Fall Detection

How It Works

A fall detection pipeline works in four stages:

1. Detect people in each frame with a neural network.
2. Estimate pose — locate key body joints (head, shoulders, hips, knees).
3. Analyze transition — track whether a person went from upright to horizontal in a short time window.
4. Confirm — check that the person remains prone for a configurable duration (to filter normal lying down).

Multi-frame analysis is critical; a single frame of someone tying their shoe should not trigger an alarm.

Why It Matters

In elder care, hospitals, and industrial sites, fast response to falls saves lives:

• A fallen worker in an industrial setting may be unable to call for help.
• Elderly patients in hospitals have higher mortality when response time exceeds one hour.
• Construction sites have legal obligations to detect and report incidents.

IncoreSoft's Pose Estimation / Fall Detection module detects falls in real time across cameras, with configurable thresholds for prone duration and scene zones — critical for schools and campuses, hospitals, and factories.

Use Cases

• Hospitals and elder care — patient fall alerts
• Construction and industrial — worker-down detection at height or on floors
• Warehouses — forklift-struck or tripped worker alerts
• Public restrooms and changing rooms — pose-only analytics preserving privacy
• Correctional facilities — inmate medical emergencies

Frequently Asked Questions

How fast does fall detection trigger an alert?

Modern systems alert in 3–10 seconds depending on the prone-duration threshold. Shorter thresholds catch events faster but can false-alarm on intentional lying down.

Does fall detection work in low light?

Yes, when cameras have IR illumination or thermal imaging. Standard daytime cameras lose accuracy after dark. Edge cases (partial occlusion, multiple overlapping people) reduce accuracy and may need multiple camera angles.

Is fall detection privacy-friendly?

Yes, when configured to use pose-only (skeleton) data without face recognition. This makes it appropriate for privacy-sensitive environments like changing rooms and medical facilities, where a face recognition approach would be unsuitable.