Why it matters
Without such a system, lame ponies, delayed welfare checks, inconsistent grading, and unverified data can lead to lost fixtures, welfare breaches, and unresolved handicap disputes.
From design to safety
Design
AI design for gait analytics
The design reads planned data streams from gait video, stride length metrics, head nod cues, hoof landing patterns, fetlock loading estimates, and vet grading workflows to define decision thresholds.
- Gait Video System
- Stride Length Sensor
- Head Nod Monitor
Build
Hardware and data pipelines built
Data schemas, camera placements, sensor calibrations, and vet record interfaces are implemented to feed AI models and ensure clean, timestamped data flow.
- Gait Video System
- Stride Length Sensor
- Head Nod Monitor
Operation
Real-time gait insights on match days
Live gait video streams are analyzed to produce cadence, head motion, hoof timing, and fetlock load scores; alerts surface to the club manager and equine vet.
- Gait Video System
- Stride Length Sensor
- Hoof Landing Analysis
- Fetlock Loading Meter
Maintenance
Calibration and model drift control
Regular camera recalibration, sensor checks, and vet sign-off reviews keep gait metrics aligned with clinical lameness scales.
- Gait Video System
- Stride Length Sensor
- Vet Lameness Grading Record
Safety
Welfare and compliance auditing
AI-generated findings are reviewed by the club vet and welfare officer; gated decisions are signed off before withholding riding, adjusting handicaps, or scheduling vet checks.
- Gait Video System
- Vet Lameness Grading Record
Part by part
- 1
Gait Video System
- Watch for
- Camera blind spots or occluded views during fast chukkas.
- AI action
- Trigger gait anomaly alerts when motion poor alignment occurs.
- 2
Stride Length Sensor
- Watch for
- Unnatural shortening or inconsistent stride during canter.
- AI action
- Flag potential lameness risk and request vet assessment.
- 3
Head Nod Monitor
- Watch for
- Excessive head bobbing or asymmetry between sides.
- AI action
- Flag gating of high-risk ponies for urgent vet check.
- 4
Hoof Landing Analysis
- Watch for
- Uneven hoof placement or toe-first landing indicative of discomfort.
- AI action
- Annotate potential forelimb distress and schedule inspection.
- 5
Fetlock Loading Meter
- Watch for
- Elevated fetlock loading during stance or flight phases.
- AI action
- Raise alert for possible joint strain and modify workload.
- 6
Vet Lameness Grading Record
- Watch for
- Discrepancies between video cues and clinical scores.
- AI action
- Record grading consensus and lock handicapping adjustments.
Worked example
Polo Club 9-Field Demonstration
Illustrative scenario with sample data.
A mid-size club expands gait-based checks to pre-screen 40 ponies before a weekend tournament to reduce last-minute veterinary disruptions and ensure consistent welfare monitoring across all strings.
Signal in
gait_video: true; field_id: F7; pony_id: P12; timestamp: 2026-11-01T09:20:00Z; stride_length_mm: 980; head_nod_deg: 8.5; hoof_landing: fore_init; fetlock_load_kg: 180; vet_score: 2; mobility_state: normal; training_stage: preparation
System out
alert: pony P12 flagged for potential lameness; action: schedule vet exam; notes: gait anomaly with elevated fetlock load; recommended rest day; follow-up: recheck after 48 hours.
Human sign-off: Club equine vet reviews alert, confirms lameness risk, approves vet exam and temporary mobility restriction; welfare officer logs the decision in the lameness file.
How to roll it out
Phase 1
Weeks 1 to 4
Baseline data capture from gait video across all ponies; validate triggers and feed initial alerts to welfare officer.
Phase 2
Weeks 5 to 8
Integrate vet grading sign-off workflow; test quiet periods and refine thresholds with match-day runs.
Phase 3
Weeks 9 to 12
Formalize records into Blue Book-compatible lameness entries; align handicap procedures with AI findings.
Where people stay in charge
- Decisions to withhold riding or to alter handicaps must be signed by the club equine vet and the tournament committee.
- All data used for official welfare or handicap decisions must be reviewed by the governing association for compliance.
- Lameness flags require confirmed clinical assessment before any welfare action or record update.
Questions owners and crews ask
How does gait video AI detect lameness without bias from different ponies?
The system uses standardized metrics across ponies, applying calibrated baselines per pony and adjusting for stride variability to minimize bias; all thresholds are reviewed by the club vet.
What happens if the AI flags a pony during a match?
An immediate alert is sent to the head groom and club vet; a welfare review is initiated and, if needed, the pony is removed from play pending assessment.
How are data stored and who can access lameness records?
All gait metrics and vet scores are stored in a secure club records system with role-based access for management, welfare officers, and licensing bodies as required.
Can AI influence handicap decisions and tournament outcomes?
If a vet-approved assessment changes a pony’s suitability or loading profile, corresponding handicap adjustments are recorded and published through standard club procedures.