Adapting CSI-Guided Imaging Across Diverse Environments: An Experimental Study Leveraging Continuous Learning

TL;DR

This paper investigates the ability of CSI-guided imaging systems to adapt to different environments using continuous learning, aiming to maintain accuracy without retraining for each new setting.

Contribution

It demonstrates the feasibility of continuous learning for CSI-guided imaging to adapt across diverse environments, reducing the need for retraining.

Findings

CSI-guided imaging maintains accuracy across environments

Continuous learning improves adaptability in dynamic settings

Experimental validation across office and industrial scenarios

Abstract

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.