Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction

The paper proposes a pipeline that compresses Wi-Fi CSI signals into discrete representations and extracts Linear Temporal Logic (LTL) rules for human activity recognition. Rather than relying solely on a black-box classifier, it produces deterministic rules that make temporal and causal dependencies explicit.

Its cybersecurity relevance lies in making wireless sensing more inspectable and governable. Wi-Fi CSI can enable sensitive inferences about presence and activity; understanding the rules behind a model helps assess its reliability, limitations, and privacy implications, while supporting more controllable countermeasures and multi-antenna fusion.