Wifi-Densepose

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wifi-densepose is an Open Source Data & Analytics tool that turns commodity WiFi signals into real-time human pose estimation, vital sign monitoring, and presence detection. It operates without cameras or wearables, using ESP32 sensors.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is wifi-densepose?

wifi-densepose, also known as RuView, is an open-source WiFi sensing platform that transforms ordinary WiFi signals into spatial intelligence. It enables real-time human pose estimation (17 COCO keypoints), vital sign monitoring (breathing and heart rate), and presence detection through walls, in the dark, and without cameras or wearables. The system leverages Channel State Information (CSI) from low-cost ESP32 sensors and can run entirely on edge hardware, integrating with Cognitum Seed for persistent memory and AI. It supports camera-free and camera-supervised training, offering high PCK@20 accuracy. The platform also generates real-time 3D point clouds by fusing camera depth, WiFi CSI, and mmWave radar, providing comprehensive environmental and human activity monitoring.

Best used for

Ideal for researchers and developers who need to implement non-intrusive human sensing, vital sign monitoring, and activity recognition. Especially valuable for applications requiring privacy-preserving solutions, such as smart homes, elder care, or security, where cameras are undesirable or impractical.

Common actions

monitor human activity

detect vital signs

estimate human pose

analyze WiFi signals

track presence

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Capabilities

Key features

Real-time human pose estimation
Vital sign monitoring
Through-wall presence detection
Edge intelligence
3D point cloud generation
Camera-free training

Target Audience

developersresearchersiot engineersai/ml engineers

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What hardware is required to use wifi-densepose for live sensing?

For full CSI capabilities including pose, breathing, and heartbeat, an ESP32-S3 ($9) is recommended, optionally paired with a Cognitum Seed (~$131) for persistent storage and AI integration. A mesh of 3-6 ESP32-S3 units can also be used. Research NICs like Intel 5300 or Atheros AR9580 also provide full CSI.

Can wifi-densepose operate without an internet connection or cloud services?

Yes, wifi-densepose is designed for low-power edge applications. It runs entirely on edge hardware, such as an ESP32 mesh paired with a Cognitum Seed, meaning no cloud, no cameras, and no internet connection are required for its core functionalities.

What is the accuracy of the pose estimation feature?

With camera-supervised training using MediaPipe and real ESP32 CSI data, wifi-densepose achieves 92.9% PCK@20 (Percentage of Correct Keypoints within 20% of torso size). This is based on a 5-minute data collection session with one ESP32-S3 and one webcam.

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