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Beta Menengah

Node Medan Kamar Pasien

Kesadaran napas, gerakan, dan jatuh nirsentuh untuk kamar pasien — tanpa wearable, tanpa kamera.

Explore the Healthcare possibilities →

What it takes

A validation-ready kit with a guided handoff.

Kesadaran napas, gerakan, dan jatuh nirsentuh untuk kamar pasien — tanpa wearable, tanpa kamera. Kit ini menggelar runtime LatentField 2.0 untuk dunia Healthcare: mesh WiFi-CSI ESP32-S3 memberi umpan ke pipeline yang sama (compressed sensing, manifold anomali PCA + MVE, medan tomografi radio, kompartemen) yang divalidasi pada 312 GB data riset publik. Berjalan lokal atau dalam kontainer, disandingkan ke akunmu dengan PIN kode-klaim, dan melapor dalam bahasa yang jelas. Kemampuan: presence, breathing_rate, motion_level, fall_detect, sleep_quality_proxy, occupancy_estimate. Jelajahi semuanya di /v/healthcare.

Starting hardware~$140
Typical setup60 min
Core hardwareESP32-S3 CSI mesh + LatentField 2.0 runtime
Data approachLocal by default

Explore freely. Create an account only when you want to save and deploy.

01Pair

One-time PIN adds the correct devices to this setup.

02Verify

Latent checks the live heartbeat before publishing.

03Hand off

The recipient receives a calm, branded app—not a control panel.

What is included
3× node CSI ESP32-S3 (Seeed XIAO / T-Dongle-S3)×3~$27
Hub USB bertenaga + kabel USB-C~$18
Host runtime — Raspberry Pi 5, mini-PC, atau komputer apa pun yang selalu menyala (atau kontainer)~$80
Estimated hardware total~$140
Preview the installation
1Flash node mesh CSI
2Jalankan runtime LatentField 2.0
3Kalibrasi pada ruang kosong
4Baca medan Healthcare
Open the step-by-step preview
Evidence, source and technical config
ReadinessBeta
Source projectlatent-field2.0
SensorsWiFi CSI

The full deployment config is exposed for technical review and can be copied or downloaded.

# LatentField 2.0 — Patient Room Field Node runtime config # Runs on the host with: lf2 run --config configs/verticals/health.yaml pipeline: { vertical: health } capabilities: - presence - breathing_rate - motion_level - fall_detect - sleep_quality_proxy - occupancy_estimate zones: - { name: bed, shape: rect, x0: 0, y0: 0, x1: 2.5, y1: 3 } - { name: room, shape: rect, x0: 0, y0: 0, x1: 6, y1: 5 } manifold: { baseline: activity } # lived-in space: routine is normal, departures alert
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