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USE CASE

AI-Powered human fall detection for real-time video surveillance

In high-risk environments such as elderly care facilities, hospitals, public transit stations, or workplace safety zones, detecting human falls instantly is essential for preventing serious injuries and enabling rapid emergency response. However, continuous manual video surveillance monitoring is resource-intensive and prone to oversight.

A growing need exists for automated fall detection systems that operate in real-time, are accurate, and respect user privacy. Such a system would assist caregivers, security teams, or emergency responders by automatically identifying falls and triggering alerts, without relying on wearable sensors.

With advancements in AI pose estimation systems, a computer vision–driven solution could be deployed directly on-site to provide privacy-compliant, accurate, and low-latency fall detection.

Conceptual Design

How do we detect human falls automatically?

Our fall detection application uses a deep learning model to estimate body key points and track motion trajectories frame-by-frame. By analizing changes in body angles, joint angles and movement velocity, the system can accurately detect genuine falls while filtering out benign movements (e.g., sitting or bending). This fall detection solution is designed for seamless integration and works with standard CCTV and IP camera streams using a GStreamer pipeline.

OUR VALUE PROPOSITION

The value we deliver: How we boost your business

value-1

Risk reduction through immediate incident response

Enables immediate response to incidents, reducing injury severity and improving care quality.

value-2

Plug-and-Play integration with configurability

Easy plug-and-play integration with existing systems, combined with configurability, enabling quick deployment and customization to meet diverse environment requirements.

value-3

Privacy-Conscious deployment: Local processing and GDPR compliance

Processes data locally, without needing personal identity recognition—ideal for GDPR-sensitive environments.