Address: 1207 Storey Innovation Center
Our work focuses on overcoming challenges in existing vision based systems to enable high quality imaging without invading privacy. Our work is based on millimeter-wave (mmWave) wireless technology in 5G-and-beyond devices. Such systems work even under no light conditions and preserves users privacy. By augmenting intelligence using custom-designed conditional Generative Adversarial Networks (GAN) model, our system generates high-resolution silhouettes and accurate poses of human body on par with existing vision-based systems. We have customized our work for gait monitoring applications, but the system can be adapted to facilitate other tracking and monitoring applications.
Further, we are looking into extending at-home beyond vision imaging to monitor sleep postures to provide insights to medical professionals and individuals in improving sleep quality and preventing negative health outcomes.
Our research presents a low-barrier means to perform at-home spirometry tests using 5G smart devices. The system that we propose works like regular spirometers, where a user forcibly exhales onto a device; but instead of relying on special-purpose hardware, it leverages built-in millimeter-wave technology in generalpurpose, ubiquitous mobile devices. Our system analyzes the tiny vibrations created by the airflow on the device surface and combines wireless signal processing with deep learning to enable a software-only spirometry solution. From empirical evaluations, we find that mmFlow can predict the spirometry indicators with performance comparable to inclinic spirometers.