| dc.description.abstract | Thanks to the definition of the new IEEE 802.11bf standard, the development of Wi-Fi sensing applications is gaining momentum in the research community. In this regard, several studies have shown that learning-based approaches that leverage the frequency response of the Wi-Fi channel in the sub-7 GHz bands can reach high accuracy in different classification tasks, such as activity recognition, or person identification. Instead, more fine-grained applications – e.g., human localization and tracking, or respiration and heartbeat monitoring – require implementing model-based approaches to estimate the Wi-Fi multi-path parameters and analyze the time evolution of the paths associated with specific targets (the human body or chest). In this paper, we investigate the performance of six super-resolution algorithms for sub-7 GHz multi-path parameter estimation. Our extensive evaluation indicates that the estimation accuracy that can be achieved through commercial devices allows implementing human localization and tracking strategies but is insufficient to effectively design human vital signs monitoring applications due to the limited frequency and spatial diversity. We pledge to release our implementations for further investigations. | es |
