wifi-densepose/examples
rUv 3d3d54d523
research(R3.1): physics-informed env prediction at raw-CSI level — NEGATIVE (architecture-error) (#723)
R3's 'next research lever' was: use R6.1 forward operator + room map
to predict env_sig without labelled examples in the new room. R6.1
shipped (tick 18); this tick implements the prediction.

Result: at raw-CSI level, all three approaches collapse to chance.

| Configuration                          | 1-shot K-NN |
|----------------------------------------|------------:|
| Within-room baseline                   |    100%    |
| Cross-room RAW                         |     10%    | (chance)
| Cross-room labelled MERIDIAN (oracle)  |     10%    | (chance)
| Cross-room physics-informed            |     10%    | (chance)

Even the LABELLED oracle fails at raw-CSI level -- which is the
diagnostic. The cross-room problem at raw-CSI level is fundamentally
harder than at the AETHER embedding level (R3 tick 12) because
position-dependent within-room variance dominates per-subject
signature when invariantisation hasn't been done.

Corrected architecture:
  raw CSI -> AETHER embedding -> physics-informed env subtraction -> K-NN
  (apply physics prediction at embedding level, NOT raw level)

AETHER does position-invariance; predicted-env then removes only the
room-shift component.

THIS IS THE LOOP'S THIRD KIND OF NEGATIVE RESULT:
1. Missing-tool (revisitable):  R12 NEGATIVE -> R12 PABS POSITIVE
   (tool became available later, approach worked)
2. Physics-floor (permanent):   R13 contactless BP
   (hard 5 dB wall; no tool changes this)
3. Architecture-error (correctable): R3.1 (this tick)
   (right idea, wrong application level; corrected architecture
   explicit but not yet implemented)

Categorising negatives by resolution path is itself a research
contribution.

Surfaces an architecture error BEFORE implementation. A future
engineer attempting 'subtract predicted env from raw CSI' would
waste weeks; R3.1 documents the failure path.

Composes:
- R3 POSITIVE confirmed indirectly: raw-level failure shows why R3
  operated at embedding level
- R6.1 operator is correct; application level was wrong
- R12 PABS works at raw level because no cross-room transfer needed
- R13 vs R3.1: two different kinds of negative

Honest scope: weak per-subject signature (body-size only), 3 positions
per room, geometry-specific. Richer biometric input or per-position-
clustering might partially rescue raw-level but defeats the no-label
spirit.

Coordination: ticks/tick-20.md, no PROGRESS.md edit.
2026-05-22 04:04:38 -04:00
..
environment feat: 4 sensing examples — sleep apnea, stress, room environment 2026-03-15 16:50:04 -04:00
happiness-vector chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00
medical feat: 10-in-1 medical vitals suite from single mmWave sensor 2026-03-15 18:05:42 -04:00
research-sota research(R3.1): physics-informed env prediction at raw-CSI level — NEGATIVE (architecture-error) (#723) 2026-05-22 04:04:38 -04:00
sleep feat: 4 sensing examples — sleep apnea, stress, room environment 2026-03-15 16:50:04 -04:00
stress feat: 4 sensing examples — sleep apnea, stress, room environment 2026-03-15 16:50:04 -04:00
three.js fix(three.js): graceful banner when X Bot.fbx 404s on gh-pages (#651) 2026-05-19 18:43:21 -04:00
README.md feat: 10-in-1 medical vitals suite from single mmWave sensor 2026-03-15 18:05:42 -04:00
ruview_live.py feat: happiness scoring pipeline + ESP32 swarm with Cognitum Seed (#285) 2026-03-20 18:46:34 -04:00

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