Interesting note, I actually disabled promiscuous mode after some testing because it made the CSI signal noisier and consumed more resources. I found that normal station mode with pings to gateway gave me cleaner, more predictable CSI data. But your MAC filtering approach might mitigate those issues!

You're spot on about the MVS approach. It's essentially a sliding window variance of the spatial turbulence (std dev across subcarriers), with adaptive thresholding based on the moving variance of that signal.

If you're interested in the MVS details, I wrote a free Medium article that walks through the segmentation algorithm step-by-step with visualizations. Links are in the README.

Your approach is actually quite similar to what I'm doing, just in a different order:

- My flow: Raw CSI → Segmentation (MVS) → Filters (Butterworth/Wavelet/Hampel/SG) → Feature extraction

- Your flow: Raw CSI → EWMA de-meaning → Dimensionality reduction → Feature extraction

The main difference is that I segment first to separate IDLE from MOTION states (keeping segmentation on raw, unfiltered CSI to preserve motion sensitivity), then only extract features during MOTION (to save CPU cycles).

Thanks for the thoughtful feedback! Always great to exchange notes with someone who's been in the trenches with CSI signal processing

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roger_ 6 hours ago | parent [-]

I noticed your feature vector is large and you don't use ML. What's the final statistic that you threshold?

	▲	francescopace 6 hours ago \| parent [-]
		The final statistic I threshold is the Moving Variance of Spatial Turbulence. The decision is a binary comparison: When moving_variance > threshold then MOTION state (movement detected) else IDLE state. The features are extracted only during MOTION segments (to save CPU cycles) and published via MQTT. They serve as rich foundation data for potential external ML models (e.g., to capture nuances like gestures, running, or falling), but they are absolutely not used for the core segmentation decision itself.