Industrial · Proven
Forklift-Pedestrian Near-Miss Field
Tracks people and forklifts separately, and warns when they get dangerously close.
Demonstrated on real public research data.
How it works
A walking person stirs the WiFi field with a rhythmic, limb-swinging pattern; a forklift moves as one smooth rigid block with a motor hum. The system tells them apart and follows both around the plant. When their paths converge in an aisle, it warns — and every near miss goes on the heatmap.
In the physics: Humans and forklifts have categorically different Doppler signatures (limb-swing micro-Doppler comb vs rigid-body monotone Doppler + mast vibration) — machine-vs-human discrimination is already proven. RTI provides simultaneous device-free tracks of both at ~0.5-1m, sufficient for zone-level proximity logic.
See it work
This is real pipeline output for Industrial, replayed. The exact reading depends on your room.
Go deeper
How the field becomes an answer
How we prove it
Machine-vs-human already class A; multi-target tracking validated on WiMANS and OPERAnet; own-hardware trial in one live warehouse aisle with UWB ground-truth tags on one forklift and two workers for a week.
Who it is for
A near-miss heatmap of the whole warehouse with zero tags and zero cameras — sold to the safety manager who can't get camera analytics past the works council.
The matching solution
Complete first-room estimateMachine Bay Watch6 nodes · ~$320 parts · 120 min Request kit, subscribe, or set up →Compatible DIY hardware options
120 min · advanced
240 min · installer