2e742305ba
ITU-R P.833-9 vegetation-attenuation model + ESP32-S3 link-budget solver produce bounded sensing range estimates per frequency and foliage density. Plus a biomechanics-grounded gait-frequency taxonomy spanning bears (0.5 Hz) to mice (15 Hz). Headline ranges (121 dB link budget, 10 dB SNR margin): freq sparse moderate dense 2.4 GHz 99.6 m 12.0 m 4.1 m 5 GHz 19.9 m 5.2 m 2.1 m The 2.4 GHz / sparse cell (~100 m) is the practical sweet spot — 10x camera-trap coverage, always-on rather than PIR-triggered. Honest scope called out explicitly: this is feasibility math, not field measurements. Animal cooperation, foliage flutter, regulatory limits, and BSSID-fingerprint degradation in remote forest are all real follow-up problems. Vertical applications (10-20 year horizon) catalogued: - Endangered-species population census - Wildlife corridor verification - Invasive-species early warning - Anti-poaching (human gait well-separated from wildlife) - Livestock-on-rangeland tracking - Agricultural pest control Cross-connects to: - R5 (saliency is task-specific — per-species classifier needs own saliency map, same lesson as R12) - R8 (wildlife sensing wants CSI not RSSI for per-subcarrier shape) - R9 (fingerprint K-NN primitive transfers to per-individual ID) - R7 (multi-link consistency for corridor coverage) Pure-NumPy, no framework deps. ITU model + binary search solver. Coordination: tick avoided PROGRESS.md to prevent races (horizon- tracker M3+ track concurrent at the time). Files: * examples/research-sota/r10_foliage_attenuation.py * examples/research-sota/r10_foliage_results.json * docs/research/sota-2026-05-22/R10-through-foliage-wildlife.md * docs/research/sota-2026-05-22/ticks/tick-6.md |
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| .. | ||
| environment | ||
| happiness-vector | ||
| medical | ||
| research-sota | ||
| sleep | ||
| stress | ||
| three.js | ||
| README.md | ||
| ruview_live.py | ||
README.md
Examples
Real-time sensing applications built on the RuView platform.
Unified Dashboard (start here)
pip install pyserial numpy
python examples/ruview_live.py --csi COM7 --mmwave COM4
The live dashboard auto-detects available sensors and displays fused vitals, environment data, and events in real-time. Works with any combination of sensors.
Individual Examples
| Example | Sensors | What It Does |
|---|---|---|
| ruview_live.py | CSI + mmWave + Light | Unified dashboard: HR, BR, BP, stress, presence, light, RSSI |
| Medical: Blood Pressure | mmWave | Contactless BP estimation from HRV |
| Medical: Vitals Suite | mmWave | 10-in-1: HR, BR, BP, HRV, sleep stages, apnea, cough, snoring, activity, meditation |
| Sleep: Apnea Screener | mmWave | Detects breathing cessation events, computes AHI |
| Stress: HRV Monitor | mmWave | Real-time stress level from heart rate variability |
| Environment: Room Monitor | CSI + mmWave | Occupancy, light, RF fingerprint, activity events |
Hardware
| Port | Device | Cost | What It Provides |
|---|---|---|---|
| COM7 | ESP32-S3 (WiFi CSI) | ~$9 | Presence, motion, breathing, heart rate (through walls) |
| COM4 | ESP32-C6 + Seeed MR60BHA2 | ~$15 | Precise HR/BR, presence, distance, ambient light |
Either sensor works alone. Both together enable fusion (mmWave 80% + CSI 20%).
Quick Start
pip install pyserial numpy
# Unified dashboard (recommended)
python examples/ruview_live.py --csi COM7 --mmwave COM4
# Blood pressure estimation
python examples/medical/bp_estimator.py --port COM4
# Sleep apnea screening (run overnight)
python examples/sleep/apnea_screener.py --port COM4 --duration 28800
# Stress monitoring (workday session)
python examples/stress/hrv_stress_monitor.py --port COM4 --duration 3600
# Room environment monitor
python examples/environment/room_monitor.py --csi-port COM7 --mmwave-port COM4
# CSI only (no mmWave)
python examples/ruview_live.py --csi COM7 --mmwave none