Instant Fall Detection.
In a medical emergency, the response clock starts the instant a worker goes down. DHI catches slips, trips, and severe collapses from high ladders or scaffolding and routes the alert to responders in seconds, not minutes.
Logic Validation
Instant Fall Detection requires precise computer vision engineering.
Verify the specific neural thresholds and VMS integration triggers used to automate this safety protocol.
Wearables Required
In a medical emergency, the response clock starts the instant a worker goes down. DHI catches slips, trips, and severe collapses from high ladders or scaffolding and routes the alert to responders in seconds, not minutes.
Skeletal Inferencing
Pose Estimation Models: Extracts skeletal joints of workers to track body kinematics, not just bounding boxes.
Integration fit
When a collapse is detected, DHI passes the exact quadrant zone, camera feed, and time-of-incident directly into a VMS macro, which can auto-page emergency responders to remote corners of the facility.
Failure mode coverage
Gravity & Velocity Anchors: Recognizes the difference between a worker voluntarily lying down to inspect machinery versus a rapid uncontrolled descent.
Use-case fit
No Wearables Required: Hardhat beacons break and lanyards are forgotten. A CCTV camera covers the entire zone passively.
Direct answer
Can instant fall detection work with existing CCTV?
DHI's Fall Detection AI uses real-time skeletal pose estimation to detect workplace slips, trips, and collapses. Operating without the need for wearable devices, the system processes existing CCTV feeds on-premises at the edge. It distinguishes between a worker actively crouching and an uncontrolled fall, routing immediate alerts with location data to medical responders in under 150ms.
Scene fit
Fall detection works best where the camera sees the body posture clearly, including stair landings, mezzanines, maintenance aisles, and remote industrial corners.
No-wearable fit
CCTV-based detection is useful when teams cannot rely on every worker, visitor, contractor, or patient to carry a charged wearable.
False-alert review
The pilot should separate actual falls from crouching, kneeling, maintenance work, sitting, and other normal postures that can confuse weaker systems.
Response fit
The alert needs a camera, zone label, timestamp, and responder path so the team can find the person without searching the building.
Pilot assumptions to validate
- The first camera zone has enough angle and lighting to distinguish posture changes.
- The facility agrees what counts as a person-down event before testing.
- Responders receive a location label and camera context, not just a generic fall message.
Sources and next pages
Person-down fit
Fall detection is strongest in zones where nobody is supposed to be invisible for long.
This page is specific to collapse, slip, trip, scaffold, and ladder scenarios where a person can be down outside the normal line of sight and where passive CCTV is more reliable than asking every worker to wear a device.
Primary scene
Good pilot areas include maintenance aisles, mezzanines, stair landings, remote utility rooms, scaffold zones, and low-traffic industrial corners where a collapsed worker may not be noticed quickly.
Operator action
The alert needs a zone label, camera feed, and escalation path to the supervisor or medical responder responsible for that part of the facility, not a generic notification in an unattended inbox.
Pilot measure
Track time from body-down posture to alarm, false positives from crouching maintenance work, and whether responders receive enough location context to reach the person without searching.
Detection Logic
- Pose Estimation Models: Extracts skeletal joints of workers to track body kinematics, not just bounding boxes.
- Gravity & Velocity Anchors: Recognizes the difference between a worker voluntarily lying down to inspect machinery versus a rapid uncontrolled descent.
- No Wearables Required: Hardhat beacons break and lanyards are forgotten. A CCTV camera covers the entire zone passively.
Direct Medical Routing
When a collapse is detected, DHI passes the exact quadrant zone, camera feed, and time-of-incident directly into a VMS macro, which can auto-page emergency responders to remote corners of the facility.
Industrial Privacy & Sovereignty
DHI's models do not stream raw video to the public cloud. All safety inferencing occurs on-premise within your local network, supporting privacy review and tighter control for critical infrastructure environments.
Continue Exploring
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Where ladders and scaffolding are most deadly.
Forklift Safety
Usually the cause of the worst physical impacts.
Fire Prevention
The other rapid-response industrial emergency.
AI vs Manual Guards
Why a person-down event cannot wait for a guard to glance back.
Deploy instant fall detection as a Pilot.
Coordinate a 30-day architecture review and live camera validation for instant fall detection in your facility.
Compatible with existing Milestone, Genetec, and standard ONVIF streams.
Request a demo
See the flow on a real operating scenario and scope a pilot around one facility or corridor.
See deployment architecture
Review camera ingest, edge inference, alert routing, and what stays on-premises.
Get the implementation checklist
Download the deployment checklist buyers use before green-lighting an industrial AI pilot.
Talk to an engineer
Bring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.