BaselineDriftDetector compared `mean_amplitude` against its EWMA baseline with *absolute* thresholds (anomaly 1.0, drift 0.15). Fine for the synthetic unit tests (amplitudes ~1.0), but raw ESP32 CSI is int8 I/Q with amplitudes up to ~128, so window-to-window RMS distance is routinely 5-50 >> 1.0 and AnomalyDetected fired on ~96% of windows (319/331 on a real node-1 capture). Drift is now `||current - baseline||2 / ||baseline||2` (a fraction, with an eps floor that falls back to absolute for a degenerate near-zero baseline), so one tuning is valid across raw-int8 ESP32, int16-scaled Nexmon, and baseline-subtracted streams. AnomalyDetected drops to 40/331 on the same data; the existing detector tests still pass (their explicit configs are valid relative thresholds too); added baseline_drift_is_scale_invariant_ no_anomaly_storm. rvcsi-events 18 -> 19 tests; 162 rvcsi tests, 0 failures, clippy-clean. Surfaced by an end-to-end test against real ESP32 CSI on COM7: the device (ESP32-S3, node 1, ADR-018 firmware, WiFi "ruv.net" ch5 RSSI -39, CSI cb only because nothing listens at .156). rvcsi has no ESP32 adapter yet, so a 7,000-frame node-1 recording was transcoded to .rvcsi via the new scripts/esp32_jsonl_to_rvcsi.py (stand-in for `record --source esp32-jsonl`) and run through `rvcsi inspect`/`replay`/`calibrate`/`events` end-to-end. ADR-095 D13 and ADR-096 sections 2.1/5 updated; CHANGELOG entry added; rvcsi-adapter-esp32 (live serial/UDP source) noted as a follow-up. Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|---|---|---|
| .. | ||
| .issue-177-body.md | ||
| ADR-001-wifi-mat-disaster-detection.md | ||
| ADR-002-ruvector-rvf-integration-strategy.md | ||
| ADR-003-rvf-cognitive-containers-csi.md | ||
| ADR-004-hnsw-vector-search-fingerprinting.md | ||
| ADR-005-sona-self-learning-pose-estimation.md | ||
| ADR-006-gnn-enhanced-csi-pattern-recognition.md | ||
| ADR-007-post-quantum-cryptography-secure-sensing.md | ||
| ADR-008-distributed-consensus-multi-ap.md | ||
| ADR-009-rvf-wasm-runtime-edge-deployment.md | ||
| ADR-010-witness-chains-audit-trail-integrity.md | ||
| ADR-011-python-proof-of-reality-mock-elimination.md | ||
| ADR-012-esp32-csi-sensor-mesh.md | ||
| ADR-013-feature-level-sensing-commodity-gear.md | ||
| ADR-014-sota-signal-processing.md | ||
| ADR-015-public-dataset-training-strategy.md | ||
| ADR-016-ruvector-integration.md | ||
| ADR-017-ruvector-signal-mat-integration.md | ||
| ADR-018-esp32-dev-implementation.md | ||
| ADR-019-sensing-only-ui-mode.md | ||
| ADR-020-rust-ruvector-ai-model-migration.md | ||
| ADR-021-vital-sign-detection-rvdna-pipeline.md | ||
| ADR-022-windows-wifi-enhanced-fidelity-ruvector.md | ||
| ADR-023-trained-densepose-model-ruvector-pipeline.md | ||
| ADR-024-contrastive-csi-embedding-model.md | ||
| ADR-025-macos-corewlan-wifi-sensing.md | ||
| ADR-026-survivor-track-lifecycle.md | ||
| ADR-027-cross-environment-domain-generalization.md | ||
| ADR-028-esp32-capability-audit.md | ||
| ADR-029-ruvsense-multistatic-sensing-mode.md | ||
| ADR-030-ruvsense-persistent-field-model.md | ||
| ADR-031-ruview-sensing-first-rf-mode.md | ||
| ADR-032-multistatic-mesh-security-hardening.md | ||
| ADR-033-crv-signal-line-sensing-integration.md | ||
| ADR-034-expo-mobile-app.md | ||
| ADR-035-live-sensing-ui-accuracy.md | ||
| ADR-036-rvf-training-pipeline-ui.md | ||
| ADR-037-multi-person-pose-detection.md | ||
| ADR-038-sublinear-goal-oriented-action-planning.md | ||
| ADR-039-esp32-edge-intelligence.md | ||
| ADR-040-wasm-programmable-sensing.md | ||
| ADR-041-wasm-module-collection.md | ||
| ADR-042-coherent-human-channel-imaging.md | ||
| ADR-043-sensing-server-ui-api-completion.md | ||
| ADR-044-geospatial-satellite-integration.md | ||
| ADR-045-amoled-display-support.md | ||
| ADR-046-android-tv-box-armbian-deployment.md | ||
| ADR-047-psychohistory-observatory-visualization.md | ||
| ADR-048-adaptive-csi-classifier.md | ||
| ADR-049-cross-platform-wifi-interface-detection.md | ||
| ADR-050-provisioning-tool-enhancements.md | ||
| ADR-050-quality-engineering-security-hardening.md | ||
| ADR-052-ddd-bounded-contexts.md | ||
| ADR-052-tauri-desktop-frontend.md | ||
| ADR-053-ui-design-system.md | ||
| ADR-054-desktop-full-implementation.md | ||
| ADR-055-integrated-sensing-server.md | ||
| ADR-056-ruview-desktop-capabilities.md | ||
| ADR-057-firmware-csi-build-guard.md | ||
| ADR-058-ruvector-wasm-browser-pose-example.md | ||
| ADR-059-live-esp32-csi-pipeline.md | ||
| ADR-060-provision-channel-mac-filter.md | ||
| ADR-061-qemu-esp32s3-firmware-testing.md | ||
| ADR-062-qemu-swarm-configurator.md | ||
| ADR-063-mmwave-sensor-fusion.md | ||
| ADR-064-multimodal-ambient-intelligence.md | ||
| ADR-065-happiness-scoring-seed-bridge.md | ||
| ADR-066-esp32-swarm-seed-coordinator.md | ||
| ADR-067-ruvector-v2.0.5-upgrade.md | ||
| ADR-068-per-node-state-pipeline.md | ||
| ADR-069-cognitum-seed-csi-pipeline.md | ||
| ADR-070-self-supervised-pretraining.md | ||
| ADR-071-ruvllm-training-pipeline.md | ||
| ADR-072-wiflow-architecture.md | ||
| ADR-073-multifrequency-mesh-scan.md | ||
| ADR-074-spiking-neural-csi-sensing.md | ||
| ADR-075-mincut-person-separation.md | ||
| ADR-076-csi-spectrogram-embeddings.md | ||
| ADR-077-novel-rf-sensing-applications.md | ||
| ADR-078-multifreq-mesh-applications.md | ||
| ADR-079-camera-ground-truth-training.md | ||
| ADR-080-qe-remediation-plan.md | ||
| ADR-081-adaptive-csi-mesh-firmware-kernel.md | ||
| ADR-082-pose-tracker-confirmed-output-filter.md | ||
| ADR-083-per-cluster-pi-compute-hop.md | ||
| ADR-084-rabitq-similarity-sensor.md | ||
| ADR-085-rabitq-pipeline-expansion.md | ||
| ADR-086-edge-novelty-gate.md | ||
| ADR-089-nvsim-nv-diamond-simulator.md | ||
| ADR-090-nvsim-lindblad-extension.md | ||
| ADR-091-stand-off-radar-tier-research.md | ||
| ADR-092-nvsim-dashboard-implementation.md | ||
| ADR-093-dashboard-gap-analysis.md | ||
| ADR-094-pointcloud-github-pages-deployment.md | ||
| ADR-095-rvcsi-edge-rf-sensing-platform.md | ||
| ADR-096-rvcsi-ffi-crate-layout.md | ||
| README.md | ||
Architecture Decision Records
This folder contains 44 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.
Why ADRs?
Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.
ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:
-
Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.
-
AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.
-
Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.
ADRs and Domain-Driven Design
The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:
- ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
- DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
- Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.
How ADRs are structured
Each ADR follows a consistent format:
- Context — What problem or gap prompted this decision
- Decision — What we chose to do and how
- Consequences — What improved, what got harder, and what risks remain
- References — Related ADRs, papers, and code paths
Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).
ADR Index
Hardware and firmware
| ADR | Title | Status |
|---|---|---|
| ADR-012 | ESP32 CSI Sensor Mesh for Distributed Sensing | Accepted (partial) |
| ADR-018 | ESP32 Development Implementation Path | Proposed |
| ADR-028 | ESP32 Capability Audit and Witness Record | Accepted |
| ADR-029 | RuvSense Multistatic Sensing Mode (TDM, channel hopping) | Proposed |
| ADR-032 | Multistatic Mesh Security Hardening | Accepted |
| ADR-039 | ESP32-S3 Edge Intelligence Pipeline (on-device vitals) | Accepted (hardware-validated) |
| ADR-040 | WASM Programmable Sensing (Tier 3) | Accepted |
| ADR-041 | WASM Module Collection (65 edge modules) | Accepted (hardware-validated) |
| ADR-044 | Provisioning Tool Enhancements | Proposed |
Signal processing and sensing
| ADR | Title | Status |
|---|---|---|
| ADR-013 | Feature-Level Sensing on Commodity Gear | Accepted |
| ADR-014 | SOTA Signal Processing Algorithms | Accepted |
| ADR-021 | Vital Sign Detection (breathing, heart rate) | Partial |
| ADR-030 | Persistent Field Model and Drift Detection | Proposed |
| ADR-033 | CRV Signal Line Sensing Integration | Proposed |
| ADR-037 | Multi-Person Pose Detection from Single ESP32 | Proposed |
| ADR-042 | Coherent Human Channel Imaging (beyond CSI) | Proposed |
Machine learning and training
| ADR | Title | Status |
|---|---|---|
| ADR-005 | SONA Self-Learning for Pose Estimation | Partial |
| ADR-006 | GNN-Enhanced CSI Pattern Recognition | Partial |
| ADR-015 | Public Dataset Strategy (MM-Fi, Wi-Pose) | Accepted |
| ADR-016 | RuVector Training Pipeline Integration | Accepted |
| ADR-017 | RuVector Signal + MAT Integration | Proposed |
| ADR-020 | Migrate AI Inference to Rust (ONNX Runtime) | Accepted |
| ADR-023 | Trained DensePose Model with RuVector Pipeline | Proposed |
| ADR-024 | Project AETHER: Contrastive CSI Embeddings | Required |
| ADR-027 | Project MERIDIAN: Cross-Environment Generalization | Proposed |
Platform and UI
| ADR | Title | Status |
|---|---|---|
| ADR-019 | Sensing-Only UI with Gaussian Splats | Accepted |
| ADR-022 | Windows WiFi Enhanced Fidelity (multi-BSSID) | Partial |
| ADR-025 | macOS CoreWLAN WiFi Sensing | Proposed |
| ADR-031 | RuView Sensing-First RF Mode | Proposed |
| ADR-034 | Expo React Native Mobile App | Accepted |
| ADR-035 | Live Sensing UI Accuracy and Data Transparency | Accepted |
| ADR-036 | Training Pipeline UI Integration | Proposed |
| ADR-043 | Sensing Server UI API Completion (14 endpoints) | Accepted |
Architecture and infrastructure
| ADR | Title | Status |
|---|---|---|
| ADR-001 | WiFi-Mat Disaster Detection Architecture | Accepted |
| ADR-002 | RuVector RVF Integration Strategy | Superseded |
| ADR-003 | RVF Cognitive Containers for CSI | Proposed |
| ADR-004 | HNSW Vector Search for Fingerprinting | Partial |
| ADR-007 | Post-Quantum Cryptography for Sensing | Proposed |
| ADR-008 | Distributed Consensus for Multi-AP | Proposed |
| ADR-009 | RVF WASM Runtime for Edge Deployment | Proposed |
| ADR-010 | Witness Chains for Audit Trail Integrity | Proposed |
| ADR-011 | Proof-of-Reality and Mock Elimination | Proposed |
| ADR-026 | Survivor Track Lifecycle (MAT crate) | Accepted |
| ADR-038 | Sublinear GOAP for Roadmap Optimization | Proposed |
| ADR-095 | rvCSI — Edge RF Sensing Runtime Platform | Proposed |
| ADR-096 | rvCSI — Crate Topology, the napi-c Shim, and the napi-rs Node Surface | Proposed |
Related
- DDD Domain Models — Bounded context definitions, aggregate roots, and ubiquitous language
- User Guide — Setup, API reference, and hardware instructions
- Build Guide — Building from source