Agriculture · Frontier

Taught Lameness & Abnormal-Gait Signature

Tag a few limping animals — it learns to spot limping everywhere.

Frontier Frontier live in runtime

Plausible physics, early work — shown honestly as an experiment.

A healthy walk has an even left-right rhythm the signal can feel. A limp makes that rhythm lopsided — one side lands softer and slower. The farmer tags a few lame animals, and the system learns that lopsidedness and quietly scores every animal's walk from then on.

In the physics: Locomotion imprints a periodic multi-limb micro-Doppler signature; lameness introduces a measurable left/right timing and amplitude asymmetry and altered stride cadence, a specific deformation of the normal gait rhythm rather than a change in overall activity.
Preparing the recorded fieldLoading real LatentField pipeline output…

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

Bring this into the real roomBarn Activity Node3 sensing nodes · ~$130 parts · about 90 min
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How the field becomes an answer

Doppler micro-Doppler gait-cycle extraction along RTI-tracked walking paths axis discovery (poke-and-observe): controlled labeling of confirmed-lame vs sound animals teaches a lameness axis project new gaits onto that axis anomaly manifold + CUSUM for gradual worsening

How we prove it

Own-hardware over a race/return-alley vs veterinary locomotion-scoring (1-5) and force-plate ground truth; SenseFi/CSI-Bench gait/activity data to prototype the micro-Doppler gait-cycle extractor before the taught axis.

Who it is for

'GaitGuard' — self-training lameness screener at the parlor exit for dairies; catches lameness days earlier when it is still curable and cheap.

runtime Runs today in LatentField 2.0 as gait_signature

Complete first-room estimateBarn Activity Node3 nodes · ~$130 parts · 90 min Request kit, subscribe, or set up →
Compatible DIY hardware options
Prosumer Pro (paired node)$110–150

60 min · intermediate

Operator Single-Venue$280–380

120 min · advanced

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