Getting Started with Wavey

Wavey is an open-source WiFi CSI sensing system. This page covers deployment architecture — for signal processing detail, see the sensing pipeline . For what to detect first, see the detection ladder .

Setup steps live in the GitHub repository — they change as the project matures.

Three-layer architecture

[ ESP32 CSI nodes ]  ->  [ Python pipeline ]  ->  [ console / automations ]
   capture CSI            preprocess + features       visualize + emit events

1. Sensing nodes (ESP32). Capture CSI from ambient WiFi traffic. See commodity CSI limits for hardware constraints.
2. Analysis pipeline (Python). Outlier rejection, amplitude features (primary on ESP32), phase sanitization for micro-motion, baseline learning, inference. See the sensing pipeline for the processing stack.
3. Console and integrations. Live visualization at console.waveyhq.dev ; events for automation and analytics .

Before you begin

• One or more CSI-capable ESP32 modules (any ESP32 family chip)
• A WiFi environment with traffic the nodes can sniff
• A host machine for the Python stack

Pick your first detection task

Start at the bottom of the detection ladder — the most robust tasks first:

1. Occupancy — change-point detection on a baseline
2. Motion — variance and spectral features
3. Presence — micro-Doppler in the respiration band

Each level adds preprocessing requirements and tuning complexity. Get occupancy working before attempting HAR. Every deployment needs its own empty-room baseline — cross-site transfer without per-site calibration remains an open problem on commodity hardware.

Follow development

• Code: github.com/waveyhq
• Console: console.waveyhq.dev
• Community: Discord
• Contact: [email protected]

Background reading: how it works , glossary , FAQ .