- WHOFI uses Wi -FI signal distortions for digital footprints without visual data
- Deep neuronal network maps signal changes to identify people with almost perfect precision
- Academic research opens new privacy debates around biometric monitoring through Wi -FI signals
Researchers at the University of La Sapienza in Rome have created Whoofi, a system that claims to identify people analyzing the Wi -FI signals.
The system tracks people interpreting how their presence interrupts Wi -FI patterns, offering a potential alternative to conventional biometric methods.
The technology works by examining the information of the state of the channel, or CSI, which measures the changes in the WI -FI signals caused by people and objects, and a deep neuronal network interprets these disturbances such as individual fingerprints.
Researchers claim that the system offers a 95.5% accuracy in the identification of people even in different environmental conditions.
The team behind Whoofi includes Danilo Avola, Daniele Pannone, Dario Montagnini and Emad Emam, who previously proposed a system called Eyefi in 2020. The new system is more precise and capable of reidentifying people through non -visual biometric firms incorporated into CSI.
Whoofi does not trust cameras or physical contact. You only need an existing WI -FI network to feel human presence and movement.
Technology can work in the dark, through the walls and even around the obstacles, so it is a discreet option compared to video surveillance systems.
The researchers emphasize that Whoofi does not collect personal data or reveals identities in the conventional sense, pointing out: “By taking advantage of the non -visual biometric characteristics integrated into Wi -Fi CSI, this study offers a privacy and solid preservation approach to RE -FI -FI -BASED RE -FI -BASED RE -FI -BASED RE -FI -BASED RE
Even so, it is clear that the ability to track people without their knowledge is a potential privacy nightmare.
Routine privacy violations can reveal daily behavior patterns, such as regular locations or movements, which can expose sensitive personal habits.
Until now, Whofi remains an academic project without known plans for commercial or government deployment. However, the advantages in surveillance capacity are clear. You can omit poor lighting and crowded environments and is less conspicuous than cameras or visual scanners.
A series of similar Wi-Fi-based detection technologies have appeared in several ways over the years.
Gamgee developed an autumn detection system that could alert others if someone fell or if an intruder entered the house.
Comcast Xfinity service introduced the Wi-Fi movement, which converts everyday devices such as smart refrigerators, printers or televisions into movement sensors.
Other researchers have gone further, using Wi-Fi signals to “see” through the walls. A UC Santa Barbara team created a system that describes objects and even reads letters through barriers.
A similar study of the Carnegie Mellon University showed how standard Wi-Fi routers can detect the location and position of a person’s body inside a room.
You can read more about the research behind Whoofi in this article published in the Arxiv Preprint server.
Through Tech xplore
