ruv
a4bd2308b7
feat: ADR-069 ESP32 CSI → Cognitum Seed RVF pipeline (v0.5.4-esp32)
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Hardware-validated pipeline connecting ESP32-S3 CSI sensing to Cognitum
Seed (Pi Zero 2 W) edge intelligence appliance via 8-dim feature vectors.
Firmware:
- New 48-byte feature vector packet (magic 0xC5110003) at 1 Hz with
normalized presence, motion, breathing, heart rate, phase variance,
person count, fall detection, and RSSI
- Compressed frame magic reassigned 0xC5110003 → 0xC5110005
- Guard against uninitialized s_top_k read when count=0
Bridge (scripts/seed_csi_bridge.py):
- UDP→HTTPS ingest with bearer token, hash-based vector IDs
- --validate (kNN), --stats, --compact, --allowed-sources modes
- NaN/inf rejection, retry logic, SEED_TOKEN env var support
Validated on live hardware:
- 941 vectors ingested, 100% kNN exact match
- Witness chain SHA-256 verified (1,325 entries)
- 1,463 Rust tests passed, Python proof VERDICT: PASS
Research: 26 docs covering Arena Physica, Maxwell's equations in WiFi
sensing, SOTA survey 2025-2026, GOAP implementation plan
Security: removed hardcoded credentials, added NVS patterns to
.gitignore, source IP filtering, NaN validation
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 19:32:18 -04:00
ruv
4713a30402
docs: add README for happiness-vector example
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Quick start guide, 8-dim vector schema, multi-node swarm setup,
Seed query tool usage, privacy considerations, and file index.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-20 18:51:05 -04:00
rUv
2b8a7cc458
feat: happiness scoring pipeline + ESP32 swarm with Cognitum Seed ( #285 )
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* feat: happiness scoring pipeline with ESP32 swarm + Cognitum Seed coordinator
ADR-065: Hotel guest happiness scoring from WiFi CSI physiological proxies.
ADR-066: ESP32 swarm with Cognitum Seed as coordinator for multi-zone analytics.
Firmware:
- swarm_bridge.c/h: FreeRTOS task on Core 0, HTTP client with Bearer auth,
registers with Seed, sends heartbeats (30s) and happiness vectors (5s)
- nvs_config: seed_url, seed_token, zone_name, swarm intervals
- provision.py: --seed-url, --seed-token, --zone CLI args
- esp32-hello-world: capability discovery firmware for 4MB ESP32-S3 variant
WASM edge modules:
- exo_happiness_score.rs: 8-dim happiness vector from gait speed, stride
regularity, movement fluidity, breathing calm, posture, dwell time
(events 690-694, 11 tests, ESP32-optimized buffers + event decimation)
- ghost_hunter.rs standalone binary: 5.7 KB WASM, feature-gated default pipeline
RuView Live:
- --mode happiness dashboard with bar visualization
- --seed flag for Cognitum Seed bridge (urllib, background POST)
- HappinessScorer + SeedBridge classes (stdlib only, no deps)
Examples:
- seed_query.py: CLI tool (status, search, witness, monitor, report)
- provision_swarm.sh: batch provisioning for multi-node deployment
- happiness_vector_schema.json: 8-dim vector format documentation
Verified live: ESP32 on COM5 (4MB flash) registered with Seed at 10.1.10.236,
vectors flowing, witness chain growing (epoch 455, chain 1108).
Co-Authored-By: claude-flow <ruv@ruv.net>
* ci: raise firmware binary size gate to 1100 KB for HTTP client stack
The swarm bridge (ADR-066) adds esp_http_client for Seed communication,
which pulls in the HTTP/TLS stack (~150 KB). Binary grew from ~978 KB to
~1077 KB. Raise the gate from 950 KB to 1100 KB. Still fits comfortably
in both 4MB (1856 KB OTA slot, 43% free) and 8MB flash variants.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-20 18:46:34 -04:00
ruv
7eba8c7286
feat: 10-in-1 medical vitals suite from single mmWave sensor
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examples/medical/vitals_suite.py — all 10 capabilities:
1. Heart rate (continuous)
2. Breathing rate (continuous)
3. Blood pressure estimation (HRV-based)
4. HRV stress analysis (SDNN, RMSSD, pNN50)
5. Sleep stage classification (awake/light/deep/REM)
6. Apnea event detection (BR=0 for >10s, AHI scoring)
7. Cough detection (BR spike > 2.5x baseline)
8. Snoring detection (periodic high-amplitude BR)
9. Activity state (resting/active/exercising)
10. Meditation quality scorer (BR regularity + HR + HRV)
Uses Welford online stats, zero-crossing analysis, and
variability-based state classification. Single $15 sensor.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 18:05:42 -04:00
ruv
a7d417837f
feat: RuView Live v2 — RuVector signal processing integration
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Ported 5 RuVector/RuvSense algorithms from Rust to Python:
- WelfordStats (field_model.rs): online mean/variance/z-score
- VitalAnomalyDetector (vitals/anomaly.rs): Welford z-score apnea/tachy/brady
- LongitudinalTracker (ruvsense/longitudinal.rs): drift detection over time
- CoherenceScorer (ruvsense/coherence.rs): signal quality with decay
- HRVAnalyzer (vitals/heartrate.rs): SDNN, RMSSD, pNN50, LF/HF spectral
Live verified: detected HR anomaly (2.5sd drop) and BR drift (2.2sd rise)
from real mmWave + CSI data. Full session baselines tracked for 3 metrics.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 17:03:29 -04:00
ruv
4239dfa35a
feat: RuView Live unified dashboard + improved examples README
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ruview_live.py: single-file dashboard that auto-detects CSI and
mmWave sensors, displays fused vitals (HR, BR, BP, stress/HRV),
environment (light, RSSI, RF fingerprint), presence, and events.
Tested live: CSI 1000 frames/60s (17 Hz), light trending 7.4→6.0
lux, RSSI -57 to -72 dBm. Handles graceful degradation when
sensors are unavailable.
README: updated with unified dashboard as primary entry point,
hardware table with capabilities, expanded quick start.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 16:56:11 -04:00
ruv
24ea88cbe0
feat: 4 sensing examples — sleep apnea, stress, room environment
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examples/sleep/apnea_screener.py — detects breathing cessation
events (>10s), computes AHI score, classifies OSA severity.
examples/stress/hrv_stress_monitor.py — real-time SDNN/RMSSD
from mmWave HR, stress level with visual bar.
examples/environment/room_monitor.py — dual-sensor (CSI + mmWave)
room awareness: occupancy, light, RF fingerprint, activity events.
examples/README.md — index with hardware table and quick start.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 16:50:04 -04:00
ruv
ef582b4429
docs: medical examples README + link from root README
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- examples/medical/README.md: full guide for BP estimator,
hardware requirements, sample output, accuracy table, AHA
categories, disclaimer, RuView integration explanation
- README.md: added Medical Examples to documentation table
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 16:36:45 -04:00
ruv
8318f9c677
feat: contactless blood pressure estimation via mmWave HRV (examples/medical)
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Reads real-time heart rate from MR60BHA2 60 GHz mmWave sensor and
estimates BP trends using HR/HRV correlation model:
- Mean HR → baseline SBP/DBP
- SDNN (HRV) → sympathetic/parasympathetic adjustment
- LF/HF spectral ratio → fine adjustment (with numpy)
- Optional calibration with a real BP reading
Verified on real hardware: 125/83 mmHg estimate from 35 HR samples
over 60 seconds at 84 bpm mean HR with 91ms SDNN.
NOT A MEDICAL DEVICE — research/wellness tracking only.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 16:24:47 -04:00
