Hospitality · Strong theory
FitRoom Analytics — fitting-room intelligence where cameras are illegal
Fitting-room insight where cameras are illegal — occupancy, waits, and safety.
The physics is solid; our own validation is scheduled.
How it works
Through the stall partition, the signal feels whether someone is inside and how long they have been there. Trying on clothes has a busy arm-motion rhythm that looks nothing like standing still on a phone. And a collapse looks like a fall anywhere else — a sharp ripple, then stillness — so distress is caught too.
In the physics: Presence and dwell come from breathing-band and motion Doppler through the stall partition; trying on clothes produces a distinctive high-articulation limb micro-Doppler burst pattern distinct from standing/phone use; a collapse is the proven fall transient.
See it work
This is real pipeline output for Hospitality, replayed. The exact reading depends on your room.
Go deeper
How the field becomes an answer
How we prove it
SenseFi/CSI-Bench activity recognition for the gesture classes; own two-stall mockup with scripted try-on sessions and dwell ground truth.
Who it is for
Fitting-room conversion analytics plus duty-of-care, in the one zone where every camera vendor must say no — for the retail CX and LP leads jointly.
runtime Runs today in LatentField 2.0 as occupancy_estimate
The matching solution
Complete first-room estimateFloor & Bar Pulse3 nodes · ~$130 parts · 90 min Request kit, subscribe, or set up →Compatible DIY hardware options
120 min · advanced