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wehub-resource-sync
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<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/ruvnet/RuView) · [上游 README](https://github.com/ruvnet/RuView/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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# π RuView
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<p align="center">
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</a>
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</p>
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## **See through walls with WiFi** ##
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## **用 WiFi 看穿墙壁** ##
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**Turn ordinary WiFi into a spatial intelligence / sensing system.** Detect people, measure breathing and heart rate, track movement, and monitor rooms — through walls, in the dark, with no cameras or wearables. Just physics.
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**将普通 WiFi 变为空间智能/感知系统。** 检测人员、测量呼吸与心率、追踪移动、监控房间——穿墙、无光环境，无需摄像头或可穿戴设备。纯物理原理。
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Works natively with the four major smart-home ecosystems: **[Home Assistant](docs/integrations/home-assistant.md)** via the HA-DISCO MQTT publisher, **[Apple Home & HomePod](docs/user-guide-apple-homepod.md)** as a discoverable HAP-1.1 bridge, **[Google Home](docs/integrations/home-assistant.md)** + **[Amazon Alexa](docs/integrations/home-assistant.md)** via the same HA bridge or a [Matter](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) endpoint. Siri, Google Assistant, and Alexa can voice presence and vitals by room with zero custom skills.
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原生支持四大智能家居生态：通过 HA-DISCO MQTT 发布器接入 **[Home Assistant](docs/integrations/home-assistant.md)**，以可发现的 HAP-1.1 桥接器接入 **[Apple Home 和 HomePod](docs/user-guide-apple-homepod.md)**，通过同一 HA 桥或 [Matter](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) 端点接入 **[Google Home](docs/integrations/home-assistant.md)** 和 **[Amazon Alexa](docs/integrations/home-assistant.md)**。Siri、Google Assistant 和 Alexa 无需自定义技能即可按房间语音查询人员存在与生命体征。
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[](docs/integrations/home-assistant.md) [](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) [](docs/user-guide-apple-homepod.md) [](docs/integrations/home-assistant.md) [](docs/integrations/home-assistant.md)
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> Drop into any **Home Assistant** install with one `--mqtt` flag. Or pair into **Apple Home / Google Home / Alexa / SmartThings** as a Matter Bridge. Ships 21 entities per node (11 raw signals + 10 inferred semantic states: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, multi-room-transition) plus 3 starter HA Blueprints. See [`docs/integrations/home-assistant.md`](docs/integrations/home-assistant.md) · [ADR-115](docs/adr/ADR-115-home-assistant-integration.md).
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> 通过一个 `--mqtt` 标志即可**嵌入任何 Home Assistant** 安装。或者作为 Matter 桥接器配对到 **Apple Home / Google Home / Alexa / SmartThings**。每个节点提供 21 个实体（11 个原始信号 + 10 个推断语义状态：有人入睡、可能遇险、房间活跃、老人活动异常、会议进行中、卫生间占用、跌倒风险升高、离床、无移动、跨房间移动），外加 3 个入门级 HA Blueprint。参见 [`docs/integrations/home-assistant.md`](docs/integrations/home-assistant.md) · [ADR-115](docs/adr/ADR-115-home-assistant-integration.md)。
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### π RuView is a WiFi sensing platform that turns radio signals into spatial intelligence.
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### π RuView 是一个将无线电信号转化为空间智能的 WiFi 感知平台。
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Every WiFi router already fills your space with radio waves. When people move, breathe, or even sit still, they disturb those waves in measurable ways. RuView captures these disturbances using Channel State Information (CSI) from low-cost ESP32 sensors and turns them into actionable data: who's there, what they're doing, and whether they're okay.
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每个 WiFi 路由器早已用无线电波填满你的空间。当人员移动、呼吸甚至静坐时，都会以可测量的方式扰动这些电波。RuView 通过低成本 ESP32 传感器捕获信道状态信息（CSI）来捕捉这些扰动，并将其转化为可操作的数据：谁在那里、他们在做什么、以及他们是否安好。
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**What it senses:**
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- **Presence and occupancy** — detect people through walls, count them, track entries and exits
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- **Vital signs** — breathing rate and heart rate, contactless, while sleeping or sitting
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- **Activity recognition** — walking, sitting, gestures, falls — from temporal CSI patterns
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- **Environment mapping** — RF fingerprinting identifies rooms, detects moved furniture, spots new objects
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- **Sleep quality** — overnight monitoring with sleep stage classification and apnea screening
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**可感知的内容：**
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- **存在与占用检测**——穿墙检测人员、统计人数、追踪进出
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- **生命体征**——呼吸频率与心率，无接触式测量，睡眠或静坐时均可
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- **活动识别**——行走、坐姿、手势、跌倒——基于时间序列 CSI 模式
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- **环境映射**——射频指纹识别房间、检测家具移动、发现新物体
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- **睡眠质量**——夜间监测，含睡眠阶段分类与呼吸暂停筛查
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Built on [RuVector](https://github.com/ruvnet/ruvector/) and [Cognitum Seed](https://cognitum.one), RuView runs entirely on edge hardware — an ESP32 mesh (as low as $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required.
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基于 [RuVector](https://github.com/ruvnet/ruvector/)) 和 [Cognitum Seed](https://cognitum.one),)，RuView 完全运行在边缘硬件上——ESP32 网状网络（每节点低至 9 美元）配合 Cognitum Seed 提供持久化内存、加密证明和 AI 集成。无需云服务、无需摄像头、无需互联网。
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The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.
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系统使用脉冲神经网络（Spiking Neural Networks）在本地学习每个环境，30 秒内即可自适应，并通过跨 6 个 WiFi 信道的多频网状扫描，将邻居的路由器用作免费的雷达发射源。每次测量均通过 Ed25519 见证链进行加密证明。
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RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized) and runs in microseconds on a Raspberry Pi. (The [v2 encoder](https://huggingface.co/ruvnet/wifi-densepose-pretrained) reports an honest, label-free held-out **temporal-triplet accuracy of 82.3%** — up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted in favor of this.) No cameras, no wearables, no app on the user's phone.
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RuView 将普通 WiFi 变为非接触式传感器。一块 9 美元的 ESP32 板卡读取房间内人员身上的无线电反射信号，一个发布在 Hugging Face [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained)) 上的小型预训练模型——告诉你谁在那里、呼吸状况如何、心率趋势如何。该模型仅占用 8 KB（4 位量化），在 Raspberry Pi 上微秒级运行。（[v2 编码器](https://huggingface.co/ruvnet/wifi-densepose-pretrained)) 报告了诚实、无标签的独立测试**时间三元组准确率为 82.3%**——较原始 66.4% 有所提升；此前的"100% 存在检测"数据基于单类别录制测得，已撤回并由本数据替代。）无需摄像头、无需可穿戴设备、无需用户手机上的应用。
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### Built for low-power edge applications
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### 专为低功耗边缘应用打造
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[Edge modules](#edge-intelligence-adr-041) are small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response.
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[边缘模块](#edge-intelligence-adr-041) 是直接在 ESP32 传感器上运行的小型程序——无需互联网、无需云服务费用、即时响应。
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[](https://www.rust-lang.org/)
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[](https://opensource.org/licenses/MIT)
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[](https://github.com/ruvnet/RuView)
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[](https://hub.docker.com/r/ruvnet/wifi-densepose)
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[](#vital-sign-detection)
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[](#esp32-s3-hardware-pipeline)
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[](https://crates.io/crates/wifi-densepose-ruvector)
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[](#-edge-module-catalog)
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[](https://www.rust-lang.org/))
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[](https://opensource.org/licenses/MIT))
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[](https://github.com/ruvnet/RuView))
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[](https://hub.docker.com/r/ruvnet/wifi-densepose))
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[](#vital-sign-detection))
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[](#esp32-s3-hardware-pipeline))
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[](https://crates.io/crates/wifi-densepose-ruvector))
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[](#-edge-module-catalog))
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> | What | How | Speed / scale |
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> |------|-----|---------------|
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> | 🫁 **Breathing rate** | Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM ([#593](https://github.com/ruvnet/RuView/issues/593)) | 6–30 BPM, real-time |
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> | 💓 **Heart rate** | Bandpass 0.8–2.0 Hz, zero-crossing BPM | 40–120 BPM, real-time |
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> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained); v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |
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> | 🧬 **CSI embeddings** | 128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB | **164,183 emb/s** on M4 Pro |
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> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)). **SOTA on MM-Fi:** [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) hits **82.69% torso-PCK@20** (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi `random_split` protocol — self-corrected and auditable on [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) | 8.4 ms cold-start on a Pi 5 |
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> | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |
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> | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
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> | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |
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> | 🌍 **World model prediction** | OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080; fine-tune on your space with `occworld_retrain.py` ([ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)) | 15 frames × 200×200×16 vox |
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> | 🧱 **Through-wall sensing** | Fresnel-zone geometry + multipath modeling | Up to ~5 m, signal-dependent |
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> | 🧠 **Edge intelligence** | **105-cog catalog** ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) live from `app-registry.json` — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain | $140 total BOM |
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> | 🎯 **Camera-free pre-training** | Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face | 84 s/epoch retrain on M4 Pro |
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> | 📷 **Camera-supervised fine-tune** | MediaPipe + ESP32 CSI paired training, end-to-end Candle pipeline on RTX 5080 ([ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md)) | 2.1 s for 400 epochs (~5 ms/epoch) |
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> | 📡 **Multi-frequency mesh** | Channel hopping across 6 bands, TDM slot scheduling ([ADR-029](docs/adr/ADR-029-multifrequency-mesh.md)) | 3× sensing bandwidth |
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> | 🌐 **3D point cloud fusion** | Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model | 22 ms pipeline · 19K+ points/frame |
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> | 功能 | 方式 | 速度/规模 |
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> |------|------|----------|
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> | 🫁 **呼吸频率** | 对包裹相位进行 0.1–0.5 Hz 带通滤波、循环方差、过零 BPM（[#593](https://github.com/ruvnet/RuView/issues/593)))） | 6–30 BPM，实时 |
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> | 💓 **心率** | 0.8–2.0 Hz 带通滤波、过零 BPM | 40–120 BPM，实时 |
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> | 👤 **存在检测** | Hugging Face 上的训练头部（[`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained);)，v2 编码器 = 82.3% 独立测试时间三元组准确率，诚实重新基准测试）+ 无需模型的相位方差回退方案 | < 1 ms，约 30 秒环境校准 |
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> | 🧬 **CSI 嵌入** | 发布在 Hugging Face 的 128 维对比编码器，4 位量化变体仅 8 KB | M4 Pro 上 **164,183 嵌入/秒** |
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> | 🦴 **17 关键点姿态估计** | `cog-pose-estimation` Cog v0.0.1——GCS 上已签名的 aarch64 + x86_64 二进制文件，通过 Candle 加载 `pose_v1.safetensors`。在 RTX 5080 上 2.1 秒即可从配对数据训练自己的模型（[ADR-101](docs/adr/ADR-101-pose-estimation-cog.md)、[基准测试](docs/benchmarks/pose-estimation-cog.md)）。**MM-Fi 上 SOTA：** [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)) 在匹配的 MM-Fi `random_split` 协议上达到 **82.69% 躯干 PCK@20**（集成 83.59%），超越 MultiFormer（72.25%）和 CSI2Pose（68.41%）——经自我纠正，可在 [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena)) 上审计 | Pi 5 上冷启动 8.4 ms |
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> | 🚶 **运动/活动** | 运动频带功率 + 相位加速度 | 实时 |
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> | 🤸 **跌倒检测** | 相位加速度阈值 + 3 帧防抖 + 5 秒冷却（[#263](https://github.com/ruvnet/RuView/issues/263)))） | < 200 ms |
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> | 🧮 **多人计数** | 自适应 P95 归一化 + 运行时可调去重因子（`/api/v1/config/dedup-factor`，[#491](https://github.com/ruvnet/RuView/pull/491)).)）。提供 6 个专门的学习型计数器 Cog：`occupancy-zones`、`elevator-count`、`queue-length`、`customer-flow`、`clean-room`、`person-matching` | 实时，自校准 |
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> | 🌍 **世界模型预测** | OccWorld TransVQVAE——15 帧未来占用预测，209 ms 推理，RTX 5080 上 3.4 GB VRAM；使用 `occworld_retrain.py` 在你的空间微调（[ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)） | 15 帧 × 200×200×16 体素 |
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> | 🧱 **穿墙感知** | 菲涅尔区几何 + 多径建模 | 最远约 5 m，依信号而定 |
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> | 🧠 **边缘智能** | **105 个 Cog 目录**（[ADR-102](docs/adr/ADR-102-edge-module-registry.md)）从 `app-registry.json` 实时提供——涵盖健康、安防、建筑、零售、工业、科研、AI、集群、信号、网络和开发模块。可选 Cognitum Seed 增加持久化向量存储 + kNN + 见证链 | 物料清单总计 140 美元 |
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> | 🎯 **无摄像头预训练** | 自监督对比编码器，60K 帧上 1220 万步训练，发布在 Hugging Face | M4 Pro 上重训练 84 秒/epoch |
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> | 📷 **摄像头监督微调** | MediaPipe + ESP32 CSI 配对训练，RTX 5080 上端到端 Candle 流水线（[ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md)） | 400 epoch 仅 2.1 秒（约 5 ms/epoch） |
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> | 📡 **多频网状网络** | 跨 6 个频段的信道跳频、TDM 时隙调度（[ADR-029](docs/adr/ADR-029-multifrequency-mesh.md)） | 3 倍感知带宽 |
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> | 🌐 **3D 点云融合** | 摄像头深度（MiDaS）+ WiFi CSI + 毫米波雷达 → 统一空间模型 | 22 ms 流水线 · 每帧 19K+ 点 |
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>
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> Browse the full 105-module catalog (with practical descriptions, sizes, and difficulty) below in [🧩 Edge Module Catalog](#-edge-module-catalog), or visit [seed.cognitum.one/store](https://seed.cognitum.one/store).
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> 浏览完整的 105 模块目录（含实用描述、规模和难度），请参见下方 [🧩 边缘模块目录](#-edge-module-catalog)，或访问 [seed.cognitum.one/store](https://seed.cognitum.one/store).)
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>
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> 🤗 **Pretrained weights**: download from [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — see [Loading the pretrained model](#loading-the-pretrained-model) below for one-command setup.
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> 🤗 **预训练权重**：从 [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained)) 下载——参见下方 [加载预训练模型](#loading-the-pretrained-model) 一行命令即可完成设置。
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```bash
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# Option 1: Docker (simulated data, no hardware needed)
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@@ -122,47 +128,47 @@ pip install "ruview[client]" # or: pip install "wifi-densepose[clie
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[](https://pypi.org/project/ruview/) [](https://pypi.org/project/wifi-densepose/)
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> [!NOTE]
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> **CSI-capable hardware recommended.** Presence, vital signs, through-wall sensing, and all advanced capabilities require Channel State Information (CSI) from an ESP32-S3 ($9) or research NIC. The Docker image runs with simulated data for evaluation. Consumer WiFi laptops provide RSSI-only presence detection.
|
||||
> **建议使用支持 CSI 的硬件。** 存在检测、生命体征、穿墙感知及所有高级能力均需要来自 ESP32-S3（$9）或研究型 NIC 的信道状态信息（Channel State Information，CSI）。Docker 镜像使用模拟数据运行以供评估。消费级 WiFi 笔记本电脑仅提供基于 RSSI 的存在检测。
|
||||
|
||||
> **Hardware options** for live CSI capture:
|
||||
> **实时 CSI 采集的硬件选项**：
|
||||
>
|
||||
> | Option | Hardware | Cost | Full CSI | Capabilities |
|
||||
> | 选项 | 硬件 | 成本 | 完整 CSI | 能力 |
|
||||
> |--------|----------|------|----------|-------------|
|
||||
> | **ESP32 + Cognitum Seed** (recommended) | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | Yes | Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy |
|
||||
> | **ESP32 Mesh** | 3-6× ESP32-S3 + WiFi router | ~$54 | Yes | Same capabilities as above without the persistent-memory features |
|
||||
> | **ESP32-C6 research node** ([ADR-110](docs/adr/ADR-110-esp32-c6-firmware-extension.md), [witness](docs/WITNESS-LOG-110.md), [reviewer guide](docs/ADR-110-REVIEW-GUIDE.md), [firmware v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0-esp32)) | ESP32-C6-DevKit ($6–10) | ~$10 | Yes (Wi-Fi 6 capable) | Same CSI pipeline as S3 with the dual-target firmware. **Firmware-side ADR-110 substrate now closed** (v0.7.0): ESP-NOW cross-board mesh quantified at **99.56 % match / 104 µs smoothed offset stdev / 3.95× EMA suppression** over a 5-min two-board soak (witness §A0.10), 32-byte UDP sync packet with operator-tunable cadence (§A0.12), ADR-018 byte 19 bit 4 wire-fix sourced from the working ESP-NOW path (§A0.13). Wire format ready for HE-LTF PPDU tagging in ADR-018 bytes 18-19 (firmware encoder + Rust + Python decoders verified end-to-end across 23 unit tests). LP-core motion-gate RISC-V program and Wi-Fi 6 soft-AP with TWT Responder both ship as opt-in code paths (default off). **Hardware-gated for measurement**: HE-LTF live subcarrier capture needs an 11ax AP (IDF v5.4 doesn't expose AP-side HE config — §A0.6); ~5 µA LP-core hibernation needs an INA meter to capture; 802.15.4 raw RX is broken in IDF v5.4 (workaround: ESP-NOW transport, shipped + measured). See witness log for the empirical / claimed split. |
|
||||
> | **Research NIC** | Intel 5300 / Atheros AR9580 | ~$50-100 | Yes | Full CSI with 3x3 MIMO |
|
||||
> | **Any WiFi** | Windows, macOS, or Linux laptop | $0 | No | RSSI-only: coarse presence and motion (see [tutorial #36](https://github.com/ruvnet/RuView/issues/36)) |
|
||||
> | **ESP32 + Cognitum Seed**（推荐） | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | 是 | 存在检测、运动、呼吸、心率、跌倒检测、多人计数、17 关键点姿态（已签名 Cog 二进制）、105-cog 目录、持久化向量存储、kNN 搜索、witness 链、MCP 代理 |
|
||||
> | **ESP32 Mesh** | 3-6× ESP32-S3 + WiFi 路由器 | ~$54 | 是 | 与上述相同的能力，但不包含持久化内存功能 |
|
||||
> | **ESP32-C6 研究节点**（[ADR-110](docs/adr/ADR-110-esp32-c6-firmware-extension.md)、[witness](docs/WITNESS-LOG-110.md)、[reviewer guide](docs/ADR-110-REVIEW-GUIDE.md)、[firmware v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0-esp32)) | ESP32-C6-DevKit（$6–10） | ~$10 | 是（支持 Wi-Fi 6） | 与 S3 相同的 CSI 流水线，采用双目标固件。**固件端 ADR-110 基板现已关闭**（v0.7.0）：ESP-NOW 跨板 mesh 在 5 分钟双板浸泡测试中量化结果为 **99.56% 匹配 / 104 µs 平滑偏移标准差 / 3.95× EMA 抑制**（witness §A0.10），32 字节 UDP 同步包，支持操作员可调节奏（§A0.12），ADR-018 字节 19 位 4 线路修复源自可用的 ESP-NOW 路径（§A0.13）。线路格式已就绪，可在 ADR-018 字节 18-19 中进行 HE-LTF PPDU 标记（固件编码器 + Rust + Python 解码器已通过 23 个单元测试完成端到端验证）。LP-core 运动门控 RISC-V 程序以及带 TWT Responder 的 Wi-Fi 6 soft-AP 均作为可选代码路径提供（默认关闭）。**需硬件配合方可测量**：HE-LTF 实时子载波采集需要 11ax AP（IDF v5.4 不暴露 AP 端 HE 配置 — §A0.6）；约 5 µA LP-core 休眠需要 INA 仪表才能采集；IDF v5.4 中 802.15.4 原始 RX 不可用（变通方案：ESP-NOW 传输，已交付并测量）。有关实测与宣称部分的划分，请参阅 witness 日志。 |
|
||||
> | **研究型 NIC** | Intel 5300 / Atheros AR9580 | ~$50-100 | 是 | 完整 CSI，支持 3x3 MIMO |
|
||||
> | **任意 WiFi** | Windows、macOS 或 Linux 笔记本电脑 | $0 | 否 | 仅 RSSI：粗略的存在与运动检测（参见 [教程 #36](https://github.com/ruvnet/RuView/issues/36)) |
|
||||
>
|
||||
> No hardware? Verify the signal processing pipeline with the deterministic reference signal: `python archive/v1/data/proof/verify.py`
|
||||
> 没有硬件？可使用确定性参考信号验证信号处理流水线：`python archive/v1/data/proof/verify.py`
|
||||
>
|
||||
---
|
||||
|
||||
|
||||
<a href="https://ruvnet.github.io/RuView/">
|
||||
<img src="assets/v2-screen.png" alt="WiFi DensePose — Live pose detection with setup guide" width="800">
|
||||
<img src="assets/v2-screen.png" alt="WiFi DensePose — 实时姿态检测与设置指南" width="800">
|
||||
</a>
|
||||
<br>
|
||||
<em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables</em>
|
||||
<em>来自 WiFi CSI 信号的实时姿态骨架 — 无需摄像头，无需可穿戴设备</em>
|
||||
<br><br>
|
||||
<a href="https://ruvnet.github.io/RuView/"><strong>▶ Live Observatory Demo</strong></a>
|
||||
<a href="https://ruvnet.github.io/RuView/"><strong>▶ 实时观测台演示</strong></a>
|
||||
|
|
||||
<a href="https://ruvnet.github.io/RuView/pose-fusion.html"><strong>▶ Dual-Modal Pose Fusion Demo</strong></a>
|
||||
<a href="https://ruvnet.github.io/RuView/pose-fusion.html"><strong>▶ 双模态姿态融合演示</strong></a>
|
||||
|
|
||||
<a href="https://ruvnet.github.io/RuView/pointcloud/"><strong>▶ Live 3D Point Cloud</strong></a>
|
||||
<a href="https://ruvnet.github.io/RuView/pointcloud/"><strong>▶ 实时 3D 点云</strong></a>
|
||||
|
|
||||
<a href="https://ruvnet.github.io/RuView/three.js/"><strong>▶ three.js Demos (5)</strong></a>
|
||||
<a href="https://ruvnet.github.io/RuView/three.js/"><strong>▶ three.js 演示（5 个）</strong></a>
|
||||
|
||||
> The [server](#-quick-start) is optional for visualization and aggregation — the ESP32 [runs independently](#esp32-s3-hardware-pipeline) for presence detection, vital signs, and fall alerts.
|
||||
> [服务器](#-quick-start) 对于可视化与聚合是可选的 — ESP32 可[独立运行](#esp32-s3-hardware-pipeline)，用于存在检测、生命体征与跌倒告警。
|
||||
>
|
||||
> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md).
|
||||
> **实时 ESP32 流水线**：连接 ESP32-S3 节点 → 运行 [sensing server](#sensing-server) → 打开 [姿态融合演示](https://ruvnet.github.io/RuView/pose-fusion.html) 进行实时双模态姿态估计（网络摄像头 + WiFi CSI）。参见 [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md)。
|
||||
>
|
||||
> **three.js scene gallery** at [`/three.js/`](https://ruvnet.github.io/RuView/three.js/) — five progressively richer ADR-097 demos: helpers, cinematic, GLTF skinned, FBX skinned, and a live MediaPipe→Mixamo retargeting feed driven by ESP32 CSI. Demos 04 and 05 require a local Mixamo `X Bot.fbx` (license boundary — not redistributed).
|
||||
> **three.js 场景画廊**位于 [`/three.js/`](https://ruvnet.github.io/RuView/three.js/) — 五个逐步丰富的 ADR-097 演示：helpers、cinematic、GLTF skinned、FBX skinned，以及由 ESP32 CSI 驱动的实时 MediaPipe→Mixamo 重定向流。演示 04 和 05 需要本地 Mixamo `X Bot.fbx`（许可边界 — 未再分发）。
|
||||
|
||||
|
||||
## 🤗 Pretrained model on Hugging Face
|
||||
## 🤗 Hugging Face 上的预训练模型
|
||||
|
||||
Pretrained CSI weights live at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — 12.2M training steps on 60K frames / 610K contrastive triplets, **82.3% held-out temporal-triplet accuracy** (up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted), 4-bit quantized variant fits in 8 KB. The release includes a contrastive **CSI encoder** producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a **presence-detection head**. Per-node LoRA adapters are included for environment-specific fine-tuning.
|
||||
预训练 CSI 权重位于 [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — 在 60K 帧 / 610K 对比三元组上训练 12.2M 步，**保留集时间三元组准确率 82.3%**（原始为 66.4%；较早的「100% 存在检测」数字是在单类别录制上测得，已撤回），4-bit 量化版本可放入 8 KB。该发布包含对比 **CSI 编码器**，可生成 128 维嵌入（M4 Pro 上 164,183 emb/s），以及 **存在检测头**。包含按节点的 LoRA 适配器，用于针对特定环境的微调。
|
||||
|
||||
```bash
|
||||
# Download the model bundle
|
||||
@@ -170,216 +176,217 @@ pip install huggingface_hub
|
||||
huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/wifi-densepose-pretrained
|
||||
```
|
||||
|
||||
**What works today vs. what's pending wiring:**
|
||||
**当前可用 vs. 待接线：**
|
||||
|
||||
| Consumer | Format used | Status |
|
||||
| 使用者 | 使用格式 | 状态 |
|
||||
|----------|-------------|--------|
|
||||
| Python training / evaluation / embedding extraction | `model.safetensors` | ✅ Works — load with `safetensors.torch.load_file` |
|
||||
| Inspect / re-export the bundle | `model.rvf.jsonl` (line-by-line JSON) | ✅ Works — plain JSONL |
|
||||
| Sensing-server `--model <PATH>` flag | binary RVF (`RVFS` magic) | ⚠️ Loader does not yet accept the JSONL container |
|
||||
| Python 训练 / 评估 / 嵌入提取 | `model.safetensors` | ✅ 可用 — 使用 `safetensors.torch.load_file` 加载 |
|
||||
| 检查 / 重新导出 bundle | `model.rvf.jsonl`（逐行 JSON） | ✅ 可用 — 纯 JSONL |
|
||||
| Sensing-server `--model <PATH>` 标志 | binary RVF（`RVFS` magic） | ⚠️ 加载器尚不接受 JSONL 容器 |
|
||||
|
||||
**Known gap:** the HF model ships in JSONL RVF format, but `v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs` only parses the binary RVF segment format. Pointing `--model` at `model.rvf.jsonl` currently errors with `invalid magic at offset 0: expected 0x52564653, got 0x7974227B` and the live pipeline degrades to null output rather than falling back to heuristic mode — so for the live sensing-server, run **without** `--model` until a JSONL adapter lands (or the model is re-published as binary RVF). Use the weights from Python / training in the meantime.
|
||||
**已知差距：** HF 模型以 JSONL RVF 格式发布，但 `v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs` 只解析二进制 RVF 分段格式。将 `--model` 指向 `model.rvf.jsonl` 当前会报错 `invalid magic at offset 0: expected 0x52564653, got 0x7974227B`，线上管道降级为空输出而非回退到启发式模式——因此在 JSONL 适配器就绪（或模型以二进制 RVF 重新发布）之前，**不要**使用 `--model` 运行在线 sensing-server。期间请使用 Python/训练端的权重。
|
||||
|
||||
**Quantization choices** (all in the HF repo): `model-q2.bin` (4 KB) · `model-q4.bin` ⭐ recommended (8 KB) · `model-q8.bin` (16 KB) · `model.safetensors` full (48 KB)
|
||||
**量化选项**（均在 HF 仓库中）：`model-q2.bin`（4 KB）· `model-q4.bin` ⭐ 推荐（8 KB）· `model-q8.bin`（16 KB）· `model.safetensors` 完整版（48 KB）
|
||||
|
||||
The separate **17-keypoint pose-estimation model** is now published at [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) — **82.69% torso-PCK@20** on MM-Fi (single model) / **83.59%** (3-model ensemble + TTA), beating the prior published SOTA MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched `random_split` protocol. See **Results & proof** below.
|
||||
独立的 **17 关键点姿态估计模型** 现已发布，地址为 [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)——在 MM-Fi 上达到 **82.69% torso-PCK@20**（单模型）/ **83.59%**（3 模型集成 + TTA），在匹配的 `random_split` 协议上超越了此前已发布的 SOTA MultiFormer（72.25%）和 CSI2Pose（68.41%）。详见下方 **结果与验证**。
|
||||
|
||||
### Results & proof
|
||||
### 结果与验证
|
||||
|
||||
| What | Where | Numbers |
|
||||
|------|-------|---------|
|
||||
| **MM-Fi pose model (SOTA)** | [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) | 82.69% torso-PCK@20 (single) · 83.59% (ensemble+TTA) · 75K-param micro variant 74.30% |
|
||||
| **AetherArena benchmark Space** | [`ruvnet/aether-arena`](https://huggingface.co/spaces/ruvnet/aether-arena) | self-correcting, auditable MM-Fi leaderboard |
|
||||
| **Full MM-Fi study (honest picture)** | [`docs/benchmarks/mmfi-wifi-sensing-study.md`](docs/benchmarks/mmfi-wifi-sensing-study.md) | pose + action; zero-shot cross-subject ~64%, +~30 s in-room calibration → 72.2% |
|
||||
| **Efficiency frontier** | [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](docs/benchmarks/wifi-pose-efficiency-frontier.md) | SOTA-beating WiFi pose in a 20 KB int4 edge model |
|
||||
| **Pretrained encoder** | [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) | 82.3% held-out temporal-triplet, 8 KB int4 |
|
||||
| **Reproducible proof (Trust Kill Switch)** | [`archive/v1/data/proof/verify.py`](archive/v1/data/proof/verify.py) + [`expected_features.sha256`](archive/v1/data/proof/expected_features.sha256) | one-command deterministic pipeline replay (SHA-256 of output vs published hash) |
|
||||
| **Benchmark-proof ADR** | [ADR-168](docs/adr/ADR-168-benchmark-proof.md) | how the numbers are produced and verified |
|
||||
| **Witness attestation** | [`docs/WITNESS-LOG-028.md`](docs/WITNESS-LOG-028.md) | 33-row capability attestation matrix with per-claim evidence |
|
||||
| 内容 | 位置 | 数据 |
|
||||
|------|------|------|
|
||||
| **MM-Fi 姿态模型（SOTA）** | [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) | 82.69% torso-PCK@20（单模型）· 83.59%（集成 + TTA）· 75K 参数微型变体 74.30% |
|
||||
| **AetherArena 基准 Space** | [`ruvnet/aether-arena`](https://huggingface.co/spaces/ruvnet/aether-arena) | 自修正、可审计的 MM-Fi 排行榜 |
|
||||
| **完整 MM-Fi 研究（真实情况）** | [`docs/benchmarks/mmfi-wifi-sensing-study.md`](docs/benchmarks/mmfi-wifi-sensing-study.md) | 姿态 + 动作；零样本跨受试者约 64%，+~30 秒室内校准 → 72.2% |
|
||||
| **效率前沿** | [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](docs/benchmarks/wifi-pose-efficiency-frontier.md) | 20 KB int4 边缘模型中超越 SOTA 的 WiFi 姿态 |
|
||||
| **预训练编码器** | [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) | 82.3% 留出时间三元组，8 KB int4 |
|
||||
| **可复现验证（信任终止开关）** | [`archive/v1/data/proof/verify.py`](archive/v1/data/proof/verify.py) + [`expected_features.sha256`](archive/v1/data/proof/expected_features.sha256) | 单命令确定性管道回放（输出与已发布哈希的 SHA-256 比对） |
|
||||
| **防基准作弊 ADR** | [ADR-168](docs/adr/ADR-168-benchmark-proof.md) | 数据的产生与验证方式 |
|
||||
| **见证证明** | [`docs/WITNESS-LOG-028.md`](docs/WITNESS-LOG-028.md) | 33 行能力证明矩阵，每项声明附带证据 |
|
||||
|
||||
```bash
|
||||
# Reproduce the deterministic pipeline proof yourself (must print VERDICT: PASS):
|
||||
python archive/v1/data/proof/verify.py
|
||||
```
|
||||
|
||||
Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) phases P7–P9 for the camera-supervised fine-tune path.
|
||||
跟踪于 [#509](https://github.com/ruvnet/RuView/issues/509);，参见 [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) 阶段 P7–P9 了解摄像头监督微调路径。
|
||||
|
||||
|
||||
## 🧩 Edge Module Catalog
|
||||
## 🧩 边缘模块目录
|
||||
|
||||
<details>
|
||||
<summary><b>🧩 105 edge modules ready to install on a Cognitum appliance</b> — live catalog from <code>app-registry.json</code> v2.1.0 (updated 2026-05-13). Browse + install at <a href="https://seed.cognitum.one/store">seed.cognitum.one/store</a> or your local appliance <code>http://<appliance>:9000/cogs</code>.</summary>
|
||||
<summary><b>🧩 可在 Cognitum 设备上安装的 105 个边缘模块</b>——来自 <code>app-registry.json</code> v2.1.0（更新于 2026-05-13）的实时目录。浏览并安装：<a href="https://seed.cognitum.one/store">seed.cognitum.one/store</a> 或本地设备 <code>http://<appliance>:9000/cogs</code>。</summary>
|
||||
|
||||
Each module is a small signed binary (~400 KB) that runs alongside the WiFi-DensePose sensing stack on a Cognitum-V0 appliance. The catalog updates over the air — your appliance fetches it via <code>GET /api/v1/edge/registry</code> ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) and verifies each binary against an Ed25519 signature ([ADR-100](docs/adr/ADR-100-cog-packaging-specification.md)) before install.
|
||||
每个模块都是一个约 400 KB 的小型签名二进制文件，在 Cognitum-V0 设备上与 WiFi-DensePose 感知栈并行运行。目录通过空中（OTA）更新——设备通过 <code>GET /api/v1/edge/registry</code>（[ADR-102](docs/adr/ADR-102-edge-module-registry.md)）获取目录，并在安装前用 Ed25519 签名（[ADR-100](docs/adr/ADR-100-cog-packaging-specification.md)）验证每个二进制文件。
|
||||
|
||||
### 🫀 Health — <sub>14 modules</sub>
|
||||
### 🫀 健康——<sub>14 个模块</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|------|-----:|:----:|
|
||||
| `air-quality-index` | 使用 CO2 和颗粒物传感器追踪室内空气质量 | 8 KB | 简单 |
|
||||
| `baby-cry` | 持续中频能量检测器，用于育儿室/婴儿监护。仅音频，无摄像头。 | 451 KB | 简单 |
|
||||
| `breathing-sync` | 检测两人是否同步呼吸 | 10 KB | 困难 |
|
||||
| `cardiac-arrhythmia` | 发现不规则心跳和异常心律 | 8 KB | 困难 |
|
||||
| `cough-detect` | 声音瞬态 + 频谱咳嗽检测器，带 30 秒聚类聚合。呼吸道疾病的早期预警信号。 | 451 KB | 简单 |
|
||||
| `dream-stage` | 追踪睡眠阶段——浅睡、深睡和做梦 | 14 KB | 困难 |
|
||||
| `fall-detect` | 基于环境特征流（ESP32 运动/麦克风）的两级冲击 + 静止跌倒检测器。可选的 ruview 模式用于 CSI 姿态增强。 | 402 KB | 简单 |
|
||||
| `gait-analysis` | 检测行走问题并评估跌倒风险 | 12 KB | 困难 |
|
||||
| `health-monitor` | 无接触心率、呼吸、睡眠和跌倒警报 | 30 KB | 中 |
|
||||
| `respiratory-distress` | 呼吸变得费力或危险时发出警报 | 10 KB | 困难 |
|
||||
| `seizure-detect` | 识别癫痫发作并立即发出警报 | 10 KB | 困难 |
|
||||
| `sleep-apnea` | 检测睡眠中呼吸停止 | 4 KB | 简单 |
|
||||
| `snore-monitor` | 周期性低频能量追踪器，用于睡眠质量/呼吸暂停风险趋势分析。睡眠呼吸暂停模块的配套工具。 | 451 KB | 简单 |
|
||||
| `vital-trend` | 追踪数周内的呼吸和心率趋势 | 6 KB | 中 |
|
||||
|
||||
### 🔒 安全——<sub>14 个模块</sub>
|
||||
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|------|-----:|:----:|
|
||||
| `audit-logger` | 记录每项操作以符合合规要求——防篡改日志 | 8 KB | 简单 |
|
||||
| `behavioral-profiler` | 学习正常行为并标记任何异常 | 12 KB | 困难 |
|
||||
| `fleet-auth` | 跨所有 seed 管理设备证书和访问权限 | 12 KB | 中 |
|
||||
| `glass-break` | 两级巨响 + 碎裂声检测器。区分玻璃破碎与普通脉冲噪声。 | 451 KB | 简单 |
|
||||
| `gunshot-detect` | 饱和峰值 + 指数衰减声音检测器，带可选的 ruview CSI 运动下降增强。 | 451 KB | 简单 |
|
||||
| `intrusion` | 未经授权者进入房间时发出警报 | 6 KB | 中 |
|
||||
| `intrusion-detect-ml` | 使用机器学习检测网络攻击 | 14 KB | 困难 |
|
||||
| `loitering` | 有人在某处逗留过久时发出警报 | 3 KB | 简单 |
|
||||
| `network-firewall` | 按模块阻止未授权的网络访问 | 6 KB | 简单 |
|
||||
| `panic-motion` | 检测突然的恐慌或异常移动 | 6 KB | 中 |
|
||||
| `perimeter-breach` | 守护多个区域并显示进入方向 | 10 KB | 中 |
|
||||
| `prompt-shield` | 阻止对 seed 的信号重放和注入攻击 | 10 KB | 中 |
|
||||
| `tailgating` | 检测有人尾随持证者潜入 | 6 KB | 中 |
|
||||
| `weapon-detect` | 检测人身上隐藏的金属物体 | 8 KB | 困难 |
|
||||
|
||||
### 🏢 建筑——<sub>11 个模块</sub>
|
||||
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|------|-----:|:----:|
|
||||
| `beehive-monitor` | 声音蜂巢状态分类器。通过嗡嗡频带能量 + 混沌度 + 鸣叫自相关检测健康/混乱/失王/分蜂/盗蜂状态。 | 451 KB | 简单 |
|
||||
| `elevator-count` | 统计电梯内人数 | 8 KB | 中 |
|
||||
| `energy-audit` | 学习你的日程安排并减少能源浪费 | 6 KB | 中 |
|
||||
| `frost-warning` | 通过温度趋势 + 露点差阈值提前 6 小时预测霜冻。适用于大田/果园农业。 | 451 KB | 简单 |
|
||||
| `hvac-presence` | 在你到达时自动开启供暖和制冷 | 3 KB | 简单 |
|
||||
| `lighting-zones` | 根据人员在房间间的移动自动开关灯 | 4 KB | 简单 |
|
||||
| `meeting-room` | 显示会议室是否空闲或被占用 | 5 KB | 简单 |
|
||||
| `occupancy-zones` | 穿墙统计每个房间的人数 | 8 KB | 中 |
|
||||
| `predictive-maintenance` | 旋转设备的振动谐波分析仪。追踪 F1/2×F1/高阶/边带能量以评估退化严重程度。 | 451 KB | 简单 |
|
||||
| `smoke-fire` | 多信号烟雾和火灾检测器。融合声音噼啪声、热漂移代理以及可选的 ruview CSI 烟羽特征。不能替代法规要求的烟雾报警器，未通过 UL 认证。 | 451 KB | 简单 |
|
||||
| `water-leak` | 持续低幅嘶嘶声 + 周期性滴水声音检测器，带多分钟持续门控。两级：疑似→确认。 | 451 KB | 简单 |
|
||||
|
||||
USD 预算：$0/$3；剩余 $3 | 每月 $0/$3——总计：$3 | **Token 用量：** 2,350 / 8,000（29%）——无需自托管
|
||||
|
||||
### 🛍️ 零售 — <sub>7 modules</sub>
|
||||
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `air-quality-index` | Track indoor air quality with CO2 and particle sensors | 8 KB | Easy |
|
||||
| `baby-cry` | Sustained mid-band energy detector for nursery / infant monitoring. Audio-only, no camera. | 451 KB | Easy |
|
||||
| `breathing-sync` | Detects when two people breathe in sync | 10 KB | Hard |
|
||||
| `cardiac-arrhythmia` | Spots irregular heartbeats and abnormal heart rhythms | 8 KB | Hard |
|
||||
| `cough-detect` | Acoustic transient + spectral cough detector with 30s cluster aggregation. Early-warning signal for respiratory illness. | 451 KB | Easy |
|
||||
| `dream-stage` | Tracks your sleep stages — light, deep, and dreaming | 14 KB | Hard |
|
||||
| `fall-detect` | Two-stage impact + stillness fall detector over ambient feature stream (ESP32 motion / mic). Optional ruview-mode for CSI-based pose reinforcement. | 402 KB | Easy |
|
||||
| `gait-analysis` | Detects walking problems and scores fall risk | 12 KB | Hard |
|
||||
| `health-monitor` | Contactless heart rate, breathing, sleep, and fall alerts | 30 KB | Med |
|
||||
| `respiratory-distress` | Alerts when breathing becomes labored or dangerously fast | 10 KB | Hard |
|
||||
| `seizure-detect` | Recognizes seizures and sends immediate alerts | 10 KB | Hard |
|
||||
| `sleep-apnea` | Detects when someone stops breathing during sleep | 4 KB | Easy |
|
||||
| `snore-monitor` | Periodic low-band energy tracker for sleep-quality / apnea-risk trending. Companion to sleep-apnea cog. | 451 KB | Easy |
|
||||
| `vital-trend` | Tracks breathing and heart rate trends over weeks | 6 KB | Med |
|
||||
| `customer-flow` | 统计各出入口的进出客流量 | 8 KB | Med |
|
||||
| `dwell-heatmap` | 显示顾客停留时间最长的区域 | 6 KB | Med |
|
||||
| `package-detect` | 用于门廊/装卸区包裹进出的持续 CSI 偏移检测器。需要 ESP32 CSI ruview 输入。 | 451 KB | Easy |
|
||||
| `parking-occupancy` | 通过 ESP32 CSI 子载波幅度偏移实现分区停车占用检测。跟踪利用率与每小时周转率。需要 ruview。 | 451 KB | Easy |
|
||||
| `queue-length` | 估算排队长度与等待时间 | 6 KB | Med |
|
||||
| `shelf-engagement` | 检测顾客与商品的互动时刻 | 6 KB | Med |
|
||||
| `table-turnover` | 跟踪餐厅桌位的空闲或占用状态 | 4 KB | Easy |
|
||||
|
||||
### 🔒 Security — <sub>14 modules</sub>
|
||||
### 🏭 工业 — <sub>7 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `audit-logger` | Record every action for compliance — tamper-proof log | 8 KB | Easy |
|
||||
| `behavioral-profiler` | Learns normal behavior and flags anything unusual | 12 KB | Hard |
|
||||
| `fleet-auth` | Manage device certificates and access across all seeds | 12 KB | Med |
|
||||
| `glass-break` | Two-phase bang + shatter acoustic detector. Distinguishes glass break from ordinary impulse noise. | 451 KB | Easy |
|
||||
| `gunshot-detect` | Saturating peak + exponential decay acoustic detector with optional ruview CSI motion-drop reinforcement. | 451 KB | Easy |
|
||||
| `intrusion` | Alerts when an unauthorized person enters a room | 6 KB | Med |
|
||||
| `intrusion-detect-ml` | Detect network attacks using machine learning | 14 KB | Hard |
|
||||
| `loitering` | Alerts when someone lingers too long in one spot | 3 KB | Easy |
|
||||
| `network-firewall` | Block unauthorized network access per cog | 6 KB | Easy |
|
||||
| `panic-motion` | Detects sudden panicked or erratic movement | 6 KB | Med |
|
||||
| `perimeter-breach` | Guards multiple zones and shows entry direction | 10 KB | Med |
|
||||
| `prompt-shield` | Blocks signal replay and injection attacks on the seed | 10 KB | Med |
|
||||
| `tailgating` | Catches when someone sneaks in behind a badge holder | 6 KB | Med |
|
||||
| `weapon-detect` | Detects concealed metal objects on a person | 8 KB | Hard |
|
||||
| `clean-room` | 在受控环境中强制执行人数上限 | 4 KB | Easy |
|
||||
| `confined-space` | 监测狭窄空间内作业人员的安全状况 | 5 KB | Med |
|
||||
| `forklift-proximity` | 叉车过于靠近工人时发出警告 | 10 KB | Hard |
|
||||
| `livestock-monitor` | 监测动物的痛苦、逃逸或疾病迹象 | 6 KB | Med |
|
||||
| `ppe-compliance` | Cog 组合层：当 ruview-densepose 在限制区域检测到人员存在，但缺少配套的 PPE 摄像头 cog 确认向量时发出警报。 | 387 KB | Easy |
|
||||
| `slip-fall-zone` | 跌倒前风险检测器。当运动方差下降、溅水音频以及可选的谨慎步态 CSI 均表明滑倒风险升高时触发。 | 451 KB | Easy |
|
||||
| `structural-vibration` | 检测建筑物或机器的危险振动 | 8 KB | Hard |
|
||||
|
||||
### 🏢 Building — <sub>11 modules</sub>
|
||||
### 🔬 科研 — <sub>12 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `beehive-monitor` | Acoustic hive state classifier. Detects healthy / chaotic / queenless / swarming / robbing via hum-band energy + chaos + piping autocorr. | 451 KB | Easy |
|
||||
| `elevator-count` | Counts how many people are in an elevator | 8 KB | Med |
|
||||
| `energy-audit` | Learns your schedule and cuts wasted energy | 6 KB | Med |
|
||||
| `frost-warning` | Predicts frost 6 hours ahead via temperature trend + dewpoint-depression gate. Field/orchard agriculture. | 451 KB | Easy |
|
||||
| `hvac-presence` | Turns heating and cooling on when you arrive | 3 KB | Easy |
|
||||
| `lighting-zones` | Turns lights on and off as people move between rooms | 4 KB | Easy |
|
||||
| `meeting-room` | Shows if a meeting room is free or occupied | 5 KB | Easy |
|
||||
| `occupancy-zones` | Counts people in each room through walls | 8 KB | Med |
|
||||
| `predictive-maintenance` | Vibration harmonic analyzer for rotating equipment. Tracks F1 / 2×F1 / high-order / sideband energy to score degradation severity. | 451 KB | Easy |
|
||||
| `smoke-fire` | Multi-signal smoke and fire detector. Fuses acoustic crackle, thermal drift proxy, and optional ruview CSI plume signature. Not a UL-listed replacement for code-required smoke alarms. | 451 KB | Easy |
|
||||
| `water-leak` | Persistent low-amplitude hiss + periodic drip acoustic detector with multi-minute persistence gate. Two-stage likely → confirmed. | 451 KB | Easy |
|
||||
| `emotion-detect` | 从肢体语言与呼吸中解读压力与平静状态 | 10 KB | Hard |
|
||||
| `energy-harvester` | 为离网 seed 部署优化太阳能与电池配置 | 6 KB | Med |
|
||||
| `gesture-language` | 实时识别手语手势 | 12 KB | Hard |
|
||||
| `ghost-hunter` | 发现无法解释的环境异常——仅供娱乐 | 10 KB | Hard |
|
||||
| `happiness-score` | 根据运动与情绪信号估算身心健康状况 | 8 KB | Med |
|
||||
| `hyperbolic-space` | 将数据映射到曲率空间以呈现树状结构 | 12 KB | Hard |
|
||||
| `music-conductor` | 解读指挥者的手势以识别速度与力度变化 | 12 KB | Hard |
|
||||
| `plant-growth` | 跟踪植物生长速率与昼夜周期 | 8 KB | Med |
|
||||
| `rain-detect` | 检测降雨的开始、停止及强度 | 6 KB | Med |
|
||||
| `ruview-densepose` | 基于 WiFi 的全身姿态跟踪——无需摄像头 | 50 KB | Hard |
|
||||
| `sound-classifier` | 识别玻璃破碎、警报或婴儿啼哭等声音 | 16 KB | Hard |
|
||||
| `time-crystal` | 实验性探索重复时间模式的对称性 | 12 KB | Hard |
|
||||
|
||||
### 🛍️ Retail — <sub>7 modules</sub>
|
||||
### 🤖 AI — <sub>15 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `customer-flow` | Counts foot traffic in and out of each entrance | 8 KB | Med |
|
||||
| `dwell-heatmap` | Shows where customers spend the most time | 6 KB | Med |
|
||||
| `package-detect` | Sustained CSI-shift detector for porch / loading bay package arrivals and departures. Requires ESP32 CSI ruview input. | 451 KB | Easy |
|
||||
| `parking-occupancy` | Per-zone parking occupancy via ESP32 CSI subcarrier-amplitude shift. Tracks utilization and churn-per-hour. Requires ruview. | 451 KB | Easy |
|
||||
| `queue-length` | Estimates line length and wait time | 6 KB | Med |
|
||||
| `shelf-engagement` | Detects when customers interact with products | 6 KB | Med |
|
||||
| `table-turnover` | Tracks which restaurant tables are free or occupied | 4 KB | Easy |
|
||||
| `anomaly-attractor` | 学习正常模式并捕捉任何异常 | 10 KB | Hard |
|
||||
| `cognitive-pipeline` | 面向 Pi Zero 2W 设备端认知事件的 FastGRNN 异常门控 + SmolLM2 稀疏 LLM 推理 | 320 KB | Hard |
|
||||
| `dtw-gesture-learn` | 通过示例演示教授自定义手势 | 14 KB | Med |
|
||||
| `ewc-lifelong` | 学习新知识而不遗忘旧知识 | 8 KB | Hard |
|
||||
| `federated-learning` | 跨 seed 训练 AI，无需共享原始数据 | 18 KB | Hard |
|
||||
| `goap-autonomy` | 自主规划并执行目标 | 14 KB | Hard |
|
||||
| `meta-adapt` | 自动调优以获得最佳性能 | 10 KB | Hard |
|
||||
| `micro-hnsw` | 快速的设备端指纹识别与分类 | 12 KB | Med |
|
||||
| `neural-trader` | 从实时数据中发现市场模式与趋势 | 20 KB | Hard |
|
||||
| `pagerank-influence` | 找出群体中最具影响力的人 | 12 KB | Med |
|
||||
| `pattern-sequence` | 检测日常作息与重复习惯 | 10 KB | Med |
|
||||
| `rag-local` | 使用 AI 搜索文档——在 seed 上运行 | 14 KB | Med |
|
||||
| `spiking-tracker` | 受脑启发、可在微型硬件上运行的跟踪器 | 16 KB | Hard |
|
||||
| `temporal-logic` | 对实时事件流强制执行安全规则 | 12 KB | Hard |
|
||||
| `time-series-forecast` | 利用历史模式预测传感器趋势 | 12 KB | Med |
|
||||
|
||||
### 🏭 Industrial — <sub>7 modules</sub>
|
||||
### 🐝 蜂群（Swarm） — <sub>11 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `clean-room` | Enforces max headcount in controlled environments | 4 KB | Easy |
|
||||
| `confined-space` | Monitors workers in tight spaces for safety | 5 KB | Med |
|
||||
| `forklift-proximity` | Warns if a forklift gets too close to workers | 10 KB | Hard |
|
||||
| `livestock-monitor` | Monitors animals for distress, escape, or illness | 6 KB | Med |
|
||||
| `ppe-compliance` | Cog-composition layer: alerts when ruview-densepose detects presence in a restricted zone without an accompanying PPE-camera-cog confirmation vector. | 387 KB | Easy |
|
||||
| `slip-fall-zone` | Pre-fall risk detector. Fires when motion-variance drop, splash audio, and optional cautious-gait CSI all signal elevated slip risk. | 451 KB | Easy |
|
||||
| `structural-vibration` | Detects dangerous vibrations in buildings or machines | 8 KB | Hard |
|
||||
| `swarm-backup-restore` | 自动将数据备份到其他 seed——一键恢复 | 8 KB | Easy |
|
||||
| `swarm-cluster-monitor` | 所有 seed 健康与状态的实时仪表板 | 6 KB | Easy |
|
||||
| `swarm-consensus` | seed 在共同进行关键变更前先投票 | 16 KB | Hard |
|
||||
| `swarm-delta-sync` | seed 间自动同步数据——仅发送变更 | 8 KB | Med |
|
||||
| `swarm-deploy` | 一次性在所有 seed 上安装或移除 cog | 10 KB | Med |
|
||||
| `swarm-distributed-store` | 将数据分布到多个 seed 并统一搜索 | 14 KB | Hard |
|
||||
| `swarm-edge-orchestrator` | 从单一位置管理所有 ESP32 传感器节点 | 14 KB | Hard |
|
||||
| `swarm-load-balancer` | 将查询分散到多个 seed，避免单点过载 | 10 KB | Med |
|
||||
| `swarm-mesh-manager` | 发现、连接并监测网络上的所有 seed | 12 KB | Easy |
|
||||
| `swarm-mqtt-bridge` | 通过 MQTT 消息在 seed 间共享事件 | 6 KB | Easy |
|
||||
| `swarm-witness-federation` | 在 seed 间共享防篡改审计追踪 | 12 KB | Hard |
|
||||
|
||||
### 🔬 Research — <sub>12 modules</sub>
|
||||
### 📡 信号 — <sub>6 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `emotion-detect` | Reads stress and calm from body language and breathing | 10 KB | Hard |
|
||||
| `energy-harvester` | Optimize solar and battery for off-grid seed deployment | 6 KB | Med |
|
||||
| `gesture-language` | Recognizes sign language gestures in real time | 12 KB | Hard |
|
||||
| `ghost-hunter` | Finds unexplained environmental anomalies — for fun | 10 KB | Hard |
|
||||
| `happiness-score` | Estimates well-being from movement and mood signals | 8 KB | Med |
|
||||
| `hyperbolic-space` | Maps data into curved space for tree-like structures | 12 KB | Hard |
|
||||
| `music-conductor` | Reads a conductor's gestures for tempo and dynamics | 12 KB | Hard |
|
||||
| `plant-growth` | Tracks plant growth rate and day/night cycles | 8 KB | Med |
|
||||
| `rain-detect` | Detects when rain starts, stops, and how heavy it is | 6 KB | Med |
|
||||
| `ruview-densepose` | Full body pose tracking from WiFi — no cameras needed | 50 KB | Hard |
|
||||
| `sound-classifier` | Identify sounds like glass break, alarm, or baby cry | 16 KB | Hard |
|
||||
| `time-crystal` | Experiments with repeating time-pattern symmetry | 12 KB | Hard |
|
||||
| `coherence-gate` | 过滤噪声信号，保留干净信号 | 8 KB | Med |
|
||||
| `flash-attention` | 将感知聚焦于特定区域以提高精度 | 12 KB | Med |
|
||||
| `optimal-transport` | 通过形状感知信号比较测量运动 | 12 KB | Hard |
|
||||
| `person-matching` | 区分同一房间内的多个人 | 18 KB | Hard |
|
||||
| `sparse-recovery` | 从部分读数中恢复缺失的信号数据 | 16 KB | Hard |
|
||||
| `temporal-compress` | 压缩旧数据以节省内存且不损失语义 | 14 KB | Med |
|
||||
|
||||
### 🤖 Ai — <sub>15 modules</sub>
|
||||
### 🌐 网络 — <sub>1 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `anomaly-attractor` | Learns what's normal and catches anything weird | 10 KB | Hard |
|
||||
| `cognitive-pipeline` | FastGRNN anomaly gate + SmolLM2 sparse-LLM inference for on-device Pi Zero 2W cognitive events | 320 KB | Hard |
|
||||
| `dtw-gesture-learn` | Teach custom hand gestures by showing examples | 14 KB | Med |
|
||||
| `ewc-lifelong` | Learns new things without forgetting old lessons | 8 KB | Hard |
|
||||
| `federated-learning` | Train AI across seeds without sharing raw data | 18 KB | Hard |
|
||||
| `goap-autonomy` | Plans and executes goals on its own | 14 KB | Hard |
|
||||
| `meta-adapt` | Automatically tunes itself for best performance | 10 KB | Hard |
|
||||
| `micro-hnsw` | Fast on-device fingerprinting and classification | 12 KB | Med |
|
||||
| `neural-trader` | Spot market patterns and trends from live data | 20 KB | Hard |
|
||||
| `pagerank-influence` | Finds the most influential person in a group | 12 KB | Med |
|
||||
| `pattern-sequence` | Detects daily routines and repeated habits | 10 KB | Med |
|
||||
| `rag-local` | Search your documents using AI — runs on the seed | 14 KB | Med |
|
||||
| `spiking-tracker` | Brain-inspired tracker that runs on tiny hardware | 16 KB | Hard |
|
||||
| `temporal-logic` | Enforces safety rules on live event streams | 12 KB | Hard |
|
||||
| `time-series-forecast` | Predict sensor trends using historical patterns | 12 KB | Med |
|
||||
| `tailscale` | 通过私有 WireGuard 网状网络（Tailscale）从任意位置访问 seed。用户空间模式——无需 root。 | 700 KB | Med |
|
||||
|
||||
### 🐝 Swarm — <sub>11 modules</sub>
|
||||
### 🛠️ 开发者 — <sub>7 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
| ID | 功能 | 大小 | 难度 |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `swarm-backup-restore` | Auto-backup data to other seeds — one-click restore | 8 KB | Easy |
|
||||
| `swarm-cluster-monitor` | Live dashboard of every seed's health and status | 6 KB | Easy |
|
||||
| `swarm-consensus` | Seeds vote before making critical changes together | 16 KB | Hard |
|
||||
| `swarm-delta-sync` | Auto-sync data between seeds — only sends changes | 8 KB | Med |
|
||||
| `swarm-deploy` | Install or remove cogs on all seeds at once | 10 KB | Med |
|
||||
| `swarm-distributed-store` | Spread data across seeds and search them all at once | 14 KB | Hard |
|
||||
| `swarm-edge-orchestrator` | Manage all ESP32 sensor nodes from one place | 14 KB | Hard |
|
||||
| `swarm-load-balancer` | Spread queries across seeds so no single one overloads | 10 KB | Med |
|
||||
| `swarm-mesh-manager` | Find, connect, and monitor all seeds on your network | 12 KB | Easy |
|
||||
| `swarm-mqtt-bridge` | Share events between seeds over MQTT messaging | 6 KB | Easy |
|
||||
| `swarm-witness-federation` | Share tamper-proof audit trails across seeds | 12 KB | Hard |
|
||||
| `adversarial` | 检测被篡改或伪造的传感器信号 | 4 KB | Easy |
|
||||
| `coherence` | 监测多通道信号质量 | 4 KB | Easy |
|
||||
| `gesture` | cog 的核心手势识别构建块 | 6 KB | Med |
|
||||
| `interference-search` | 并行搜索多种可能性以快速得出答案 | 14 KB | Hard |
|
||||
| `psycho-symbolic` | 以多种风格对知识图谱进行推理 | 16 KB | Hard |
|
||||
| `quantum-coherence` | 用于高级信号状态的量子启发模型 | 16 KB | Hard |
|
||||
| `self-healing-mesh` | 即使节点掉线也能保持传感器网状网络运行 | 14 KB | Hard |
|
||||
|
||||
### 📡 Signal — <sub>6 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `coherence-gate` | Filters out noisy signals and keeps clean ones | 8 KB | Med |
|
||||
| `flash-attention` | Focuses sensing on specific areas for better accuracy | 12 KB | Med |
|
||||
| `optimal-transport` | Measures motion using shape-aware signal comparison | 12 KB | Hard |
|
||||
| `person-matching` | Tells apart multiple people in the same room | 18 KB | Hard |
|
||||
| `sparse-recovery` | Recovers missing signal data from partial readings | 16 KB | Hard |
|
||||
| `temporal-compress` | Shrinks old data to save memory without losing meaning | 14 KB | Med |
|
||||
|
||||
### 🌐 Network — <sub>1 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `tailscale` | Reach the seed from anywhere via a private WireGuard mesh (Tailscale). Userspace mode — no root. | 700 KB | Med |
|
||||
|
||||
### 🛠️ Developer — <sub>7 modules</sub>
|
||||
|
||||
| ID | What it does | Size | Difficulty |
|
||||
|----|--------------|-----:|:----------:|
|
||||
| `adversarial` | Detects tampered or spoofed sensor signals | 4 KB | Easy |
|
||||
| `coherence` | Monitors signal quality across multiple channels | 4 KB | Easy |
|
||||
| `gesture` | Core gesture recognition building block for cogs | 6 KB | Med |
|
||||
| `interference-search` | Searches many possibilities at once for fast answers | 14 KB | Hard |
|
||||
| `psycho-symbolic` | Reasons over knowledge graphs with multiple styles | 16 KB | Hard |
|
||||
| `quantum-coherence` | Quantum-inspired model for advanced signal states | 16 KB | Hard |
|
||||
| `self-healing-mesh` | Keeps sensor mesh running even when nodes drop out | 14 KB | Hard |
|
||||
|
||||
> ℹ️ Build your own cog: see [ADR-100](docs/adr/ADR-100-cog-packaging-specification.md) for the packaging spec. The first cog this repo ships into the catalog lives in [v2/crates/cog-pose-estimation/](v2/crates/cog-pose-estimation/) (17-keypoint WiFi pose, [ADR-101](docs/adr/ADR-101-pose-estimation-cog.md)).
|
||||
> ℹ️ 构建你自己的 cog：打包规范见 [ADR-100](docs/adr/ADR-100-cog-packaging-specification.md)。本仓库收录到目录中的首个 cog 位于 [v2/crates/cog-pose-estimation/](v2/crates/cog-pose-estimation/)（17 关键点 WiFi 姿态，[ADR-101](docs/adr/ADR-101-pose-estimation-cog.md)）。
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## 🔬 How It Works
|
||||
## 🔬 工作原理
|
||||
|
||||
WiFi routers flood every room with radio waves. When a person moves — or even breathes — those waves scatter differently. WiFi DensePose reads that scattering pattern and reconstructs what happened:
|
||||
WiFi 路由器向每个房间灌满无线电波。当人移动——甚至呼吸——这些波的散射方式就会不同。WiFi DensePose 读取该散射模式并重建发生了什么：
|
||||
|
||||
```
|
||||
WiFi Router → radio waves pass through room → hit human body → scatter
|
||||
@@ -403,90 +410,90 @@ Neural Network: processed signals → 17 body keypoints + vital signs + room mod
|
||||
Output: real-time pose, breathing, heart rate, room fingerprint, drift alerts
|
||||
```
|
||||
|
||||
No training cameras required — the [Self-Learning system (ADR-024)](docs/adr/ADR-024-contrastive-csi-embedding-model.md) bootstraps from raw WiFi data alone. [MERIDIAN (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) ensures the model works in any room, not just the one it trained in.
|
||||
无需训练摄像头 —— [自学习系统 (ADR-024)](docs/adr/ADR-024-contrastive-csi-embedding-model.md) 仅从原始 WiFi 数据即可完成引导。[MERIDIAN (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) 确保模型在任意房间都能工作，而不仅限于训练所在的房间。
|
||||
|
||||
---
|
||||
|
||||
## 🏢 Use Cases & Applications
|
||||
## 🏢 用例与应用场景
|
||||
|
||||
WiFi sensing works anywhere WiFi exists. No new hardware in most cases — just software on existing access points or a $8 ESP32 add-on. Because there are no cameras, deployments avoid privacy regulations (GDPR video, HIPAA imaging) by design.
|
||||
WiFi 感知可在任何有 WiFi 的地方工作。大多数情况下无需新增硬件 —— 只需在现有接入点上运行软件，或加装一个 $8 的 ESP32 模块即可。由于不使用摄像头，部署方案从设计上即规避了隐私法规（GDPR 视频、HIPAA 成像）的约束。
|
||||
|
||||
**Scaling:** Each AP distinguishes ~3-5 people (56 subcarriers). Multi-AP multiplies linearly — a 4-AP retail mesh covers ~15-20 occupants. No hard software limit; the practical ceiling is signal physics.
|
||||
**扩展能力：** 每个 AP 可区分约 3-5 人（56 个子载波）。多 AP 可线性倍增 —— 4 个 AP 的零售网格可覆盖约 15-20 人。无硬性软件限制；实际上限取决于信号物理特性。
|
||||
|
||||
| | Why WiFi sensing wins | Traditional alternative |
|
||||
| | WiFi 感知为何胜出 | 传统替代方案 |
|
||||
|---|----------------------|----------------------|
|
||||
| 🔒 | **No video, no GDPR/HIPAA imaging rules** | Cameras require consent, signage, data retention policies |
|
||||
| 🧱 | **Works through walls, shelving, debris** | Cameras need line-of-sight per room |
|
||||
| 🌙 | **Works in total darkness** | Cameras need IR or visible light |
|
||||
| 💰 | **$0-$8 per zone** (existing WiFi or ESP32) | Camera systems: $200-$2,000 per zone |
|
||||
| 🔌 | **WiFi already deployed everywhere** | PIR/radar sensors require new wiring per room |
|
||||
| 🔒 | **无视频，不受 GDPR/HIPAA 成像法规约束** | 摄像头需知情同意、标识牌、数据留存政策 |
|
||||
| 🧱 | **可穿透墙壁、货架、障碍物** | 摄像头需每间房间有视线可达 |
|
||||
| 🌙 | **完全黑暗环境也能工作** | 摄像头需红外或可见光照明 |
|
||||
| 💰 | **每个区域 $0-$8**（现有 WiFi 或 ESP32） | 摄像头系统：每个区域 $200-$2,000 |
|
||||
| 🔌 | **WiFi 已无处不在** | PIR/雷达传感器每间房间均需重新布线 |
|
||||
|
||||
<details>
|
||||
<summary><strong>🏥 Everyday</strong> — Healthcare, retail, office, hospitality (commodity WiFi)</summary>
|
||||
<summary><strong>🏥 日常场景</strong> —— 医疗健康、零售、办公、酒店（普通商用 WiFi）</summary>
|
||||
|
||||
| Use Case | What It Does | Hardware | Key Metric | Edge Module |
|
||||
| 用例 | 功能描述 | 硬件 | 关键指标 | 边缘模块 |
|
||||
|----------|-------------|----------|------------|-------------|
|
||||
| **Elderly care / assisted living** | Fall detection, nighttime activity monitoring, breathing rate during sleep — no wearable compliance needed | 1 ESP32-S3 per room ($8) | Fall alert <2s | [Sleep Apnea](docs/edge-modules/medical.md), [Gait Analysis](docs/edge-modules/medical.md) |
|
||||
| **Hospital patient monitoring** | Continuous breathing + heart rate for non-critical beds without wired sensors; nurse alert on anomaly | 1-2 APs per ward | Breathing: 6-30 BPM | [Respiratory Distress](docs/edge-modules/medical.md), [Cardiac Arrhythmia](docs/edge-modules/medical.md) |
|
||||
| **Emergency room triage** | Automated occupancy count + wait-time estimation; detect patient distress (abnormal breathing) in waiting areas | Existing hospital WiFi | Occupancy accuracy >95% | [Queue Length](docs/edge-modules/retail.md), [Panic Motion](docs/edge-modules/security.md) |
|
||||
| **Retail occupancy & flow** | Real-time foot traffic, dwell time by zone, queue length — no cameras, no opt-in, GDPR-friendly | Existing store WiFi + 1 ESP32 | Dwell resolution ~1m | [Customer Flow](docs/edge-modules/retail.md), [Dwell Heatmap](docs/edge-modules/retail.md) |
|
||||
| **Office space utilization** | Which desks/rooms are actually occupied, meeting room no-shows, HVAC optimization based on real presence | Existing enterprise WiFi | Presence latency <1s | [Meeting Room](docs/edge-modules/building.md), [HVAC Presence](docs/edge-modules/building.md) |
|
||||
| **Hotel & hospitality** | Room occupancy without door sensors, minibar/bathroom usage patterns, energy savings on empty rooms | Existing hotel WiFi | 15-30% HVAC savings | [Energy Audit](docs/edge-modules/building.md), [Lighting Zones](docs/edge-modules/building.md) |
|
||||
| **Restaurants & food service** | Table turnover tracking, kitchen staff presence, restroom occupancy displays — no cameras in dining areas | Existing WiFi | Queue wait ±30s | [Table Turnover](docs/edge-modules/retail.md), [Queue Length](docs/edge-modules/retail.md) |
|
||||
| **Parking garages** | Pedestrian presence in stairwells and elevators where cameras have blind spots; security alert if someone lingers | Existing WiFi | Through-concrete walls | [Loitering](docs/edge-modules/security.md), [Elevator Count](docs/edge-modules/building.md) |
|
||||
| **养老照护/辅助生活** | 跌倒检测、夜间活动监测、睡眠呼吸频率 —— 无需穿戴设备配合 | 每个房间 1 个 ESP32-S3（$8） | 跌倒告警 <2s | [睡眠呼吸暂停](docs/edge-modules/medical.md)、[步态分析](docs/edge-modules/medical.md) |
|
||||
| **医院患者监测** | 对非重症床位持续监测呼吸与心率，无需有线传感器；异常时护士告警 | 每个病区 1-2 个 AP | 呼吸：6-30 BPM | [呼吸窘迫](docs/edge-modules/medical.md)、[心律失常](docs/edge-modules/medical.md) |
|
||||
| **急诊分诊** | 自动统计就诊人数与候诊时间估算；在候诊区检测患者异常状态（呼吸异常） | 现有医院 WiFi | 人数统计准确率 >95% | [排队长度](docs/edge-modules/retail.md)、[恐慌动作](docs/edge-modules/security.md) |
|
||||
| **零售客流与动线** | 实时客流量、各区域停留时长、排队长度 —— 无摄像头、无需用户选择加入、符合 GDPR | 现有店内 WiFi + 1 个 ESP32 | 停留分辨率 ~1m | [顾客动线](docs/edge-modules/retail.md)、[停留热力图](docs/edge-modules/retail.md) |
|
||||
| **办公空间利用** | 哪些工位/会议室实际被占用、会议室爽约、基于实际人员存在优化 HVAC | 现有企业 WiFi | 存在检测延迟 <1s | [会议室](docs/edge-modules/building.md)、[HVAC 存在感知](docs/edge-modules/building.md) |
|
||||
| **酒店与服务业** | 房间占用无需门磁传感器、迷你吧/卫生间使用模式、空房节能 | 现有酒店 WiFi | HVAC 节能 15-30% | [能耗审计](docs/edge-modules/building.md)、[照明分区](docs/edge-modules/building.md) |
|
||||
| **餐厅与餐饮服务** | 翻台追踪、后厨人员存在检测、卫生间占用显示 —— 用餐区无摄像头 | 现有 WiFi | 排队等待 ±30s | [翻台追踪](docs/edge-modules/retail.md)、[排队长度](docs/edge-modules/retail.md) |
|
||||
| **停车场** | 摄像头存在盲区的楼梯间和电梯间行人存在检测；有人逗留时发出安全告警 | 现有 WiFi | 可穿透混凝土墙壁 | [逗留检测](docs/edge-modules/security.md)、[电梯人数](docs/edge-modules/building.md) |
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>🏟️ Specialized</strong> — Events, fitness, education, civic (CSI-capable hardware)</summary>
|
||||
<summary><strong>🏟️ 专业场景</strong> —— 活动赛事、健身、教育、市政（支持 CSI 的硬件）</summary>
|
||||
|
||||
| Use Case | What It Does | Hardware | Key Metric | Edge Module |
|
||||
| 用例 | 功能描述 | 硬件 | 关键指标 | 边缘模块 |
|
||||
|----------|-------------|----------|------------|-------------|
|
||||
| **Smart home automation** | Room-level presence triggers (lights, HVAC, music) that work through walls — no dead zones, no motion-sensor timeouts | 2-3 ESP32-S3 nodes ($24) | Through-wall range ~5m | [HVAC Presence](docs/edge-modules/building.md), [Lighting Zones](docs/edge-modules/building.md) |
|
||||
| **Fitness & sports** | Rep counting, posture correction, breathing cadence during exercise — no wearable, no camera in locker rooms | 3+ ESP32-S3 mesh | Pose: 17 keypoints | [Breathing Sync](docs/edge-modules/exotic.md), [Gait Analysis](docs/edge-modules/medical.md) |
|
||||
| **Childcare & schools** | Naptime breathing monitoring, playground headcount, restricted-area alerts — privacy-safe for minors | 2-4 ESP32-S3 per zone | Breathing: ±1 BPM | [Sleep Apnea](docs/edge-modules/medical.md), [Perimeter Breach](docs/edge-modules/security.md) |
|
||||
| **Event venues & concerts** | Crowd density mapping, crush-risk detection via breathing compression, emergency evacuation flow tracking | Multi-AP mesh (4-8 APs) | Density per m² | [Customer Flow](docs/edge-modules/retail.md), [Panic Motion](docs/edge-modules/security.md) |
|
||||
| **Stadiums & arenas** | Section-level occupancy for dynamic pricing, concession staffing, emergency egress flow modeling | Enterprise AP grid | 15-20 per AP mesh | [Dwell Heatmap](docs/edge-modules/retail.md), [Queue Length](docs/edge-modules/retail.md) |
|
||||
| **Houses of worship** | Attendance counting without facial recognition — privacy-sensitive congregations, multi-room campus tracking | Existing WiFi | Zone-level accuracy | [Elevator Count](docs/edge-modules/building.md), [Energy Audit](docs/edge-modules/building.md) |
|
||||
| **Warehouse & logistics** | Worker safety zones, forklift proximity alerts, occupancy in hazardous areas — works through shelving and pallets | Industrial AP mesh | Alert latency <500ms | [Forklift Proximity](docs/edge-modules/industrial.md), [Confined Space](docs/edge-modules/industrial.md) |
|
||||
| **Civic infrastructure** | Public restroom occupancy (no cameras possible), subway platform crowding, shelter headcount during emergencies | Municipal WiFi + ESP32 | Real-time headcount | [Customer Flow](docs/edge-modules/retail.md), [Loitering](docs/edge-modules/security.md) |
|
||||
| **Museums & galleries** | Visitor flow heatmaps, exhibit dwell time, crowd bottleneck alerts — no cameras near artwork (flash/theft risk) | Existing WiFi | Zone dwell ±5s | [Dwell Heatmap](docs/edge-modules/retail.md), [Shelf Engagement](docs/edge-modules/retail.md) |
|
||||
| **智能家居自动化** | 房间级存在感知触发（灯光、HVAC、音乐），可穿墙工作 —— 无死角，无运动传感器超时问题 | 2-3 个 ESP32-S3 节点（$24） | 穿墙范围 ~5m | [HVAC 存在感知](docs/edge-modules/building.md)、[照明分区](docs/edge-modules/building.md) |
|
||||
| **健身与运动** | 动作计数、姿态矫正、运动中呼吸节奏 —— 无需穿戴设备、更衣室无摄像头 | 3+ 个 ESP32-S3 网格 | 姿态：17 个关键点 | [呼吸同步](docs/edge-modules/exotic.md)、[步态分析](docs/edge-modules/medical.md) |
|
||||
| **托育与学校** | 午睡呼吸监测、操场人数统计、限制区域告警 —— 对未成年人保护隐私安全 | 每个区域 2-4 个 ESP32-S3 | 呼吸：±1 BPM | [睡眠呼吸暂停](docs/edge-modules/medical.md)、[周界入侵](docs/edge-modules/security.md) |
|
||||
| **活动场馆与演唱会** | 人群密度分布、通过呼吸压缩检测踩踏风险、紧急疏散人流追踪 | 多 AP 网格（4-8 个 AP） | 每平方米密度 | [顾客动线](docs/edge-modules/retail.md)、[恐慌动作](docs/edge-modules/security.md) |
|
||||
| **体育场与竞技场** | 分区占用用于动态定价、特许经营人员配置、紧急疏散人流建模 | 企业级 AP 网格 | 每个 AP 网格 15-20 人 | [停留热力图](docs/edge-modules/retail.md)、[排队长度](docs/edge-modules/retail.md) |
|
||||
| **宗教场所** | 无需人脸识别的参与人数统计 —— 注重隐私的会众、多房间园区追踪 | 现有 WiFi | 区域级精度 | [电梯人数](docs/edge-modules/building.md)、[能耗审计](docs/edge-modules/building.md) |
|
||||
| **仓储与物流** | 工人安全区域、叉车接近告警、危险区域人员占用 —— 可穿透货架和托盘 | 工业 AP 网格 | 告警延迟 <500ms | [叉车接近](docs/edge-modules/industrial.md)、[受限空间](docs/edge-modules/industrial.md) |
|
||||
| **市政基础设施** | 公共卫生间占用（无法使用摄像头）、地铁站台拥挤、应急避难人数统计 | 市政 WiFi + ESP32 | 实时人数统计 | [顾客动线](docs/edge-modules/retail.md)、[逗留检测](docs/edge-modules/security.md) |
|
||||
| **博物馆与美术馆** | 参观者动线热力图、展品前停留时长、人群瓶颈告警 —— 艺术品旁无摄像头（闪光/盗窃风险） | 现有 WiFi | 区域停留 ±5s | [停留热力图](docs/edge-modules/retail.md)、[展架互动](docs/edge-modules/retail.md) |
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>🤖 Robotics & Industrial</strong> — Autonomous systems, manufacturing, android spatial awareness</summary>
|
||||
<summary><strong>🤖 机器人与工业</strong> —— 自主系统、制造业、仿人机器人空间感知</summary>
|
||||
|
||||
WiFi sensing gives robots and autonomous systems a spatial awareness layer that works where LIDAR and cameras fail — through dust, smoke, fog, and around corners. The CSI signal field acts as a "sixth sense" for detecting humans in the environment without requiring line-of-sight.
|
||||
WiFi 感知为机器人和自主系统提供了一层空间感知能力，可在 LIDAR 和摄像头失效的环境中工作 —— 穿透灰尘、烟雾、雾气及绕过角落。CSI 信号场如同一种"第六感"，用于检测环境中的人类存在，无需视线可达。
|
||||
|
||||
| Use Case | What It Does | Hardware | Key Metric | Edge Module |
|
||||
| 用例 | 功能描述 | 硬件 | 关键指标 | 边缘模块 |
|
||||
|----------|-------------|----------|------------|-------------|
|
||||
| **Cobot safety zones** | Detect human presence near collaborative robots — auto-slow or stop before contact, even behind obstructions | 2-3 ESP32-S3 per cell | Presence latency <100ms | [Forklift Proximity](docs/edge-modules/industrial.md), [Perimeter Breach](docs/edge-modules/security.md) |
|
||||
| **Warehouse AMR navigation** | Autonomous mobile robots sense humans around blind corners, through shelving racks — no LIDAR occlusion | ESP32 mesh along aisles | Through-shelf detection | [Forklift Proximity](docs/edge-modules/industrial.md), [Loitering](docs/edge-modules/security.md) |
|
||||
| **Android / humanoid spatial awareness** | Ambient human pose sensing for social robots — detect gestures, approach direction, and personal space without cameras always on | Onboard ESP32-S3 module | 17-keypoint pose | [Gesture Language](docs/edge-modules/exotic.md), [Emotion Detection](docs/edge-modules/exotic.md) |
|
||||
| **Manufacturing line monitoring** | Worker presence at each station, ergonomic posture alerts, headcount for shift compliance — works through equipment | Industrial AP per zone | Pose + breathing | [Confined Space](docs/edge-modules/industrial.md), [Gait Analysis](docs/edge-modules/medical.md) |
|
||||
| **Construction site safety** | Exclusion zone enforcement around heavy machinery, fall detection from scaffolding, personnel headcount | Ruggedized ESP32 mesh | Alert <2s, through-dust | [Panic Motion](docs/edge-modules/security.md), [Structural Vibration](docs/edge-modules/industrial.md) |
|
||||
| **Agricultural robotics** | Detect farm workers near autonomous harvesters in dusty/foggy field conditions where cameras are unreliable | Weatherproof ESP32 nodes | Range ~10m open field | [Forklift Proximity](docs/edge-modules/industrial.md), [Rain Detection](docs/edge-modules/exotic.md) |
|
||||
| **Drone landing zones** | Verify landing area is clear of humans — WiFi sensing works in rain, dust, and low light where downward cameras fail | Ground ESP32 nodes | Presence: >95% accuracy | [Perimeter Breach](docs/edge-modules/security.md), [Tailgating](docs/edge-modules/security.md) |
|
||||
| **Clean room monitoring** | Personnel tracking without cameras (particle contamination risk from camera fans) — gown compliance via pose | Existing cleanroom WiFi | No particulate emission | [Clean Room](docs/edge-modules/industrial.md), [Livestock Monitor](docs/edge-modules/industrial.md) |
|
||||
| **协作机器人安全区域** | 检测协作机器人附近的人类存在 —— 在接触前自动减速或停止，即使有障碍物遮挡 | 每个单元 2-3 个 ESP32-S3 | 存在检测延迟 <100ms | [叉车接近](docs/edge-modules/industrial.md)、[周界入侵](docs/edge-modules/security.md) |
|
||||
| **仓储 AMR 导航** | 自主移动机器人感知盲区拐角、货架后方的人类 —— 无 LIDAR 遮挡问题 | 沿通道部署 ESP32 网格 | 穿透货架检测 | [叉车接近](docs/edge-modules/industrial.md)、[逗留检测](docs/edge-modules/security.md) |
|
||||
| **仿人机器人/人形机器人空间感知** | 为社交机器人提供环境人体姿态感知 —— 检测手势、接近方向和个人空间，无需摄像头始终开启 | 板载 ESP32-S3 模块 | 17 个关键点姿态 | [手势语言](docs/edge-modules/exotic.md)、[情绪检测](docs/edge-modules/exotic.md) |
|
||||
| **生产线监测** | 各工位工人存在检测、人体工学姿态告警、班次合规人数统计 —— 可穿透设备工作 | 每个区域工业 AP | 姿态 + 呼吸 | [受限空间](docs/edge-modules/industrial.md)、[步态分析](docs/edge-modules/medical.md) |
|
||||
| **建筑工地安全** | 重型机械周围禁区执行、脚手架跌倒检测、人员人数统计 | 加固型 ESP32 网格 | 告警 <2s，可穿透灰尘 | [恐慌动作](docs/edge-modules/security.md)、[结构振动](docs/edge-modules/industrial.md) |
|
||||
| **农业机器人** | 在多尘/多雾的田间条件下检测自主收割机附近农场工人，摄像头在此类条件下不可靠 | 防风雨 ESP32 节点 | 开放场地范围 ~10m | [叉车接近](docs/edge-modules/industrial.md)、[降雨检测](docs/edge-modules/exotic.md) |
|
||||
| **无人机降落区域** | 确认降落区域无人员 —— WiFi 感知可在雨水、灰尘和弱光条件下工作，而朝下摄像头会失效 | 地面 ESP32 节点 | 人员存在：准确率 >95% | [周界入侵](docs/edge-modules/security.md)、[尾随检测](docs/edge-modules/security.md) |
|
||||
| **洁净室监测** | 人员追踪无需摄像头（摄像头风扇会产生颗粒污染风险）—— 通过姿态检测防护服合规 | 现有洁净室 WiFi | 无颗粒物排放 | [洁净室](docs/edge-modules/industrial.md)、[牲畜监测](docs/edge-modules/industrial.md) |
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>🔥 Extreme</strong> — Through-wall, disaster, defense, underground</summary>
|
||||
<summary><strong>🔥 极端场景</strong> — 穿墙、灾难、安防、地下</summary>
|
||||
|
||||
These scenarios exploit WiFi's ability to penetrate solid materials — concrete, rubble, earth — where no optical or infrared sensor can reach. The WiFi-Mat disaster module (ADR-001) is specifically designed for this tier.
|
||||
这些场景利用了 WiFi 穿透固体材料（混凝土、碎石、泥土）的能力——这是任何光学或红外传感器都无法做到的。WiFi-Mat 灾难模块（ADR-001）专为此级别设计。
|
||||
|
||||
| Use Case | What It Does | Hardware | Key Metric | Edge Module |
|
||||
|----------|-------------|----------|------------|-------------|
|
||||
| **Search & rescue (WiFi-Mat)** | Detect survivors through rubble/debris via breathing signature, START triage color classification, 3D localization | Portable ESP32 mesh + laptop | Through 30cm concrete | [Respiratory Distress](docs/edge-modules/medical.md), [Seizure Detection](docs/edge-modules/medical.md) |
|
||||
| **Firefighting** | Locate occupants through smoke and walls before entry; breathing detection confirms life signs remotely | Portable mesh on truck | Works in zero visibility | [Sleep Apnea](docs/edge-modules/medical.md), [Panic Motion](docs/edge-modules/security.md) |
|
||||
| **Prison & secure facilities** | Cell occupancy verification, distress detection (abnormal vitals), perimeter sensing — no camera blind spots | Dedicated AP infrastructure | 24/7 vital signs | [Cardiac Arrhythmia](docs/edge-modules/medical.md), [Loitering](docs/edge-modules/security.md) |
|
||||
| **Military / tactical** | Through-wall personnel detection, room clearing confirmation, hostage vital signs at standoff distance | Directional WiFi + custom FW | Range: 5m through wall | [Perimeter Breach](docs/edge-modules/security.md), [Weapon Detection](docs/edge-modules/security.md) |
|
||||
| **Border & perimeter security** | Detect human presence in tunnels, behind fences, in vehicles — passive sensing, no active illumination to reveal position | Concealed ESP32 mesh | Passive / covert | [Perimeter Breach](docs/edge-modules/security.md), [Tailgating](docs/edge-modules/security.md) |
|
||||
| **Mining & underground** | Worker presence in tunnels where GPS/cameras fail, breathing detection after collapse, headcount at safety points | Ruggedized ESP32 mesh | Through rock/earth | [Confined Space](docs/edge-modules/industrial.md), [Respiratory Distress](docs/edge-modules/medical.md) |
|
||||
| **Maritime & naval** | Below-deck personnel tracking through steel bulkheads (limited range, requires tuning), man-overboard detection | Ship WiFi + ESP32 | Through 1-2 bulkheads | [Structural Vibration](docs/edge-modules/industrial.md), [Panic Motion](docs/edge-modules/security.md) |
|
||||
| **Wildlife research** | Non-invasive animal activity monitoring in enclosures or dens — no light pollution, no visual disturbance | Weatherproof ESP32 nodes | Zero light emission | [Livestock Monitor](docs/edge-modules/industrial.md), [Dream Stage](docs/edge-modules/exotic.md) |
|
||||
| 应用场景 | 功能 | 硬件 | 关键指标 | 边缘模块 |
|
||||
|----------|------|------|----------|----------|
|
||||
| **搜索与救援（WiFi-Mat）** | 通过呼吸信号检测废墟下的幸存者，START 检伤分类颜色识别，3D 定位 | 便携式 ESP32 网格 + 笔记本电脑 | 穿透 30cm 混凝土 | [呼吸窘迫](docs/edge-modules/medical.md)、[癫痫检测](docs/edge-modules/medical.md) |
|
||||
| **消防** | 进入前通过烟雾和墙壁定位被困人员；呼吸检测远程确认生命迹象 | 车载便携式网格 | 零可见度环境下工作 | [睡眠呼吸暂停](docs/edge-modules/medical.md)、[恐慌运动](docs/edge-modules/security.md) |
|
||||
| **监狱与安全设施** | 牢房人员核实、异常状态检测（生命体征异常）、周界感知——无摄像头盲区 | 专用 AP 基础设施 | 24/7 生命体征监测 | [心律失常](docs/edge-modules/medical.md)、[徘徊检测](docs/edge-modules/security.md) |
|
||||
| **军事/战术** | 穿墙人员检测、房间清场确认、远距离人质生命体征监测 | 定向 WiFi + 自定义固件 | 距离：穿墙 5m | [周界入侵](docs/edge-modules/security.md)、[武器检测](docs/edge-modules/security.md) |
|
||||
| **边境与周界安防** | 检测隧道内、围栏后、车辆中的人员存在——无源感知，无需主动照明暴露位置 | 隐蔽式 ESP32 网格 | 无源/隐蔽 | [周界入侵](docs/edge-modules/security.md)、[尾随检测](docs/edge-modules/security.md) |
|
||||
| **矿业与地下工程** | GPS/摄像头失效的隧道中工作人员定位、坍塌后呼吸检测、安全点人员清点 | 加固型 ESP32 网格 | 穿透岩石/泥土 | [密闭空间](docs/edge-modules/industrial.md)、[呼吸窘迫](docs/edge-modules/medical.md) |
|
||||
| **海事与海军** | 甲板下人员追踪（穿透钢制舱壁，有限距离，需调校）、人员落水检测 | 船舶 WiFi + ESP32 | 穿透 1-2 层舱壁 | [结构振动](docs/edge-modules/industrial.md)、[恐慌运动](docs/edge-modules/security.md) |
|
||||
| **野生动物研究** | 圈养场或巢穴内非侵入式动物活动监测——无光污染、无视觉干扰 | 防风雨 ESP32 节点 | 零光辐射 | [牲畜监测](docs/edge-modules/industrial.md)、[梦阶段](docs/edge-modules/exotic.md) |
|
||||
|
||||
</details>
|
||||
|
||||
@@ -494,39 +501,42 @@ These scenarios exploit WiFi's ability to penetrate solid materials — concrete
|
||||
---
|
||||
|
||||
<details>
|
||||
<summary><strong>🧠 Self-Learning WiFi AI (ADR-024)</strong> — Adaptive recognition, self-optimization, and intelligent anomaly detection</summary>
|
||||
<summary><strong>🧠 自学习 WiFi AI（ADR-024）</strong> — 自适应识别、自我优化与智能异常检测</summary>
|
||||
|
||||
Every WiFi signal that passes through a room creates a unique fingerprint of that space. WiFi-DensePose already reads these fingerprints to track people, but until now it threw away the internal "understanding" after each reading. The Self-Learning WiFi AI captures and preserves that understanding as compact, reusable vectors — and continuously optimizes itself for each new environment.
|
||||
每一个穿过房间的 WiFi 信号都会为该空间留下一枚独特的指纹。WiFi-DensePose 已经能够读取这些指纹来追踪人员，但在此之前，每次读取完成后都会丢弃内部的"理解"。自学习 WiFi AI 则捕获并保存这种理解，将其转化为紧凑、可复用的向量——并针对每个新环境持续自我优化。
|
||||
|
||||
**What it does in plain terms:**
|
||||
- Turns any WiFi signal into a 128-number "fingerprint" that uniquely describes what's happening in a room
|
||||
- Learns entirely on its own from raw WiFi data — no cameras, no labeling, no human supervision needed
|
||||
- Recognizes rooms, detects intruders, and classifies activities using only WiFi (named person-identity is an experimental, data-gated research capability — see below, not a shipped feature)
|
||||
- Runs on an $8 ESP32 chip (the entire model fits in 55 KB of memory)
|
||||
- Produces both body pose tracking AND environment fingerprints in a single computation
|
||||
**通俗解释：**
|
||||
- 将任何 WiFi 信号转化为一个 128 位的数字"指纹"，唯一描述房间内正在发生的情况
|
||||
- 完全通过原始 WiFi 数据自主学习——无需摄像头、无需标注、无需人工监督
|
||||
- 仅使用 WiFi 即可识别房间、检测入侵者、分类活动（人名身份识别属于实验性、受数据制约的研究能力——见下文，并非已发布功能）
|
||||
- 在 8 美元的 ESP32 芯片上运行（整个模型仅占用 55 KB 内存）
|
||||
- 一次计算同时输出人体姿态追踪和环境指纹
|
||||
|
||||
**Key Capabilities**
|
||||
**核心能力**
|
||||
|
||||
| What | How it works | Why it matters |
|
||||
|------|-------------|----------------|
|
||||
| **Self-supervised learning** | The model watches WiFi signals and teaches itself what "similar" and "different" look like, without any human-labeled data | Deploy anywhere — just plug in a WiFi sensor and wait 10 minutes |
|
||||
| **Room identification** | Each room produces a distinct WiFi fingerprint pattern | Know which room someone is in without GPS or beacons |
|
||||
| **Anomaly detection** | An unexpected person or event creates a fingerprint that doesn't match anything seen before | Automatic intrusion and fall detection as a free byproduct |
|
||||
| **Person re-identification** *(experimental, research)* | A real per-channel similarity matcher (Soul Signature §3.6, `wifi-densepose-bfld`); **measured** result: on WiFi-only cardiac+respiratory channels alone two people are *not* separable (gap ~0.0005) | Honest research capability — **named identity is not claimed** and is data-gated on enrollment with the decisive AETHER/body-resonance channel. See [#1021](https://github.com/ruvnet/RuView/issues/1021) |
|
||||
| **Environment adaptation** | MicroLoRA adapters (1,792 parameters per room) fine-tune the model for each new space | Adapts to a new room with minimal data — 93% less than retraining from scratch |
|
||||
| **Memory preservation** | EWC++ regularization remembers what was learned during pretraining | Switching to a new task doesn't erase prior knowledge |
|
||||
| **Hard-negative mining** | Training focuses on the most confusing examples to learn faster | Better accuracy with the same amount of training data |
|
||||
| 能力 | 工作原理 | 为何重要 |
|
||||
|------|----------|----------|
|
||||
| **自监督学习** | 模型观察 WiFi 信号，自学"相似"和"不同"的形态，无需任何人工标注数据 | 随处部署——只需插入 WiFi 传感器，等待 10 分钟 |
|
||||
| **房间识别** | 每个房间产生独特的 WiFi 指纹模式 | 无需 GPS 或信标即可知道某人在哪个房间 |
|
||||
| **异常检测** | 意外的人员或事件会产生与之前所见都不匹配的指纹 | 自动入侵和跌倒检测，作为免费副产品 |
|
||||
| **人员重识别** *（实验性，研究阶段）* | 真实的逐通道相似度匹配器（灵魂签名 §3.6，``wifi-densepose-bfld``）；**实测**结果：仅凭 WiFi 心脏+呼吸通道无法区分两个人（差距约 0.0005） | 诚实的研究能力——**不声称识别具体身份**，受数据制约，需 enrollment 和决定性的 AETHER/体共振通道。详见 [#1021](https://github.com/ruvnet/RuView/issues/1021)) |
|
||||
| **环境自适应** | MicroLoRA 适配器（每个房间 1,792 个参数）为每个新空间微调模型 | 用极少数据适应新房间——比从头训练少 93% |
|
||||
| **记忆保持** | EWC++ 正则化记住预训练阶段学到的知识 | 切换到新任务不会擦除先前知识 |
|
||||
| **难例挖掘** | 训练聚焦于最令人困惑的样本，加速学习 | 相同训练数据下获得更高准确率 |
|
||||
|
||||
**Architecture**
|
||||
**架构**
|
||||
|
||||
```
|
||||
```
|
||||
WiFi Signal [56 channels] → Transformer + Graph Neural Network
|
||||
├→ 128-dim environment fingerprint (for search + identification)
|
||||
└→ 17-joint body pose (for human tracking)
|
||||
```
|
||||
```
|
||||
|
||||
**Quick Start**
|
||||
**快速开始**
|
||||
|
||||
```
|
||||
```bash
|
||||
# Step 1: Learn from raw WiFi data (no labels needed)
|
||||
cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50
|
||||
@@ -540,44 +550,43 @@ cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed
|
||||
# Step 4: Search — find similar environments or detect anomalies
|
||||
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env
|
||||
```
|
||||
```
|
||||
|
||||
**Training Modes**
|
||||
**训练模式**
|
||||
|
||||
| Mode | What you need | What you get |
|
||||
|------|--------------|-------------|
|
||||
| Self-Supervised | Just raw WiFi data | A model that understands WiFi signal structure |
|
||||
| Supervised | WiFi data + body pose labels | Full pose tracking + environment fingerprints |
|
||||
| Cross-Modal | WiFi data + camera footage | Fingerprints aligned with visual understanding |
|
||||
| 模式 | 所需数据 | 所得结果 |
|
||||
|------|----------|----------|
|
||||
| 自监督 | 仅需原始 WiFi 数据 | 理解 WiFi 信号结构的模型 |
|
||||
| 监督学习 | WiFi 数据 + 人体姿态标签 | 完整姿态追踪 + 环境指纹 |
|
||||
| 跨模态 | WiFi 数据 + 摄像头画面 | 与视觉理解对齐的指纹 |
|
||||
|
||||
**Fingerprint Index Types**
|
||||
**指纹索引类型**
|
||||
|
||||
| Index | What it stores | Real-world use |
|
||||
|-------|---------------|----------------|
|
||||
| `env_fingerprint` | Average room fingerprint | "Is this the kitchen or the bedroom?" |
|
||||
| `activity_pattern` | Activity boundaries | "Is someone cooking, sleeping, or exercising?" |
|
||||
| `temporal_baseline` | Normal conditions | "Something unusual just happened in this room" |
|
||||
| `person_track` | Individual movement signatures | "Person A just entered the living room" |
|
||||
| 索引 | 存储内容 | 实际用途 |
|
||||
|------|----------|----------|
|
||||
| ``env_fingerprint`` | 房间平均指纹 | "这是厨房还是卧室？" |
|
||||
| ``activity_pattern`` | 活动边界 | "有人在做饭、睡觉还是锻炼？" |
|
||||
| ``temporal_baseline`` | 正常状态 | "这个房间刚刚发生了异常情况" |
|
||||
| ``person_track`` | 个体运动特征 | "A 刚刚进入了客厅" |
|
||||
|
||||
**Model Size**
|
||||
**模型大小**
|
||||
|
||||
| Component | Parameters | Memory (on ESP32) |
|
||||
|-----------|-----------|-------------------|
|
||||
| Transformer backbone | ~28,000 | 28 KB |
|
||||
| Embedding projection head | ~25,000 | 25 KB |
|
||||
| Per-room MicroLoRA adapter | ~1,800 | 2 KB |
|
||||
| **Total** | **~55,000** | **55 KB** (of 520 KB available) |
|
||||
| 组件 | 参数数量 | 内存占用（ESP32 上） |
|
||||
|------|----------|----------------------|
|
||||
| Transformer 骨干网络 | ~28,000 | 28 KB |
|
||||
| 嵌入投影头 | ~25,000 | 25 KB |
|
||||
| 每房间 MicroLoRA 适配器 | ~1,800 | 2 KB |
|
||||
| **合计** | **~55,000** | **55 KB**（可用 520 KB） |
|
||||
|
||||
The self-learning system builds on the [AI Backbone (RuVector)](#ai-backbone-ruvector) signal-processing layer — attention, graph algorithms, and compression — adding contrastive learning on top.
|
||||
自学习系统构建在 [AI 骨干网络（RuVector）](#ai-backbone-ruvector) 信号处理层之上——注意力机制、图算法和压缩——并在其上层添加对比学习。
|
||||
|
||||
See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-contrastive-csi-embedding-model.md) for full architectural details.
|
||||
完整架构细节请参见 [``docs/adr/ADR-024-contrastive-csi-embedding-model.md``](docs/adr/ADR-024-contrastive-csi-embedding-model.md)。
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
## 🧩 Claude Code 与 Codex 插件
|
||||
|
||||
## 🧩 Claude Code & Codex Plugin
|
||||
|
||||
RuView ships a [Claude Code](https://docs.anthropic.com/en/docs/claude-code) plugin (and Codex prompt mirror) that wraps the whole workflow — onboarding, ESP32 setup, configuration, sensing apps, model training, advanced multistatic sensing, CLI/API/WASM, mmWave radar, and witness verification — as 9 skills, 7 `/ruview-*` commands, and 3 agents. It lives in [`plugins/ruview/`](plugins/ruview/README.md); the marketplace manifest is [`.claude-plugin/marketplace.json`](.claude-plugin/marketplace.json) at the repo root.
|
||||
RuView 提供了 [Claude Code](https://docs.anthropic.com/en/docs/claude-code) 插件（及 Codex 提示词镜像），将整个工作流——包括上手引导、ESP32 设置、配置、感知应用、模型训练、高级多站感知、CLI/API/WASM、毫米波雷达和见证验证——封装为 9 项技能、7 条 `/ruview-*` 命令和 3 个智能体。它位于 [`plugins/ruview/`](plugins/ruview/README.md)；市场清单位于仓库根目录下的 [`.claude-plugin/marketplace.json`](.claude-plugin/marketplace.json)。
|
||||
|
||||
```bash
|
||||
# In Claude Code — add this repo as a plugin marketplace, then install:
|
||||
@@ -597,59 +606,63 @@ claude --plugin-dir ./plugins/ruview
|
||||
# /ruview-verify → tests + deterministic proof + witness bundle
|
||||
```
|
||||
|
||||
**Codex (OpenAI CLI):** `cp plugins/ruview/codex/prompts/*.md ~/.codex/prompts/` — the seven `/ruview-*` commands are mirrored as Codex prompts; [`plugins/ruview/codex/AGENTS.md`](plugins/ruview/codex/AGENTS.md) carries the project rules. See [`plugins/ruview/codex/README.md`](plugins/ruview/codex/README.md).
|
||||
**Codex（OpenAI CLI）：** `cp plugins/ruview/codex/prompts/*.md ~/.codex/prompts/` —— 七条 `/ruview-*` 命令被镜像为 Codex 提示词；[`plugins/ruview/codex/AGENTS.md`](plugins/ruview/codex/AGENTS.md) 承载了项目规则。参见 [`plugins/ruview/codex/README.md`](plugins/ruview/codex/README.md)。
|
||||
|
||||
Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full details: [`plugins/ruview/README.md`](plugins/ruview/README.md).
|
||||
验证插件结构：`bash plugins/ruview/scripts/smoke.sh`。完整详情：[`plugins/ruview/README.md`](plugins/ruview/README.md)。
|
||||
|
||||
**Portable harness — `npx @ruvnet/ruview`:** a lighter, host-portable companion to the in-repo plugin, minted via [MetaHarness](https://www.npmjs.com/package/metaharness) and hardened per [ADR-182](docs/adr/ADR-182-npx-ruview-harness-via-metaharness.md). It runs **without cloning this repo** and on more hosts (Claude Code, Codex, Copilot, opencode, …), exposing the RuView operator tools (`onboard`, `verify`, `node_monitor`, `calibrate`, `node_flash`) over an MCP server — plus the project's **MEASURED-vs-CLAIMED honesty guardrail enforced in code** (`ruview.claim_check` flags untagged or retracted-"100%" accuracy claims). v0.1: the onboarding/verify/claim-check paths are tested (17/17, `verify.py` → PASS); the hardware tools are fail-closed wrappers. Try `npx @ruvnet/ruview` to onboard, or `npx @ruvnet/ruview claim-check --text "…"`. Source: [`harness/ruview/`](harness/ruview/README.md).
|
||||
**便携式工具集 —— `npx @ruvnet/ruview`：** 一种更轻量、主机可移植的插件伴侣工具，通过 [MetaHarness](https://www.npmjs.com/package/metaharness) 生成，并依据 [ADR-182](docs/adr/ADR-182-npx-ruview-harness-via-metaharness.md) 加固。它**无需克隆本仓库**即可运行，且支持更多主机（Claude Code、Codex、Copilot、opencode 等），通过 MCP 服务器暴露 RuView 操作工具（`onboard`、`verify`、`node_monitor`、`calibrate`、`node_flash`），此外还内置了项目的 **「实测值 vs 声称值」诚实护栏**（`ruview.claim_check` 会标记未标注或已撤回的"100%"准确率声明）。v0.1：上手/验证/声明检查路径已测试通过（17/17，`verify.py` → 通过）；硬件工具为故障关闭包装器。运行 `npx @ruvnet/ruview` 上手，或 `npx @ruvnet/ruview claim-check --text "…"`。源码：[`harness/ruview/`](harness/ruview/README.md)。
|
||||
|
||||
---
|
||||
|
||||
## 📖 Documentation
|
||||
## 📖 文档
|
||||
|
||||
| Document | Description |
|
||||
| 文档 | 说明 |
|
||||
|----------|-------------|
|
||||
| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
|
||||
| [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
|
||||
| [**Home Assistant + Matter Integration**](docs/integrations/home-assistant.md) | **Works with Home Assistant** via MQTT auto-discovery + **Works with Matter** (Apple Home / Google Home / Alexa / SmartThings) — full entity catalog, 3 starter blueprints, Lovelace dashboards, privacy mode, threshold tuning ([ADR-115](docs/adr/ADR-115-home-assistant-integration.md)). |
|
||||
| [**BFLD — Beamforming Feedback Layer for Detection**](v2/crates/wifi-densepose-bfld/README.md) | New privacy-gated WiFi sensing layer that measures + structurally prevents identity leakage from 802.11ac/ax Beamforming Feedback Information. Three type-enforced invariants (raw BFI never exits node, identity embedding is in-RAM-only, cross-site correlation cryptographically impossible via per-site BLAKE3 keyed hash + daily rotation). Ships full operator surface (`BfldPipeline`, `BfldPipelineHandle`, the Soul Signature §3.6 per-channel matcher `EnrolledMatcher`/`SoulMatchOracle` — experimental; named identity is data-gated, **measured** as not-separable on WiFi-only channels alone), MQTT topic router + HA-DISCO + availability + LWT, 3 operator HA blueprints, two runnable examples, eclipse-mosquitto:2 CI service container. 327+ tests. [ADR-118](docs/adr/ADR-118-bfld-beamforming-feedback-layer-for-detection.md) umbrella + sub-ADRs [119](docs/adr/ADR-119-bfld-frame-format-and-wire-protocol.md)/[120](docs/adr/ADR-120-bfld-privacy-class-and-hash-rotation.md)/[121](docs/adr/ADR-121-bfld-identity-risk-scoring.md)/[122](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md)/[123](docs/adr/ADR-123-bfld-capture-path-nexmon-and-esp32.md). Research dossier: [`docs/research/BFLD/`](docs/research/BFLD/) (11 files, 13,544 words). |
|
||||
| [**SENSE-BRIDGE — rvagent MCP server**](tools/ruview-mcp/README.md) | Dual-transport MCP server (`@ruvnet/rvagent`) bridging the RuView sensing stack to AI agents (Claude Code, Cursor, ruflo swarms). 6 tools wired: `ruview.presence.now`, `ruview.vitals.get_{breathing,heart_rate,all}`, `ruview.bfld.last_scan`, `ruview.bfld.subscribe`. stdio + Streamable HTTP (`POST /mcp`, Origin-validated, bearer-token auth, `127.0.0.1` bind). Full 20-tool Zod schema barrel + 5 RUVIEW-POLICY governance tools. 93 tests. [ADR-124](docs/adr/ADR-124-rvagent-mcp-ruvector-npm-integration.md). Try: `npx @ruvnet/rvagent stdio`. |
|
||||
| [Semantic Primitives — Precision/Recall](docs/integrations/semantic-primitives-metrics.md) | Per-primitive F1 on the held-out paired-capture set: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting, bathroom, fall-risk, bed-exit, no-movement, multi-room. |
|
||||
| [Claude Code / Codex Plugin](plugins/ruview/README.md) | The `ruview` plugin + marketplace — skills, `/ruview-*` commands, agents, and the Codex prompt mirror |
|
||||
| [Portable harness — `npx @ruvnet/ruview`](harness/ruview/README.md) | MetaHarness-minted, host-portable RuView operator harness — `ruview.*` MCP tools + the MEASURED-vs-CLAIMED honesty guardrail enforced in code ([ADR-182](docs/adr/ADR-182-npx-ruview-harness-via-metaharness.md)). A lighter, multi-host companion to the in-repo plugin. |
|
||||
| [Architecture Decisions](docs/adr/README.md) | 182 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure) |
|
||||
| [Domain Models](docs/ddd/README.md) | 8 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI, rvCSI) — bounded contexts, aggregates, domain events, and ubiquitous language |
|
||||
| [rvCSI — edge RF sensing runtime](https://github.com/ruvnet/rvcsi) | Rust-first / TypeScript-accessible / hardware-abstracted CSI runtime: multi-source ingestion (incl. real nexmon_csi `.pcap` from a **Raspberry Pi 5** / Pi 4 / Pi 3B+ — CYW43455 / BCM43455c0) → validation → DSP → typed events → RuVector RF memory ([ADR-095](docs/adr/ADR-095-rvcsi-edge-rf-sensing-platform.md), [ADR-096](docs/adr/ADR-096-rvcsi-ffi-crate-layout.md), [domain model](docs/ddd/rvcsi-domain-model.md)). Now its own repo — [`ruvnet/rvcsi`](https://github.com/ruvnet/rvcsi) — vendored here under `vendor/rvcsi`; 9 `rvcsi-*` crates on crates.io, `@ruv/rvcsi` on npm, plus a Claude Code plugin. |
|
||||
| [Desktop App](v2/crates/wifi-densepose-desktop/README.md) | **WIP** — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization |
|
||||
| `ruview-swarm` | Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4/ArduPilot compatibility, Ruflo AI-agent integration |
|
||||
| [Medical Examples](examples/medical/README.md) | Contactless blood pressure, heart rate, breathing rate via 60 GHz mmWave radar — $15 hardware, no wearable |
|
||||
| [Extended Documentation](docs/readme-details.md) | Latest additions, key features, installation, quick start, signal processing, training, CLI, testing, deployment, and changelog |
|
||||
| [用户指南](docs/user-guide.md) | 分步指南：安装、首次运行、API 使用、硬件设置、训练 |
|
||||
| [构建指南](docs/build-guide.md) | 从源码构建（Rust 和 Python） |
|
||||
| [**Home Assistant + Matter 集成**](docs/integrations/home-assistant.md) | **可与 Home Assistant 配合使用**（通过 MQTT 自动发现）+ **可与 Matter 配合使用**（Apple Home / Google Home / Alexa / SmartThings）—— 完整实体目录、3 个入门蓝图、Lovelace 面板、隐私模式、阈值调优（[ADR-115](docs/adr/ADR-115-home-assistant-integration.md)）。 |
|
||||
| [**BFLD —— 用于检测的波束赋形反馈层**](v2/crates/wifi-densepose-bfld/README.md) | 新的隐私保护型 WiFi 感知层，可测量并从结构上防止 802.11ac/ax 波束赋形反馈信息导致身份泄露。三个类型强制不变量（原始 BFI 永不离开节点，身份嵌入仅驻留于内存，通过基于站点的 BLAKE3 密钥哈希和每日轮换从密码学上杜绝跨站点关联）。提供完整操作接口（`BfldPipeline`、`BfldPipelineHandle`、Soul Signature §3.6 每通道匹配器 `EnrolledMatcher`/`SoulMatchOracle` —— 实验性；命名身份受数据门控，在纯 WiFi 通道上**实测**为不可分离）、MQTT 主题路由器 + HA-DISCO + 可用性 + LWT、3 个操作员 HA 蓝图、两个可运行示例、eclipse-mosquitto:2 CI 服务容器。327+ 测试。[ADR-118](docs/adr/ADR-118-bfld-beamforming-feedback-layer-for-detection.md) 总括 + 子 ADR [119](docs/adr/ADR-119-bfld-frame-format-and-wire-protocol.md)/[120](docs/adr/ADR-120-bfld-privacy-class-and-hash-rotation.md)/[121](docs/adr/ADR-121-bfld-identity-risk-scoring.md)/[122](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md)/[123](docs/adr/ADR-123-bfld-capture-path-nexmon-and-esp32.md)。研究报告：[`docs/research/BFLD/`](docs/research/BFLD/)（11 个文件，13,544 词）。 |
|
||||
| [**SENSE-BRIDGE —— rvagent MCP 服务器**](tools/ruview-mcp/README.md) | 双传输层 MCP 服务器（`@ruvnet/rvagent`），将 RuView 感知栈桥接到 AI 智能体（Claude Code、Cursor、ruflo 群体）。已接入 6 个工具：`ruview.presence.now`、`ruview.vitals.get_{breathing,heart_rate,all}`、`ruview.bfld.last_scan`、`ruview.bfld.subscribe`。stdio + Streamable HTTP（`POST /mcp`、来源验证、Bearer Token 认证、`127.0.0.1` 绑定）。完整 20 个工具的 Zod Schema 桶 + 5 个 RUVIEW-POLICY 治理工具。93 个测试。[ADR-124](docs/adr/ADR-124-rvagent-mcp-ruvector-npm-integration.md)。试用：`npx @ruvnet/rvagent stdio`。 |
|
||||
| [语义基元 —— 精确率/召回率](docs/integrations/semantic-primitives-metrics.md) | 在留出配对采集集上按基元计算的 F1：someone-sleeping、possible-distress、room-active、elderly-inactivity-anomaly、meeting、bathroom、fall-risk、bed-exit、no-movement、multi-room。 |
|
||||
| [Claude Code / Codex 插件](plugins/ruview/README.md) | `ruview` 插件 + 市场 —— 技能、`/ruview-*` 命令、智能体和 Codex 提示词镜像 |
|
||||
| [便携式工具集 —— `npx @ruvnet/ruview`](harness/ruview/README.md) | 经 MetaHarness 生成、主机可移植的 RuView 操作工具集 —— `ruview.*` 个 MCP 工具 + 内置的「实测值 vs 声称值」诚实护栏（[ADR-182](docs/adr/ADR-182-npx-ruview-harness-via-metaharness.md)）。仓库内插件的轻量多主机伴侣。 |
|
||||
| [架构决策](docs/adr/README.md) | 182 个 ADR —— 每项技术选择的原因，按领域组织（硬件、信号处理、机器学习、平台、基础设施） |
|
||||
| [领域模型](docs/ddd/README.md) | 8 个 DDD 模型（RuvSense、信号处理、训练流水线、硬件平台、感知服务器、WiFi-Mat、CHCI、rvCSI）—— 限界上下文、聚合、领域事件和统一语言 |
|
||||
| [rvCSI —— 边缘 RF 感知运行时](https://github.com/ruvnet/rvcsi) | Rust 优先 / TypeScript 可访问 / 硬件抽象的 CSI 运行时：多源摄入（包括来自 **Raspberry Pi 5** / Pi 4 / Pi 3B+ 的实际 nexmon_csi `.pcap` —— CYW43455 / BCM43455c0）→ 验证 → DSP → 类型化事件 → RuVector RF 内存（[ADR-095](docs/adr/ADR-095-rvcsi-edge-rf-sensing-platform.md)、[ADR-096](docs/adr/ADR-096-rvcsi-ffi-crate-layout.md)、[领域模型](docs/ddd/rvcsi-domain-model.md)）。现为独立仓库 —— [`ruvnet/rvcsi`](https://github.com/ruvnet/rvcsi) —— 在本仓库中通过 `vendor/rvcsi` 进行 vendor；crates.io 上有 9 个 `rvcsi-*` crate，npm 上有 `@ruv/rvcsi`，另附 Claude Code 插件。 |
|
||||
| [桌面应用](v2/crates/wifi-densepose-desktop/README.md) | **开发中** —— 基于 Tauri v2 的桌面应用，用于节点管理、OTA 更新、WASM 部署和 Mesh 可视化 |
|
||||
| `ruview-swarm` | 无人机集群控制系统（ADR-148）—— 分层 Mesh 拓扑、Raft 共识、MARL、CSI 感知载荷、MAVLink/PX4/ArduPilot 兼容性、Ruflo AI 智能体集成 |
|
||||
| [医疗示例](examples/medical/README.md) | 通过 60 GHz 毫米波雷达实现非接触式血压、心率、呼吸率测量 —— 硬件仅需 15 美元，无需可穿戴设备 |
|
||||
| [扩展文档](docs/readme-details.md) | 最新新增、核心功能、安装、快速入门、信号处理、训练、CLI、测试、部署和更新日志 |
|
||||
|
||||
---
|
||||
|
||||
## 🚧 Beta software
|
||||
## 🚧 Beta 软件
|
||||
|
||||
> **Beta Software** — Under active development. APIs and firmware may change. Known limitations:
|
||||
> - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
|
||||
> - Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a [Cognitum Seed](https://cognitum.one) for best results
|
||||
> - Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — [camera ground-truth training](docs/adr/ADR-079-camera-ground-truth-training.md) targets **35%+ PCK@20**; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending.
|
||||
> **Beta 软件** —— 正在积极开发中。API 和固件可能发生变更。已知限制：
|
||||
> - ESP32-C3 和原始 ESP32 不受支持（单核，不足以处理 CSI DSP）
|
||||
> - 单个 ESP32 部署的空间分辨率有限 —— 请使用 2 个以上节点，或添加 [Cognitum Seed](https://cognitum.one) 以获得最佳效果
|
||||
> - 无摄像头的姿态估计精度有限（PCK@20 ≈ 2.5%，基于代理标签）—— [摄像头真值训练](docs/adr/ADR-079-camera-ground-truth-training.md)的目标是 **PCK@20 达到 35% 以上**；该流水线已实现，但数据采集和评估阶段（ADR-079 P7–P9）尚待完成。
|
||||
>
|
||||
> Contributions and bug reports welcome at [Issues](https://github.com/ruvnet/RuView/issues).
|
||||
> 欢迎通过 [Issues](https://github.com/ruvnet/RuView/issues). 提交贡献和错误报告。
|
||||
USD 预算：$0/$3；本月 OpenAI API 评估额度剩余 $3。
|
||||
|
||||
## 📄 License
|
||||
## 📄 许可证
|
||||
|
||||
MIT License — see [LICENSE](LICENSE) for details.
|
||||
MIT 许可证 — 详见 [LICENSE](LICENSE)。
|
||||
|
||||
## 🤝 Creator Affiliate Program
|
||||
## 🤝 创作者联盟计划
|
||||
|
||||
**For TikTok · Instagram · YouTube creators** — earn **25% on every Cognitum sale** you refer. The RuFlo, RuView, and RuVector videos you're already making have done millions of views; get paid for the orders they drive. Click-tracking activates instantly; commissions activate after a quick manual review (usually under 24 hours).
|
||||
**面向 TikTok · Instagram · YouTube 创作者** — 您推荐的每笔 **Cognitum 销售**均可获得 **25% 佣金**。您已经在制作的 RuFlo、RuView 和 RuVector 视频已获得数百万播放量；现在，通过这些视频带来的订单获得报酬。点击追踪即刻生效，佣金在快速人工审核后激活（通常在 24 小时内）。
|
||||
|
||||
[Apply now → cognitum.one/affiliate](https://cognitum.one/affiliate)
|
||||
[立即申请 → cognitum.one/affiliate](https://cognitum.one/affiliate))
|
||||
|
||||
## 📞 Support
|
||||
## 📞 支持
|
||||
|
||||
[GitHub Issues](https://github.com/ruvnet/RuView/issues) | [Discussions](https://github.com/ruvnet/RuView/discussions) | [PyPI](https://pypi.org/project/wifi-densepose/)
|
||||
[GitHub Issues](https://github.com/ruvnet/RuView/issues)) | [讨论区](https://github.com/ruvnet/RuView/discussions)) | [PyPI](https://pypi.org/project/wifi-densepose/))
|
||||
|
||||
---
|
||||
|
||||
**WiFi DensePose** — Privacy-preserving human pose estimation through WiFi signals.
|
||||
**WiFi DensePose** — 通过 WiFi 信号进行隐私保护的人体姿态估计。
|
||||
预算：$0/$3；剩余 $3。
|
||||
|
||||
**WeHub** — 我们编写工具，助您构建未来。⚡️
|
||||
|
||||
Reference in New Issue
Block a user