In a groundbreaking advancement, scientists from Carnegie Mellon University have harnessed the power of WiFi technology to sense people through walls, enabling the imaging of a person’s 3D shape and pose.
This cutting edge research opens new possibilities for privacy preserving human sensing applications at a fraction of the cost compared to traditional methods.
In the research paper, it is explained that the technology relies on two WiFi routers, and an off the shelf 1D sensor, to accurately map multiple subjects’ poses.
The researchers utilised a deep neural network named DensePose, which was originally developed by researchers at Imperial College London, Facebook AI, and University College London. DensePose maps WiFi signals, specifically phase and amplitude, to UV coordinates, allowing the projection of a 3D model’s surface into a 2D image for computer generated mapping.
One of the key achievements of the Carnegie Mellon researchers is the ability to achieve precise pose mapping using WiFi antennas, eliminating the need for costly RGB cameras, LiDAR, or radars. The WiFi technology not only enables human sensing but also surpasses previous capabilities of merely locating objects in a room.
In their paper, titled “DensePose From WiFi,” researchers Jiaqi Geng, Dong Huang, and Fernando De la Torre express the significance of their findings.
They explain that their model effectively estimates the dense pose of multiple subjects, demonstrating comparable performance to image based approaches while relying solely on WiFi signals as input.
It is important to note that this paves the way for affordable, widely accessible, and privacy preserving algorithms for human sensing.
While many might feel uncomfortable about the ability to use WiFi signals to sense humans through walls, the researchers have pointed out that the technology could be applied to monitor the wellbeing of the elderly or identify suspicious behaviours in homes while ensuring individuals’ privacy.
While human detection, tracking, and pose estimation from images and video have been extensively studied, research on human pose estimation from WiFi or radar has been relatively limited.
Furthermore, the Carnegie Mellon researchers’ groundbreaking work paves the way to an entirely new frontier for using WiFi signals as a powerful tool for interior monitoring and human sensing.
“The performance of our work is still limited by the public training data in the field of WiFi based perception, especially under different layouts. In future work, we also plan to collect multi layout data and extend our work to predict 3D human body shapes from WiFi signals. We believe that the advanced capability of dense perception could empower the WiFi device as a privacy friendly, illumination invariant and cheap human sensor compared to RGB cameras and Lidars,” concluded the researchers.
To read the researchers’ paper, click here.
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