From WiFi packets to AI detection
A six-stage pipeline turns invisible radio reflections into actionable human detection.
ESP32 or CSI-supported WiFi hardware collects Channel State Information packets.
Human movement changes how WiFi signals reflect, scatter, and arrive at the receiver.
RuView processes CSI amplitude and phase variations to extract motion features.
AI models analyze patterns for presence, gestures, motion, breathing, and heartbeat.
The system classifies events: occupancy, movement, vitals, and posture.
Results stream into real-time dashboards or downstream automations.
What is WiFi CSI?
Channel State Information is per-subcarrier amplitude and phase data extracted from WiFi packets. It exposes how the radio channel changes over time — including from human movement.
How WiFi Signals Detect Movement
When a person moves in the channel, multipath reflections shift. AI models learn the signature of those shifts to classify what's happening.
Signal Reflection Explained
Bodies are mostly water and reflect 2.4/5 GHz energy. The receiver sees subtle changes in delay, phase, and amplitude across subcarriers.
CSI Data Collection
Firmware on ESP32-class chips exposes CSI for every received frame, giving rich raw input for machine learning.
AI Inference Pipeline
Preprocessing, feature extraction, and a lightweight neural network produce predictions — usually under 100ms end to end.
Edge Processing
Everything runs locally on the device or a nearby host. No cloud round-trip is required.
Signal Visualization
Spectrograms, occupancy timelines, and per-subcarrier heatmaps make CSI interpretable to humans.
Limitations and Realistic Expectations
WiFi sensing is probabilistic. Through-wall metal, dense furniture, and interference all affect accuracy. Treat outputs as estimates.