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Blackbook.ai

Fall Detection

The client engaged Blackbook.ai to improve accuracy and speed of their fall detection device.

Industry

Aged Care & Disability

Technology stack

Computer Vision

Edge Computing

Industry

Aged Care & Disability

Technology stack

Computer Vision

Edge Computing

Industry

Aged Care & Disability

Technology stack

Computer Vision

Edge Computing

The challenge

The client had a device designed to alert medical services after detecting falls from elderly users. However, the existing model used by the client was slow, and the sound and computer vision detection were not performing as quickly or accurately as needed. Updates to the model were needed to ensure the device was performing quickly and accurately.

The solution

Blackbook was engaged to verify the accuracy of the device and rebuild models and bring the models up to the latest technology to to deploy.  The solution focused on building new Edge AI model architecture, code optimisations and data cleaning and collection. The new model architecture and data optimisations allowed for more up-to-date technology to be applied with improved accuracy and remove unnecessary code from the looping statement to improve the speed.

The outcomes

The improvement made by Blackbook improve accuracy and speed significantly. The process of detecting falls from the initial sound of the fall to the images captured and sent to alert desk for verification is much faster than the previous model. This allowed the client to improve their devices and ensure the quick safety response assured to users of their device.

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