  This study explores the feasibility of adapting CSI-guided imaging across
varied environments. Focusing on continuous model learning through continuous
updates, we investigate CSI-Imager's adaptability in dynamically changing
settings, specifically transitioning from an office to an industrial
environment. Unlike traditional approaches that may require retraining for new
environments, our experimental study aims to validate the potential of
CSI-guided imaging to maintain accurate imaging performance through Continuous
Learning (CL). By conducting experiments across different scenarios and
settings, this work contributes to understanding the limitations and
capabilities of existing CSI-guided imaging systems in adapting to new
environmental contexts.
