/ruview-mmwave

name: ruview-mmwave
description: Set up and run RuView mmWave / FMCW radar sensing — ESP32-C6 + Seeed MR60BHA2 (60 GHz, heart rate / breathing rate / presence) and HLK-LD2410 (24 GHz, presence + distance), plus mmWave↔WiFi-CSI sensor fusion (48-byte fused vitals, MR60BHA2/LD2410 auto-detect, v0.5.0+). Use when the deployment includes a millimetre-wave radar alongside or instead of WiFi CSI.
allowed-tools: Bash Read Write Edit Glob Grep

RuView mmWave / FMCW Radar

The radio side-channel: 60 GHz and 24 GHz FMCW radar, standalone and fused with WiFi CSI.

Hardware

| Device | Port | Band | Provides | ~Cost | |--------|------|------|----------|-------| | ESP32-C6 + Seeed MR60BHA2 | COM4 (typical) | 60 GHz FMCW | Heart rate, breathing rate, presence | ~$15 | | HLK-LD2410 | — | 24 GHz FMCW | Presence + distance (gated zones) | ~$3 |

The C6 is RISC-V and can run the radar pipeline; it is **not** a WiFi-CSI node (use an ESP32-S3 for CSI). LD2410 is a UART module wired to a host or to the C6.

1. Firmware with mmWave fusion (v0.5.0+)

The ESP32 firmware auto-detects an attached MR60BHA2 or LD2410 and emits **48-byte fused vitals** records (CSI-derived + radar-derived, reconciled). Binary is ~12 KB larger than the CSI-only build. Build/flash as in `ruview-hardware-setup` (Windows: Python-subprocess; ESP-IDF v5.4 ≠ Git Bash). Recommended stable firmware tag: `v0.5.0-esp32` or later — see `docs/user-guide.md` release table.

# Provision the radar/fusion node (same provision.py; the firmware probes for the radar on boot)
python firmware/esp32-csi-node/provision.py --port COM8 --ssid "WiFi" --password "secret" --target-ip 192.168.1.20
# Confirm: serial monitor should report which radar was detected and start emitting fused vitals

2. mmWave ↔ WiFi-CSI fusion bridge (host side)

python scripts/mmwave_fusion_bridge.py            # bridges radar HR/BR + CSI → unified spatial model
node scripts/passive-radar.js                     # passive-radar style processing for exploration

The 3D point-cloud demo fuses **camera depth (MiDaS) + WiFi CSI + mmWave radar** → unified spatial model (~22 ms pipeline, 19K+ pts/frame; ADR-094). Drive it with `scripts/mmwave_fusion_bridge.py` plus the point-cloud front-end.

3. Standalone radar use

• **MR60BHA2 (60 GHz)** — best for contactless vitals on a (near-)stationary subject: blood pressure proxy, heart rate, breathing rate; $15 hardware, no wearable. See `examples/medical/README.md`.
• **LD2410 (24 GHz)** — best for cheap presence + coarse distance / gated zones; complements CSI presence (PIR-style fusion) for higher confidence.

4. When to use mmWave vs. WiFi CSI

| Situation | Prefer | |-----------|--------| | Contactless vitals, subject stationary, line of sight | **MR60BHA2** (cleaner HR/BR than CSI alone) | | Cheap, robust presence / occupancy in a defined zone | **LD2410** (or LD2410 + CSI) | | Through-wall presence / activity, no line of sight | **WiFi CSI** (mmWave doesn't penetrate walls) | | Pose / skeletons | **WiFi CSI** (WiFlow) — mmWave doesn't do this here | | Highest-confidence vitals | **Fusion** — 48-byte fused vitals reconcile CSI + radar | | Volumetric 3D | **Fusion** — camera depth + CSI + mmWave point cloud |

Reference

• Hardware tables: `README.md`, `docs/user-guide.md` (release table — v0.5.0 mmWave fusion notes, binary sizes)
• `scripts/mmwave_fusion_bridge.py`, `scripts/passive-radar.js`
• `examples/medical/README.md` (60 GHz mmWave vitals)
• ADR-094 (point-cloud GitHub Pages deployment)
• Validate firmware changes with the QEMU helpers and `ruview-verify`