XAI2CSI: Interpreting CSI with eXplainable AI for Human Activity Recognition
This paper introduces XAI2CSI, a framework that leverages eXplainable Artificial Intelligence (XAI) to analyze DL-based CSI sensing systems and employs SAGE, a model-agnostic explainability method, to quantify temporal, spectral, and spatial CSI contributions to HAR decisions under nominal and cross-context evaluations on IEEE 802.11ax data.
Idio Guarino, Alfredo Nascita, Domenico Ciuonzo et al.
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