Healthcare · Proven
Waiting-room and ED occupancy / crowd-density for infection control and flow
Counts how many people are waiting, and where, without any cameras.
Demonstrated on real public research data.
How it works
Every person in a room bends and scatters WiFi waves a little. More people means more disturbance, so the system can count heads and see which corners are crowded. Nobody is photographed or identified.
In the physics: Each additional person adds independent reflectors and scattering paths that measurably raise multipath richness and RTI cell occupancy; the aggregate change scales monotonically with count, and dwell shows as persistent occupancy in specific zones.
See it work
This is real pipeline output for Healthcare, replayed. The exact reading depends on your room.
Go deeper
How the field becomes an answer
How we prove it
Occupancy count r=0.9 already proven; confirm on WiMANS multi-person scenes and own-hardware waiting-room deployment vs. manual/turnstile counts.
Who it is for
'Privacy-safe crowd meter' — real-time occupancy/flow for ED operations and infection-prevention capacity management.
runtime Runs today in LatentField 2.0 as occupancy_estimate
The matching solution
Complete first-room estimatePatient Room Field Node3 nodes · ~$140 parts · 60 min Request kit, subscribe, or set up →Compatible DIY hardware options
15 min · beginner
120 min · advanced