Wi-Fi sensing enables innovative applications in healthcare and surveillance by providing continuous, contactless monitoring through existing infrastructure. Moreover, exploiting information from different links within an environment (i.e., different views) and using it as input to Deep Learning (DL) models facilitates sophisticated use cases. However, combining multiple views for improved sensing is not trivial. In this talk, I will present recent work introducing and addressing two limitations of existing Wi-Fi sensing frameworks: 1) at which depth within the DL model should views be fused together? 2) Are all views equally important? How can we assess view importance?

About Iñaki Bravo

Iñaki is a doctoral candidate at the IMDEA Networks Institute and Universidad Carlos III de Madrid (UC3M). He is part of the Resilient AI Networking Lab, under the supervision of Prof. Claudio Fiandrino.He holds a degree in Aerospace Engineering from Universidad Politécnica de Madrid (UPM), and an M.Sc. in Computational Mathematics from Universidad Carlos III de Madrid (UC3M). Before his PhD, he worked one year as a Research Engineer in the Edge Networks Group.

Este evento se impartirá en inglés