Researchers at Carnegie Mellon University have developed a breakthrough technology that maps human bodies through walls using WiFi signals. The innovative method tracks key body points, expanding previous research on signal-based human detection.
The study introduces a deep neural network that converts WiFi signal phases and amplitudes into human body coordinates across 24 regions. As the researchers explain, their model can estimate dense pose for multiple subjects with performance comparable to image-based approaches, using only WiFi signals as input.
While high-cost technologies have long mapped human movements through walls, this approach offers a more accessible alternative. Researchers at MIT have spent years exploring similar technologies using cell phone and WiFi signals. The Carnegie Mellon team’s method involves sending and receiving WiFi signals to map body coordinates.
The study acknowledges current limitations in human pose estimation, noting that traditional image-based methods struggle with occlusion and lighting challenges. By leveraging WiFi signals, they provide an innovative solution that works in low-light conditions.
Interestingly, the researchers frame this technology as privacy-friendly, despite its potential for easier human tracking. However, the claim seems paradoxicalusing WiFi signals to detect people through walls raises significant privacy concerns.
The breakthrough potentially democratizes human tracking technology, making it more cost-effective and widely accessible. Yet, the ethical implications remain complex and somewhat unsettling.
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