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.

Proven Proven live in runtime

Demonstrated on real public research data.

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.
Preparing the recorded fieldLoading real LatentField pipeline output…

This is real pipeline output for Healthcare, replayed. The exact reading depends on your room.

Bring this into the real roomPatient Room Field Node3 sensing nodes · ~$140 parts · about 60 min
Go deeper

How the field becomes an answer

RTI differential imaging for spatial occupancy map occupancy-count regression compartment model tracks zone dwell/flow CUSUM flags crowding surge vs. baseline capacity

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

Complete first-room estimatePatient Room Field Node3 nodes · ~$140 parts · 60 min Request kit, subscribe, or set up →
Compatible DIY hardware options
Practitioner Discrete$60–90

15 min · beginner

Operator Single-Venue$280–380

120 min · advanced

← All Healthcare capabilities