**Quick answer:** RuView is a 2026 open-source WiFi-sensing project that reads Channel State Information from cheap $8 ESP32-S3 boards and reconstructs human pose, breathing rate, and heart rate through walls. Rust firmware + Python bindings + Candle inference. Ships Home Assistant, Matter, MQTT bridges. 82.69% PCK@20 on MM-Fi — genuine state of the art for camera-free sensing.
What RuView actually does
Every WiFi router in your home broadcasts continuous 2.4 GHz and 5 GHz signals. Those signals reflect off walls, furniture, and people. RuView captures Channel State Information (CSI) — the amplitude and phase of each subcarrier — and feeds it through a Rust signal-processing pipeline (Hampel filtering, SpotFi, Fresnel gating) into a transformer backbone that infers what shape and motion produced those reflections.
Output:
- 17-point body keypoints — pose skeleton, no camera
- Breathing rate 6-30 BPM
- Heart rate 40-120 BPM
- Room-level occupancy and movement heatmap
- Real-time. Through drywall. In the dark.
Why this matters right now
Camera-based surveillance is under legal pressure (GDPR, India's DPDPA 2023, California's CCPA amendments). Meanwhile eldercare, occupancy sensing, and smart-home automation demand always-on presence detection. WiFi sensing solves both — it does not capture identifiable data, so most privacy law does not attach.
The concept has been academic for a decade (MIT CSAIL's WiFi-DensePose). What is new in RuView:
- Runs on $8 ESP32-S3 chips, not $10k research NICs
- Rust firmware + safetensors + Candle — memory-safe, fast, deployable
- Home Assistant + Matter + MQTT bridges included — 15-min integration into any smart-home
- Open weights on Hugging Face (
ruvnet/wifi-densepose-pretrained) - Public leaderboard at ruvnet/aether-arena on HF Spaces
Live demos worth 5 minutes of your time
- **Live Observatory** — real-time pose skeleton from WiFi in your browser
- **Dual-Modal Pose Fusion** — webcam + WiFi CSI side-by-side to show they agree
- **3D Point Cloud Viewer** — spatial occupancy in three dimensions
- **Pretrained model on HF** — 82.3% temporal-triplet accuracy, download and run
Try it in 90 seconds (simulated data, no hardware)
docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 ruvnet/wifi-densepose:latest
# open http://localhost:3000You will get the full pipeline with synthetic CSI streams. No ESP32 required to evaluate.
Real hardware setup (~₹800)
- Buy one ESP32-S3-DevKitC-1 board from Robu.in or Amazon India (~₹700)
- Flash the RuView firmware:
espflash flash target/riscv32imc-esp-espidf/release/ruview - Place it 2-3 metres from your WiFi router with clear line of sight
- Run the RuView server on any Linux/macOS box:
cargo run --release --bin ruview-server - Point Home Assistant at the MQTT topic
ruview/#
That is it. You now have room-level pose + vitals sensing for ₹800 all-in.
Real deployments people are shipping in 2026
- Assisted living homes in Kerala — 40 residents, fall detection replaces overnight rounds
- Coworking spaces in Bangalore — desk occupancy without cameras (compliance-safe)
- Rescue teams in Turkey earthquake — WiFi-Mat variant detected breathing through rubble in 3 rescues
The trade-offs to know
- Calibration matters. CSI is noisy; you need a 10-15 min settle period per room to build a baseline.
- Metal reflects heavily. A metal cabinet or fridge in line of sight distorts readings.
- Two-body scenes are hard. Above 2 people in a small room, pose accuracy drops.
- Not medical-grade. Vital signs are close but not FDA/CE certified — good for wellness, not diagnosis.
Why we featured it on StackPicks
It is the first WiFi-sensing project that ships out-of-the-box for hobbyists AND has performance numbers real deployments can trust. Rust + open weights + Home Assistant + Matter bridges + a public leaderboard — this is what production-grade privacy-first sensing looks like when the license is MIT.
Where to go from here
- **Star the repo** if you plan to build anything eldercare, occupancy, or smart-home related in 2026
- **MIT CSAIL's original WiFi-DensePose paper** for the academic foundation
- **StackPicks AI/ML directory** for the rest of the 2026 open-source AI shortlist
Full repo: https://github.com/ruvnet/ruview