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Fall Detection

Automated vision-based fall detection system to recognize human falls.

What is Fall Detection?

Human fall detection systems use artificial intelligence technology to detect a person’s fall using real-time video feeds of a camera. Human falls are an important health problem worldwide and a major cause of death for older people, with over 30% of falls causing severe injuries. Therefore, such fall detection systems are becoming increasingly important in today’s aging population.

Vision-based fall detection systems examine human movement and perform activity recognition to classify fall events detected in video streams.

Key Features of Detecting Human Fall

Deep learning algorithms use neural networks to detect human falls. Edge AI makes it possible to guarantee privacy by processing visual data locally.

  • Real-time fall detection of human falls without any human interaction by the subject.
  • Contactless setup without the need to use invasive wearables that can be forgotten or impact the person
  • Easier to install compared to physical sensors while being able to obtain more rich information.
  • Privacy-preserving Edge AI methods to compute all visuals on-device and in real-time. No visuals leave the edge device that is connected to the camera at any time.

Why is Fall Detection valuable?

Recognizing human fall automatically is of high importance to increase the safety of the elderly.

  1. High impact on life quality for the dependent community.
  2. Increased safety by detecting human falls and sending an alert.
  3. Vision-based fall detection is very flexible and highly scalable.
  4. Save costs by optimizing manual inspections.
Industries
Last updated
September 20, 2021
Usage
Requires Viso Suite
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How to get started

This solution is built on Viso Suite, the all-in-one computer vision platform to deploy, run and scale the solution with powerful tools and infrastructure.

To get started, request a personal demo, we will put together a plan that includes all the services you need to get started.

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