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...
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.
Gait analysis is complete fiction. Especially with a non-visual approach like this.
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.
> 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.
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?]