a85d4e31e4
Sets up docs/research/sota-2026-05-22/ as the autonomous-research output dir, with PROGRESS.md as the canonical 15-vector research agenda spanning spatial intelligence, RF features, RSSI-only, and exotic/long-horizon verticals. Cron d6e5c473 (*/10 * * * *) picks threads from this file and self-terminates at 2026-05-22 08:00 ET. First concrete contribution this tick — R5 subcarrier saliency: * examples/research-sota/r5_subcarrier_saliency.py: pure-numpy port of the count cog's Conv1d encoder + count head, computes per- subcarrier input×gradient saliency via central-difference. 128 samples × 56 subcarriers × 2 forward passes/subcarrier ≈ ~3 s on CPU, no GPU or framework dependency. * docs/research/sota-2026-05-22/R5-subcarrier-saliency.md: research note with motivation, method, novelty argument, and the first measured ranking. Top-8 subcarriers for cog-person-count v0.0.2: [41, 52, 30, 31, 10, 35, 2, 38]. Max/mean ratio 2.85x. * v2/crates/cog-person-count/cog/artifacts/saliency.json: machine- readable per-subcarrier saliency + top-K lists, so future-tick experiments (retrain at K=8/16/32) consume it without re-running. Key insight from the first measurement: top-8 saliency is *band- spread* (indices span 2-52), not concentrated. This directly raises R8's (RSSI-only) feasibility ceiling, because RSSI is a band- aggregate — it retains the integral of a band-spread signal. First- order estimate: RSSI-only should hit ~60% of full-CSI accuracy for the count task. R7 (adversarial defence) inherits a concrete defender- priority list: corroborate these 8 subcarriers across nodes. This commit is the first of many short, focused contributions over the next ~12 hours. PROGRESS.md is the canonical pointer for the next tick to pick up the next thread. |
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| 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