An AI-powered computer vision system that detects slip, trip, and fall risks on construction and industrial sites in real time and triggers immediate safety alerts.
Construction sites and high-risk industrial facilities involve working at height, uneven terrain, and moving equipment, making slip-and-fall incidents one of the leading causes of workplace injuries and fatalities.
MoreYeahs built an AI-powered slip and fall detection system for organizations operating in these environments, giving site supervisors and safety officers a way to monitor worker posture and movement continuously rather than relying on manual observation alone.
Before automation, safety teams faced recurring gaps in how falls and unsafe postures were identified and escalated:
Manual Supervision Only: safety mechanisms depended primarily on manual supervision and delayed incident reporting.
No Real-Time Detection: supervisors had no reliable way to catch unsafe postures, sudden loss of balance, or fall events as they happened.
Slow Emergency Response: delayed detection increased both injury severity and the time needed to reach an injured worker.
Difficult to Scale: visual monitoring across large, multi-zone sites could not scale with existing staffing levels.
MoreYeahs designed a real-time AI safety monitoring system that analyzes existing camera feeds to continuously track worker posture, movement patterns, and stability indicators.
Posture and Motion Analysis: computer vision and motion-analysis models continuously assess worker stability to flag unsafe postures, slips, and fall events.
Instant Multi-Channel Alerts: detected incidents trigger immediate alerts to supervisors through mobile apps, SMS, and local alarms or visual indicators.
Location-Aware Escalation: each alert automatically shares the worker's location and escalates to safety officers and medical response teams.
Automatic Incident Logging: every event is timestamped, logged, and stored with a video clip for review and reporting.
The system runs as a continuous pipeline from live video capture through classification, alerting, and incident logging.
Continuous Monitoring: video streams from zone cameras are analyzed continuously for posture, gait, and motion stability.
Risk Scoring: a risk score is calculated for each worker or activity, enabling early warnings before a fall occurs.
Critical Event Classification: confirmed fall events are classified as critical incidents and immediately routed to supervisors and safety officers.
Resilience Handling: partial camera occlusion triggers a motion-pattern fallback model with a low-confidence tag, and camera feed loss automatically flags the affected zone as unmonitored.
The solution combines edge and server-based inference with pose estimation and fall-detection models built for real-time video analysis.
In validation testing, the solution met its target benchmarks for detection accuracy and response speed.
90%+ fall detection accuracy achieved across test scenarios.
Unsafe posture and slip-risk patterns detected before the fall in over 70% of simulated cases.
Alerts generated within 3 seconds of a detected fall event, well inside the target response window.
Every incident automatically logged with correct zone location, timestamp, and video clip.
Beyond individual incident detection, the system gives safety teams a scalable way to extend supervision across large sites.
Faster Emergency Response: automated, location-aware alerts cut the time between a fall and a response team being notified.
Reduced Injury Severity: earlier detection of unsafe posture allows intervention before a fall occurs, not just after.
Consistent Safety Oversight: continuous AI monitoring reduces dependence on manual supervision across multiple zones simultaneously.
Audit-Ready Records: timestamped incident logs and video clips support safety audits and continuous model improvement.
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