PhD Forum Abstract: Towards Deep Learning in Signal Translation for Cross Configurations in device-free WiFi Sensing

摘要

Wireless Sensing has been a topic in contemporary research for years and popularity is still intact as its ubiquitous and non-invasive nature. Human activity recognition using wireless sensing is leveraged by the deep learning advancements. However, maintaining the robustness of the sensing model in sensitive wireless environments is a non-trivial task to achieve. We propose a deep learning based signal translation framework for multiple variations observed in sensing environments such as transceiver variance and configuration of the user who performing the activities. Our approach has two major pipelines, translation pipeline which translates from novel configuration to a reference configuration and an inference pipeline which classifies the activities performed by the user. We further expect to extend this model to more complex multi user interacting sensing environments and to In-Situ setups.