Watch a short video showing how, with the help of AI, researchers were able to detect the movement of human bodies in a room using Wi-Fi routers — even through walls.
Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns.
To address such limitations, recent research has explored the use of Wi-Fi antennas (1D sensors) for body segmentation and key-point body detection. Read about this technology that uses the Wi-Fi signal in combination with deep learning architectures to estimate dense human pose correspondence.
The attached article elaborates on how the researchers developed a deep neural network that maps the phase and amplitude of Wi-Fi signals to UV coordinates within 24 human regions. The results of the study reveal that the DensePose model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing Wi-Fi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.