RuView
RuView is a WiFi-based sensing platform that detects people, monitors vital signs (breathing and heart rate), and tracks activity through walls using Channel State Information (CSI) from cheap ESP32 sensors—no cameras or wearables required. It integrates with Home Assistant, Apple Home, Google Home, and Alexa, and runs entirely on edge hardware with no cloud dependency.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | ruvnet/RuView |
| Owner | ruvnet |
| Primary language | Rust |
| License | MIT — OSI-approved |
| Stars | 78.8k |
| Forks | 10.6k |
| Open issues | 342 |
| Latest release | v0.8.3-esp32 (2026-06-27) |
| Last updated | 2026-07-08 |
| Source | https://github.com/ruvnet/RuView |
What RuView is
Written in Rust, RuView captures CSI data from ESP32 mesh networks and processes it through pretrained contrastive encoders (8 KB quantized model on Hugging Face) and spiking neural networks to infer occupancy, vital signs, pose (17-keypoint), and activity. The system supports cryptographic attestation via Ed25519, multi-frequency mesh scanning across 6 WiFi channels, and a 105-cog edge module registry for extensible on-device intelligence.
Get the RuView source
Clone the repository and explore it locally.
git clone https://github.com/ruvnet/RuView.gitcd RuView# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- ESP32 hardware BOM ~$140 for a node plus Cognitum Seed; scale to multiple rooms requires mesh deployment and MQTT broker or Matter bridge infrastructure.
- Initial ambient calibration takes ~30 seconds per deployment; spiking neural networks adapt locally but may require retraining or fine-tuning in novel RF environments.
- CSI quality and vital sign accuracy depend on antenna placement, room geometry, and distance to subjects (effective range ~5 m); multipath and through-wall sensing require site validation.
- Model quantization (8 KB 4-bit) trades accuracy for edge latency; full-precision training on GPU (~2.1 s on RTX 5080) but inference on Pi 5 cold-start ~8.4 ms.
- Cryptographic attestation (Ed25519 witness chain) is available but integration with existing audit/compliance workflows is not documented; requires review for regulated use.
When to avoid it — and what to weigh
- Require guaranteed medical-grade accuracy — Vital sign detection is real-time but not clinically validated. The v2 encoder reports 82.3% held-out temporal-triplet accuracy; breathing and heart rate use signal-processing heuristics (bandpass filters) that are environment-dependent and may drift without recalibration.
- Need production support and SLAs — Project is actively maintained (latest release June 2026) but is open-source without commercial support contracts. Production deployments rely on community issues (342 open), internal expertise, or commercial partnerships (not documented here).
- Building systems for non-technical users — Requires ESP32 hardware procurement, network configuration, MQTT or Matter bridge setup, and Home Assistant or custom integration. Not a plug-and-play consumer device; technical overhead is moderate to high.
- Operating in RF-hostile environments — Depends on stable WiFi channel state information. Heavy RF interference, Faraday-shielded spaces, or environments with frequent channel switching may degrade CSI quality and model accuracy.
License & commercial use
MIT License (SPDX: MIT). Permissive open-source license allowing commercial use, modification, and distribution with attribution. No copyleft restrictions.
MIT license permits commercial use without royalty. However, source/dependency licensing of Cognitum Seed, pretrained models on Hugging Face, and any proprietary Cog binaries (aarch64/x86_64 signed binaries on GCS) must be verified independently. Recommend legal review before shipping in commercial products.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
No claims made about security posture in provided data. CSI-based sensing is inherently passive (no camera/microphone). Cryptographic attestation via Ed25519 witness chain is mentioned but not detailed; recommend threat modeling for your RF environment. MQTT and Matter integrations should use standard security best practices (TLS, authentication). No penetration test data or vulnerability disclosure policy provided.
Alternatives to consider
mmWave radar (Texas Instruments, Infineon, Qorvo)
Purpose-built for occupancy and vital sign detection; higher accuracy and range (~10 m) but higher cost (~$50–200/node) and more power consumption than WiFi CSI.
Ultra-wideband (UWB) localization (Decawave, Qorvo DW1000)
Sub-meter positioning and occupancy with lower latency; not contactless vital signs but cleaner RF fingerprinting in cluttered environments.
Thermal imaging + edge vision (FLIR Lepton, MLX90640)
Privacy-preserving occupancy and activity via heat signature; no through-wall capability, higher BOM (~$80–150/camera), but deterministic and regulation-friendly in some verticals.
Build on RuView with DEV.co software developers
RuView offers privacy-preserving occupancy and vital sign monitoring without cameras. Assess fit for your RF environment, integration needs, and accuracy requirements. Start with a single ESP32 node (~$9–50) and Home Assistant or Matter bridge.
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RuView FAQ
Does RuView work through concrete and metal walls?
What is the typical latency for presence detection and vital signs?
Can I train my own models or do I have to use the pretrained weights?
Is RuView suitable for regulatory compliance (HIPAA, GDPR, etc.)?
Software development & web development with DEV.co
From first prototype to production, DEV.co delivers software development services around tools like RuView. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source observability and beyond.
Evaluate RuView for Your Smart Home or IoT Project
RuView offers privacy-preserving occupancy and vital sign monitoring without cameras. Assess fit for your RF environment, integration needs, and accuracy requirements. Start with a single ESP32 node (~$9–50) and Home Assistant or Matter bridge.