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⭐ Tutorial: Windows WiFi Sensing Quick Start (ADR-013) #36

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opened 2026-03-01 03:02:38 +08:00 by ruvnet · 3 comments
ruvnet commented 2026-03-01 03:02:38 +08:00 (Migrated from github.com)
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WiFi-DensePose: Windows WiFi Sensing Quick Start (ADR-013)

Zero-cost presence and motion detection using your existing Windows WiFi — no special hardware needed.

This tutorial walks through setting up the ADR-013 commodity sensing pipeline on a Windows laptop. The full pipeline reads real RSSI from your WiFi adapter via netsh, extracts spectral and statistical features, and classifies presence/motion in real-time.

What You Need

Item Notes
Windows 10/11 laptop Any WiFi adapter works
Connected WiFi network Must be associated with an AP
Python 3.10+ With pip
~5 minutes No hardware mods, no drivers, no root

What You'll Get

Capability Works? How
Presence detection ✅ Yes RSSI variance threshold
Motion detection (still/active) ✅ Yes Spectral band power
Breathing detection ❌ No RSSI resolution too coarse
Heartbeat ❌ No Not possible with RSSI
Pose estimation ❌ No Requires CSI (see issue #34)

Step 1: Clone and Install

git clone https://github.com/ruvnet/wifi-densepose.git
cd wifi-densepose
pip install numpy scipy

Step 2: Verify WiFi is Connected

netsh wlan show interfaces

You should see State: connected and an Rssi value (e.g., -39). If disconnected:

netsh wlan connect name="YourNetworkName"

Step 3: Run a Single RSSI Sample

# Quick test — run from the repo root
python -c "
import sys; sys.path.insert(0, '.')
from v1.src.sensing.rssi_collector import WindowsWifiCollector
c = WindowsWifiCollector(interface='Wi-Fi')
s = c.collect_once()
print(f'RSSI: {s.rssi_dbm} dBm, Quality: {s.link_quality:.0%}')
"

Expected output:

RSSI: -39.0 dBm, Quality: 94%

Step 4: Run the Full Pipeline (Feature Extraction + Classification)

python -c "
import sys, time; sys.path.insert(0, '.')
from v1.src.sensing.rssi_collector import WindowsWifiCollector
from v1.src.sensing.feature_extractor import RssiFeatureExtractor
from v1.src.sensing.classifier import PresenceClassifier

collector = WindowsWifiCollector(interface='Wi-Fi', sample_rate_hz=2.0)
extractor = RssiFeatureExtractor(window_seconds=15.0)
classifier = PresenceClassifier(presence_variance_threshold=0.3)

collector.start()
print('Collecting 15 seconds of RSSI data...')
time.sleep(15)
collector.stop()

samples = collector.get_samples()
features = extractor.extract(samples)
result = classifier.classify(features)

print(f'Samples:   {len(samples)}')
print(f'RSSI mean: {features.mean:.1f} dBm')
print(f'Variance:  {features.variance:.4f}')
print(f'Motion:    {features.motion_band_power:.4f}')
print(f'Verdict:   {result.motion_level.value} ({result.confidence:.0%})')
"

Step 5: Live Monitoring (Walk Around to Test)

# From repo root
set PYTHONPATH=. && python v1/tests/integration/live_sense_monitor.py

This prints a live dashboard every 3 seconds:

[14:00:05] RSSI= -37.0dBm var=0.000 motion_e=0.0000 => absent         (100%)
[14:00:08] RSSI= -37.0dBm var=0.000 motion_e=0.0000 => absent         (100%)
[14:00:26] RSSI= -37.1dBm var=0.120 motion_e=0.0016 => absent          (76%)  ← RSSI changing!
[14:00:32] RSSI= -41.3dBm var=4.850 motion_e=1.2300 => active          (95%)  ← MOTION DETECTED

To trigger detection: Walk between your laptop and the WiFi router. This causes 3-10+ dBm RSSI swings that the classifier picks up as ACTIVE motion.

Press Ctrl+C to stop and see a summary.

Step 6: Use the CommodityBackend API

from v1.src.sensing.backend import CommodityBackend, Capability
from v1.src.sensing.rssi_collector import WindowsWifiCollector

collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=2.0)
backend = CommodityBackend(collector=collector)

print(backend.get_capabilities())
# {<Capability.PRESENCE>, <Capability.MOTION>}

backend.start()
# ... wait for data collection ...
result = backend.get_result()
print(result.motion_level)   # MotionLevel.ABSENT / PRESENT_STILL / ACTIVE
print(result.confidence)     # 0.0 to 1.0
backend.stop()

Step 7: Run the Tests

# Unit tests (36 tests, no WiFi needed — uses SimulatedCollector)
python -m pytest v1/tests/unit/test_sensing.py -v -o "addopts="

# Live integration tests (5 tests, requires connected WiFi)
python -m pytest v1/tests/integration/test_windows_live_sensing.py -v -o "addopts=" -s

How It Works

Windows WiFi (netsh)          Feature Extraction              Classification
┌─────────────────┐     ┌───────────────────────┐     ┌──────────────────┐
│ netsh wlan show  │     │ Hann-windowed FFT     │     │ Variance > 0.3?  │
│ interfaces       │────▶│ Band power analysis   │────▶│  → PRESENT       │
│                  │     │ CUSUM change-point    │     │ Motion energy?   │
│ RSSI: -39 dBm   │     │ Rolling statistics    │     │  → STILL/ACTIVE  │
└─────────────────┘     └───────────────────────┘     └──────────────────┘

Pipeline: WindowsWifiCollector → RssiFeatureExtractor (FFT, CUSUM, spectral bands) → PresenceClassifier (rule-based, interpretable)

Limitations (Honest Assessment)

Limitation Why
RSSI quantized to 1 dBm netsh reports integers; sub-dBm variation invisible
~2 Hz max sample rate netsh takes 200-400ms per call
No breathing/heartbeat Requires sub-dBm resolution (use ESP32 CSI instead)
Best for coarse motion Person must cross the WiFi signal path for large RSSI swings
Single receiver only Multi-receiver fusion requires multiple machines

Upgrade Path

For higher-fidelity sensing (respiration, fine motion, multi-person):

Verified On

  • OS: Windows 11 Home 10.0.26200
  • Adapter: Intel Wi-Fi 7 BE201 320MHz
  • Network: WPA2-Personal, 5 GHz 802.11ax
  • RSSI observed: -37 to -40 dBm
  • Tests: 36 unit + 5 integration = 41 passed
  • Python: 3.13, numpy, scipy

Related

## WiFi-DensePose: Windows WiFi Sensing Quick Start (ADR-013) **Zero-cost presence and motion detection using your existing Windows WiFi — no special hardware needed.** This tutorial walks through setting up the ADR-013 commodity sensing pipeline on a Windows laptop. The full pipeline reads real RSSI from your WiFi adapter via `netsh`, extracts spectral and statistical features, and classifies presence/motion in real-time. --- ### What You Need | Item | Notes | |------|-------| | Windows 10/11 laptop | Any WiFi adapter works | | Connected WiFi network | Must be associated with an AP | | Python 3.10+ | With pip | | ~5 minutes | No hardware mods, no drivers, no root | ### What You'll Get | Capability | Works? | How | |-----------|--------|-----| | Presence detection | ✅ Yes | RSSI variance threshold | | Motion detection (still/active) | ✅ Yes | Spectral band power | | Breathing detection | ❌ No | RSSI resolution too coarse | | Heartbeat | ❌ No | Not possible with RSSI | | Pose estimation | ❌ No | Requires CSI (see issue #34) | --- ### Step 1: Clone and Install ```bash git clone https://github.com/ruvnet/wifi-densepose.git cd wifi-densepose pip install numpy scipy ``` ### Step 2: Verify WiFi is Connected ```powershell netsh wlan show interfaces ``` You should see `State: connected` and an `Rssi` value (e.g., `-39`). If disconnected: ```powershell netsh wlan connect name="YourNetworkName" ``` ### Step 3: Run a Single RSSI Sample ```python # Quick test — run from the repo root python -c " import sys; sys.path.insert(0, '.') from v1.src.sensing.rssi_collector import WindowsWifiCollector c = WindowsWifiCollector(interface='Wi-Fi') s = c.collect_once() print(f'RSSI: {s.rssi_dbm} dBm, Quality: {s.link_quality:.0%}') " ``` Expected output: ``` RSSI: -39.0 dBm, Quality: 94% ``` ### Step 4: Run the Full Pipeline (Feature Extraction + Classification) ```python python -c " import sys, time; sys.path.insert(0, '.') from v1.src.sensing.rssi_collector import WindowsWifiCollector from v1.src.sensing.feature_extractor import RssiFeatureExtractor from v1.src.sensing.classifier import PresenceClassifier collector = WindowsWifiCollector(interface='Wi-Fi', sample_rate_hz=2.0) extractor = RssiFeatureExtractor(window_seconds=15.0) classifier = PresenceClassifier(presence_variance_threshold=0.3) collector.start() print('Collecting 15 seconds of RSSI data...') time.sleep(15) collector.stop() samples = collector.get_samples() features = extractor.extract(samples) result = classifier.classify(features) print(f'Samples: {len(samples)}') print(f'RSSI mean: {features.mean:.1f} dBm') print(f'Variance: {features.variance:.4f}') print(f'Motion: {features.motion_band_power:.4f}') print(f'Verdict: {result.motion_level.value} ({result.confidence:.0%})') " ``` ### Step 5: Live Monitoring (Walk Around to Test) ```bash # From repo root set PYTHONPATH=. && python v1/tests/integration/live_sense_monitor.py ``` This prints a live dashboard every 3 seconds: ``` [14:00:05] RSSI= -37.0dBm var=0.000 motion_e=0.0000 => absent (100%) [14:00:08] RSSI= -37.0dBm var=0.000 motion_e=0.0000 => absent (100%) [14:00:26] RSSI= -37.1dBm var=0.120 motion_e=0.0016 => absent (76%) ← RSSI changing! [14:00:32] RSSI= -41.3dBm var=4.850 motion_e=1.2300 => active (95%) ← MOTION DETECTED ``` **To trigger detection:** Walk between your laptop and the WiFi router. This causes 3-10+ dBm RSSI swings that the classifier picks up as ACTIVE motion. Press `Ctrl+C` to stop and see a summary. ### Step 6: Use the CommodityBackend API ```python from v1.src.sensing.backend import CommodityBackend, Capability from v1.src.sensing.rssi_collector import WindowsWifiCollector collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=2.0) backend = CommodityBackend(collector=collector) print(backend.get_capabilities()) # {<Capability.PRESENCE>, <Capability.MOTION>} backend.start() # ... wait for data collection ... result = backend.get_result() print(result.motion_level) # MotionLevel.ABSENT / PRESENT_STILL / ACTIVE print(result.confidence) # 0.0 to 1.0 backend.stop() ``` ### Step 7: Run the Tests ```bash # Unit tests (36 tests, no WiFi needed — uses SimulatedCollector) python -m pytest v1/tests/unit/test_sensing.py -v -o "addopts=" # Live integration tests (5 tests, requires connected WiFi) python -m pytest v1/tests/integration/test_windows_live_sensing.py -v -o "addopts=" -s ``` --- ### How It Works ``` Windows WiFi (netsh) Feature Extraction Classification ┌─────────────────┐ ┌───────────────────────┐ ┌──────────────────┐ │ netsh wlan show │ │ Hann-windowed FFT │ │ Variance > 0.3? │ │ interfaces │────▶│ Band power analysis │────▶│ → PRESENT │ │ │ │ CUSUM change-point │ │ Motion energy? │ │ RSSI: -39 dBm │ │ Rolling statistics │ │ → STILL/ACTIVE │ └─────────────────┘ └───────────────────────┘ └──────────────────┘ ``` **Pipeline:** `WindowsWifiCollector` → `RssiFeatureExtractor` (FFT, CUSUM, spectral bands) → `PresenceClassifier` (rule-based, interpretable) ### Limitations (Honest Assessment) | Limitation | Why | |-----------|-----| | RSSI quantized to 1 dBm | `netsh` reports integers; sub-dBm variation invisible | | ~2 Hz max sample rate | `netsh` takes 200-400ms per call | | No breathing/heartbeat | Requires sub-dBm resolution (use ESP32 CSI instead) | | Best for coarse motion | Person must cross the WiFi signal path for large RSSI swings | | Single receiver only | Multi-receiver fusion requires multiple machines | ### Upgrade Path For higher-fidelity sensing (respiration, fine motion, multi-person): - **ESP32 CSI mesh** (ADR-012): $54 starter kit, 56 subcarrier amplitudes, 20+ Hz → [Tutorial #34](https://github.com/ruvnet/wifi-densepose/issues/34) - **Pre-built firmware**: [Release v0.1.0-esp32](https://github.com/ruvnet/wifi-densepose/releases/tag/v0.1.0-esp32) --- ### Verified On - **OS:** Windows 11 Home 10.0.26200 - **Adapter:** Intel Wi-Fi 7 BE201 320MHz - **Network:** WPA2-Personal, 5 GHz 802.11ax - **RSSI observed:** -37 to -40 dBm - **Tests:** 36 unit + 5 integration = **41 passed** - **Python:** 3.13, numpy, scipy ### Related - ADR-013: [Feature-Level Sensing on Commodity Gear](https://github.com/ruvnet/wifi-densepose/blob/main/docs/adr/ADR-013-feature-level-sensing-commodity-gear.md) - ADR-012: [ESP32 CSI Sensor Mesh](https://github.com/ruvnet/wifi-densepose/blob/main/docs/adr/ADR-012-esp32-csi-sensor-mesh.md) - Issue #34: [ESP32 CSI Tutorial](https://github.com/ruvnet/wifi-densepose/issues/34)
ruvnet commented 2026-03-01 12:25:51 +08:00 (Migrated from github.com)
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Update: Windows WiFi Sensing Now Includes Vital Signs

The sensing server used in this tutorial now supports vital sign detection from Windows WiFi RSSI data:

What's new

  • Breathing rate estimation from RSSI variance patterns (requires stationary subject near AP)
  • Motion classification with confidence scoring
  • SONA adaptation: The server can adapt to your specific environment using LoRA-based online learning — detecting baseline drift and re-calibrating automatically

Try it

cd rust-port/wifi-densepose-rs
cargo build --release -p wifi-densepose-sensing-server

# Windows WiFi mode with vital signs
./target/release/sensing-server --source windows --tick-ms 500

# Check vital signs
curl http://localhost:3000/api/v1/vital-signs

Note: RSSI-based vital sign detection has lower fidelity than ESP32 CSI. For accurate breathing/heartbeat monitoring, CSI hardware (ESP32-S3) is recommended. RSSI mode is best for presence detection and coarse motion classification.

— Ruflo AI

## Update: Windows WiFi Sensing Now Includes Vital Signs The sensing server used in this tutorial now supports vital sign detection from Windows WiFi RSSI data: ### What's new - **Breathing rate estimation** from RSSI variance patterns (requires stationary subject near AP) - **Motion classification** with confidence scoring - **SONA adaptation**: The server can adapt to your specific environment using LoRA-based online learning — detecting baseline drift and re-calibrating automatically ### Try it ```bash cd rust-port/wifi-densepose-rs cargo build --release -p wifi-densepose-sensing-server # Windows WiFi mode with vital signs ./target/release/sensing-server --source windows --tick-ms 500 # Check vital signs curl http://localhost:3000/api/v1/vital-signs ``` Note: RSSI-based vital sign detection has lower fidelity than ESP32 CSI. For accurate breathing/heartbeat monitoring, CSI hardware (ESP32-S3) is recommended. RSSI mode is best for presence detection and coarse motion classification. — Ruflo AI
ruvnet commented 2026-03-01 12:48:43 +08:00 (Migrated from github.com)
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Update: Docker + Rust Alternatives Available

In addition to the Python pipeline described above, you can now use the Rust sensing server via Docker:

docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 -p 3001:3001 ruvnet/wifi-densepose:latest

docker pull ruvnet/wifi-densepose:python
docker run -p 8765:8765 -p 8080:8080 ruvnet/wifi-densepose:python
Feature Python (RSSI) Rust (RSSI + CSI)
Presence detection Yes Yes
Motion classification Yes Yes
Vital signs No Yes (with ESP32 CSI)
Benchmark throughput ~200 fps 11,665 fps
Docker image size 569 MB 132 MB
RVF model export No Yes

For Windows WiFi RSSI-only sensing, the Python tutorial in this issue still works as-is. Both stacks via docker-compose: cd docker && docker compose up

## Update: Docker + Rust Alternatives Available In addition to the Python pipeline described above, you can now use the Rust sensing server via Docker: ```bash docker pull ruvnet/wifi-densepose:latest docker run -p 3000:3000 -p 3001:3001 ruvnet/wifi-densepose:latest docker pull ruvnet/wifi-densepose:python docker run -p 8765:8765 -p 8080:8080 ruvnet/wifi-densepose:python ``` | Feature | Python (RSSI) | Rust (RSSI + CSI) | |---------|--------------|-------------------| | Presence detection | Yes | Yes | | Motion classification | Yes | Yes | | Vital signs | No | Yes (with ESP32 CSI) | | Benchmark throughput | ~200 fps | 11,665 fps | | Docker image size | 569 MB | 132 MB | | RVF model export | No | Yes | For Windows WiFi RSSI-only sensing, the Python tutorial in this issue still works as-is. Both stacks via docker-compose: `cd docker && docker compose up`
Ace-jack-aCe commented 2026-03-01 14:47:04 +08:00 (Migrated from github.com)
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Reference: dearsky/wifi-densepose#36
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