This is the Linux app named RuView whose latest release can be downloaded as v0.6.0--Pre-TrainedModelsonHuggingFace+17SensingAppssourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named RuView with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.
- 5. From the OnWorks Linux OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application, install it and run it.
SCREENSHOTS
Ad
RuView
DESCRIPTION
RuView is an edge AI perception system that transforms ordinary WiFi signals into real-time environmental sensing and human pose estimation. Built on the concept of WiFi DensePose, it analyzes disturbances in WiFi Channel State Information (CSI) caused by human movement to reconstruct body position, breathing patterns, heart rate, and presence. Unlike traditional vision systems, RuView operates without cameras, wearables, or cloud connectivity, making it a privacy-first sensing solution. The system runs on low-cost hardware such as ESP32 sensor meshes and performs signal processing and machine learning directly at the edge. By learning the RF signature of each environment over time, RuView adapts automatically to different spaces and improves its sensing accuracy. Designed for applications ranging from healthcare monitoring to disaster response, it enables spaces to gain spatial awareness using the radio signals already present in the environment.
Features
- WiFi DensePose technology that reconstructs human body pose using only WiFi signal disturbances.
- Contactless vital sign monitoring that detects breathing and heart rate without wearables.
- Through-wall sensing that identifies presence, motion, and activity even without line-of-sight.
- Edge AI architecture running on low-cost ESP32 sensor meshes with no internet or cloud dependency.
- Self-learning models that adapt to each room’s RF signature and improve over time.
- Real-time sensing pipeline with REST APIs, WebSocket streaming, and visualization dashboards for monitoring environments.
Programming Language
Python, Rust
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/ruview.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.