dearsky/wifi-densepose
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docs: fix model size inconsistency and add AI Backbone cross-reference in ADR-024 section

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Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-03-01 11:25:35 -05:00
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README.md
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@@ -162,7 +162,7 @@ Every WiFi signal that passes through a room creates a unique fingerprint of tha
- Turns any WiFi signal into a 128-number "fingerprint" that uniquely describes what's happening in a room
- Learns entirely on its own from raw WiFi data — no cameras, no labeling, no human supervision needed
- Recognizes rooms, detects intruders, identifies people, and classifies activities using only WiFi
- Runs on an $8 ESP32 chip (the entire model fits in 60 KB of memory)
- Runs on an $8 ESP32 chip (the entire model fits in 55 KB of memory)
- Produces both body pose tracking AND environment fingerprints in a single computation
**Key Capabilities**
@@ -227,6 +227,8 @@ cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index en
| Per-room MicroLoRA adapter | ~1,800 | 2 KB |
| **Total** | **~55,000** | **55 KB** (of 520 KB available) |
The self-learning system builds on the [AI Backbone (RuVector)](#ai-backbone-ruvector) signal-processing layer — attention, graph algorithms, and compression — adding contrastive learning on top.
See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-contrastive-csi-embedding-model.md) for full architectural details.
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