@Smartpigai: It's not just the camera that's shocking; this time it's 'WiFi itself seeing you.' An open-source project called RuView has gained over 70k stars on GitHub, and its idea is somewhat outrageous: The ordinary WiFi signal in your home is not just an internet tool; it can also become an 'invisible sensing radar.' It can do these things...

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Introduces an open-source project called RuView that uses CSI data from ordinary WiFi signals and AI models to achieve camera-free indoor human perception, including people counting, pose estimation, breathing and heart rate monitoring, and fall detection. It has gained over 70k stars on GitHub.

It's not just the camera that's shocking; this time it's "WiFi itself seeing you." An open-source project called RuView has gained over 70k stars on GitHub, and its idea is somewhat outrageous: The ordinary WiFi signal in your home is not just an internet tool; it can also become an "invisible sensing radar." It can do these things: - Without a camera, it can sense how many people are in a room and their positions. - Recognize whether people are walking, sitting, or lying down. - Real-time human pose estimation (keypoint level). - Estimate breathing and heart rate during sleep. - Trigger alerts for abnormal movements (such as falls). It uses WiFi CSI signals + AI models at the core, turning "signal perturbations" in the environment into motion information. Even more astonishing: A basic version can run on a regular ESP32, and you can even experience it with just a Docker environment. No cameras, no wearable devices, no recording — but "what's happening in the space" is digitized. Privacy is starting to get complicated, too.
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Cached at: 06/08/26, 05:19 AM

Shocking isn’t just the camera — this time, “the WiFi itself is seeing you.”

An open-source project called RuView has already garnered 70,000+ stars on GitHub, and its approach is a bit mind-blowing:

Your home’s ordinary WiFi signal isn’t just for internet access — it can become an “invisible sensing radar.”

Here’s what it can do:

  • Detect how many people are in a room and where they are — without any camera
  • Recognize whether someone is walking, sitting, or lying down
  • Real-time human pose estimation (keypoint-level)
  • Estimate breathing rate and heart rate during sleep
  • Trigger alerts for abnormal movements (e.g., falls)

The underlying tech uses WiFi CSI signals + AI models to turn environmental “signal disturbances” into motion information.

What’s even more wild:

  • A basic version can run on a regular ESP32
  • You can even try it with just a Docker environment
  • No cameras, no wearable devices, no recording — yet “what’s happening in the space” is fully digitized

Privacy has just gotten a bit more complicated.

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@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.

ruvnet/RuView

GitHub Trending (daily)

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.