finely grained human presence detection and also human fall detection[1] with the right algos, so you can for example detect if grandma in the nursing home fell down and didn't get back up, but in a privacy focused way that doesn't resort to microphones or cameras. Neat.
>Reminds me of the story about how the Google Nest smoke detector had a microphone in it.
Vapes have microphone arrays in them to detect when you're sucking and light up the heating element. Cheap electronics have enabled a new world of crazy.
[1] https://www.seeedstudio.com/MR60FDA2-60GHz-mmWave-Sensor-Fal...
I mean you could even jam a microwave oven door open, turn it on, and then measure how much energy loss there was through certain paths. That's essentially all beamforming in Wifi requires -- a really sophisticated way of measuring paths that cause energy loss, and a really sophisticated antenna design that allows you to direct the signal through paths that don't cause energy loss. The first problem is what's facilitating surveillance because humans cause signal loss because our bodies are mostly water, and 2.4 GHz radio waves happen to get absorbed really well by water. This causes measurable signal loss on those paths and the beamforming antennae use that information to route around your body. But they could also just log that information and know where you are relative to the WAP.
It was listed in the features for the 2nd gen units. https://support.google.com/googlenest/answer/9229922#zippy=%...
Edit: That article isn't about the Nest Protect (smoke detector), it's about the Nest Secure, an alarm system.
The researchers never claimed to generate "images," that's editorializing by this publication. The pipeline just generates a confidence value for correlating one capture from the same sensor setup with another.
[Sidenote: did ACM really go "Open Access" but gate PDF download behind the paid tier? Or is the download link just very well hidden in their crappy PDF viewer?]
Run grapheneos!
> a study with 197 participants, the team could infer the identity of persons with almost 100% accuracy – independently of the perspective or their gait.
The paper seems to make it clear that the technique still depends on gait analysis, but claims it's more robust against gait variations.
My understanding is that it has improved in some circumstances, but if the connection ends up "hopping" through your breaker you get back to garbage speeds.
In theory you can get 2 Gbps speeds, but in practice it seems like still around 500 Mbps. I don't know if the loss has improved but it was a significant problem before, since even a low loss will render a connection unusable.
Philips WiZ bulbs: https://www.wizconnected.com/en-us/explore-wiz/spacesense
Alarm.com also supports such sensors: https://poweredbyalarm.com/eventresources/wp-content/uploads...
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.
The phone actually records audio and sends it remotely to someone else.
If you want to do advanced sensing, trying to identify a person, I would postulate you need to saturate a space with high frequency wifi traffic, ideally placed mesh points, and let the algo train on identifying people first by a certain signature (combination of size/weight, movement/gait, breath / chest movements).
Source: I worked on such technologies while at Signify (variants of this power Philips/Wiz "SpaceSense" feature).
More here: https://www.theverge.com/2022/9/16/23355255/signify-wiz-spac...
Gait analysis is complete fiction. Especially with a non-visual approach like this.
> The results for CSI can also be found in Figure 3. We find that we can identify individuals based on their normal walking style using CSI with high accuracy, here 82.4% ± 0.62.
If you're a person of interest you could be monitored, your walking pattern internalized in the model then followed through buildings. That's my intuition at practical applications, and the level of detail.
Second, it is a logical leap to assume Google knows everything already. They could for example build this nearby Wi-Fi based location querying API with privacy in mind, by purposefully making it anonymous without associating it with your account, going through relays (such as Oblivious HTTP), use various private set intersection techniques instead. It is tired and lazy to argue that just because some Big Tech has the capability of doing something bad therefore they must already be doing it.
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.
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.
This approach relies solely on the "unencrypted parts of legitimate traffic". The attacker does not need to send any packets or "generate" their own traffic; they simply "listen" to the natural communication between an access point and its clients.
BFI is much more complex than simple signal strength. RSSI is an aggregation of information that the researchers describe as "not robust" for fine-grained tasks In contrast, BFI is a high-resolution, compressed representation of signal characteristics. This rich data allows the system to distinguish between 197 different individuals with 99.5% accuracy, something impossible with basic RSSI.
While older CSI methods often focused on walking directly between a specific transmitter and receiver (Line-of-Sight), BFI allows a single malicious node to capture "every perspective" between the router and all its legitimate clients.
Also CSI requires specialized hardware and custom firmware, this one isn't, just wifi module in monitor mode.
In which world are you living?
> It is tired and lazy to argue that just because some Big Tech has the capability of doing something bad therefore they must already be doing it.
It has the capability of doing something bad, and it has a history of doing it. Better not forget the last part.
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.
Your resolution limit is about 30mm as a result.
Answer: no need, if it had been cured, it would be cured. And it is not.
My point being that many publications saying "towards X" may mean that we are making some progress towards X, but they don't mean at all that X is possible.