Datacenter · Proven

Lights-Out Sentinel: Human-vs-Robot Discrimination

In robot-patrolled halls, it alarms only when the moving thing is a person.

Proven Proven live in runtime

Demonstrated on real public research data.

A walking human stirs the WiFi field with a messy, rhythmic pattern — swinging arms, stepping legs, and always breathing. A wheeled robot glides at constant speed with a clean motor hum and no breath at all. The system tells the two apart, so robots stop drowning the alarms.

In the physics: Human micro-Doppler is broadband and quasi-periodic (limb swing 1-3Hz with harmonics, gait cadence) and always carries a 0.1-0.5Hz breathing component; wheeled robots produce narrowband, constant-velocity Doppler with motor-vibration lines and zero respiration — separable signatures in the 0.1-40Hz band.
Preparing the recorded fieldLoading real LatentField pipeline output…

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

Bring this into the real roomCage & Plenum Sentinel6 sensing nodes · ~$320 parts · about 120 min
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How the field becomes an answer

Micro-Doppler spectrogram features + breathing-band check machine-vs-human classifier (already proven) RTI trajectory tagging so each track is labeled human/robot anomaly manifold alarms on 'human track in lights-out zone'.

How we prove it

Machine-vs-human discrimination already class A on our data; validate trajectory tagging on OPERAnet + own-hardware with a roving vacuum robot and human confederate, measure per-track label accuracy.

Who it is for

The only motion alarm that ignores your robots — 'a human stepped into Hall C at 03:14, and we knew it wasn't the tape bot' — sold to hyperscaler physical security operations.

runtime Runs today in LatentField 2.0 as presence

Complete first-room estimateCage & Plenum Sentinel6 nodes · ~$320 parts · 120 min Request kit, subscribe, or set up →
Compatible DIY hardware options
Operator Single-Venue$280–380

120 min · advanced

Operator Multi-Node$700–950

240 min · installer

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