Sense Humans with WiFi – Ruview

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Summary

A review of techniques for sensing human presence, movement, and vital signs using WiFi signals.

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ruvnet/RuView

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RuView is an open-source WiFi sensing platform that uses Channel State Information (CSI) from low-cost ESP32 sensors to detect people, track movement, measure vital signs, and estimate pose through walls without cameras or wearables. The system runs entirely on edge hardware with cryptographic attestation and uses spiking neural networks for local adaptation.

@IndieDevHailey: Terrifying! You can tell what people are doing behind the next wall just using home Wi-Fi! The open-source project RuView has skyrocketed to over 50k stars on GitHub, totally blowing up! No cameras needed, no wearable devices required—just ordinary home Wi-Fi signals allow you to see through walls: - How many people are next door, their exact locations, whether they’re walking or lying down—all visible - Real-time human pose estimation (17 keypoints) - Automatically measures breathing and heart rate while sleeping - Instant alerts for falls, with highly accurate action recognition

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The open-source project RuView leverages Wi-Fi signals and AI technology to achieve camera-free through-wall sensing, capable of real-time human pose recognition, breathing monitoring, and fall detection. It has garnered significant attention on GitHub. Emphasizing privacy and security, the project processes all data locally, supporting easy deployment via ESP32 or Docker.

Show HN: Mezz, a curl-able WiFi sandbox for IoT pentesting

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Mezz is a self-contained WiFi sandbox tool for inspecting IoT device traffic, providing an isolated network with DNS logging and optional MITM proxy, all deployable via Docker on a Linux host with AP-capable WiFi.