berkus
/
wifi-densepose
mirror of https://github.com/ruvnet/wifi-densepose.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
wifi-densepose/plugins/ruview/skills/ruview-quickstart/SKILL.md

3.1 KiB
Raw Permalink Blame History

name description allowed-tools
ruview-quickstart Onboarding and first-run for RuView (WiFi-DensePose) — Docker demo with simulated data, repo build, and the fastest path to a live sensing dashboard. Use when someone is new to RuView or wants the shortest path to "it works on my machine". Bash Read Write Edit Glob Grep

RuView Quickstart

Get a newcomer from zero to a running RuView sensing dashboard. Three tiers, pick the one that matches the hardware on hand.

Tier 0 — Docker, no hardware (2 minutes)

docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 ruvnet/wifi-densepose:latest
# open http://localhost:3000  — simulated CSI, full UI

Use this to demo the dashboard, explore the API, or develop UI without a sensor.

Tier 1 — Build the repo from source

# Rust workspace (1,400+ tests, ~2 min)
cd v2
cargo test --workspace --no-default-features

# Single-crate sanity check (no GPU)
cargo check -p wifi-densepose-train --no-default-features

# Python proof (deterministic SHA-256 pipeline check)
cd ..
python archive/v1/data/proof/verify.py   # must print VERDICT: PASS

If verify.py fails on a hash mismatch after a numpy/scipy bump:

python archive/v1/data/proof/verify.py --generate-hash
python archive/v1/data/proof/verify.py

Tier 2 — Live sensing with an ESP32-S3 ($9)

This is the real thing. Hand off to the ruview-hardware-setup skill for the flash/provision/monitor loop, then:

# Lightweight sensing server (consumes the ESP32 UDP CSI stream)
cd v2
cargo run -p wifi-densepose-sensing-server
# Live RF room scan / SNN learning helpers:
node ../scripts/rf-scan.js --port 5006
node ../scripts/snn-csi-processor.js --port 5006

What to know before you start

  • ESP32-C3 and the original ESP32 are NOT supported — single-core, can't run the CSI DSP pipeline. Use ESP32-S3 (8MB or 4MB) or ESP32-C6.
  • A single ESP32 has limited spatial resolution — 2+ nodes (or add a Cognitum Seed) for good results.
  • Camera-free pose accuracy is limited (~84s to train, modest PCK). For 92.9% PCK@20 use camera-supervised training (see ruview-model-training skill, ADR-079).
  • No cloud, no internet, no cameras required — everything runs on edge hardware.

Next steps to suggest

Goal Skill / command
Flash & provision an ESP32 node ruview-hardware-setup · /ruview-flash · /ruview-provision
Tune channels / MAC filter / edge modules ruview-configure
Run a sensing application (presence, vitals, pose, sleep, MAT) ruview-applications · /ruview-app
Train a pose / sensing model ruview-model-training · /ruview-train
Multistatic mesh, tomography, cross-viewpoint fusion ruview-advanced-sensing · /ruview-advanced
Verify the build + generate a witness bundle ruview-verify · /ruview-verify

Reference

  • README.md — feature matrix, hardware table, install options
  • docs/user-guide.md, docs/wifi-mat-user-guide.md, docs/build-guide.md, docs/TROUBLESHOOTING.md
  • docs/tutorials/, examples/ — runnable examples (environment, medical, sleep, stress, ruview_live.py)