FIT-EDGE · ON-DEVICE AI

On-device AI detects risks during unmanned hours right on site — no cloud round-trip — and transmits only de-identified safety logs.

HOW IT WORKS

From detection to response, 0.1s

Every inference completes on the edge device inside the venue. Video never leaves the site — only safety signals travel.

👁
STEP 01

Edge detection

On-site devices capture falls, equipment accidents, and anomalies in real time from millimeter-level spatial data. Raw RGB frames are discarded immediately.

🧠
STEP 02

On-device analysis

Pose-signature AI instantly classifies and clusters risk from grade A (critical) to F (normal).

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STEP 03

Real-time response

Risk events reach owner and HQ consoles within 0.1 seconds, with a four-stage response kanban tracking every action taken.

UNMANNED · 24/7 MONITORING
Gym spaces are monitored 24/7 during unmanned hours. Falls, equipment accidents, and other anomalies trigger alerts within 0.1 seconds. (Sample footage)
ARCHITECTURE

Device — Edge — Console

📷 Device On-site sensor · RGB discarded ⚡ Edge AI On-device inference Risk grading A–F 🖥 Console Owner & HQ real-time monitoring pose stream de-identified signal
Fit-Edge AI monitoring console — cluster dashboard and response kanban
FREE_WEIGHT · BARBELL_DROP
GOLF · GENERIC
REALTIME: CONNECTED · Live 3 stores

The Fit-Edge console in live operation — anomaly clusters (A critical to F normal), a four-stage response kanban, an incident labeling queue, and 3D unified monitoring, all on one screen.

Patent certificate preview
⚡ PATENTED TECHNOLOGY

Core technology, officially registered and proven

Fitness facility monitoring method and device using video recognition and AI
Patent No. 10-2786321 · Registered Mar 20, 2025

0s
Risk event delivery time
0/7
Uninterrupted unmanned monitoring
0 grades
Risk classification (A–F)
PRIVACY-BY-DESIGN

Technology that protects people
protects privacy first

🔒

Raw frames discarded instantly

No video, faces, or names are stored or transmitted. The camera doesn't "watch" — it detects.

🔑

Pseudonymous identification

SHA-256-based pose_signature pseudonyms with ε-differential-privacy noise applied.

📄

De-identified safety logs

Only the minimal event metadata required for monitoring is graded, recorded, and delivered.

A safety net for unmanned spaces —
bring in Fit-Edge

From PoC to on-site deployment, we walk the validation journey with you.