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Related Experiment Videos

Embedded Systems and TensorFlow Frameworks as Assistive Technology Solutions.

Davide Mulfari1, Alessandro Palla1, Luca Fanucci1

  • 1Dept. of Information Engineering, University of Pisa, Italy.

Studies in Health Technology and Informatics
|September 7, 2017
PubMed
Summary

This study introduces a wearable computer vision system using deep learning for visually impaired individuals. The assistive technology enhances environmental awareness through real-time object recognition and audio feedback.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Visually impaired individuals face challenges in navigating and understanding their surroundings.
  • Existing assistive technologies may lack real-time environmental perception capabilities.

Purpose of the Study:

  • To design and develop a wearable computer vision system for the visually impaired.
  • To leverage deep learning for real-time object recognition in assistive technology.

Main Methods:

  • Utilized a single board computer and smart glasses with a camera.
  • Employed the Google TensorFlow machine learning framework for image classification.
  • Integrated audio feedback for user interaction.

Main Results:

Keywords:
InceptionTensor Flow Softwareassistive technologycomputer visionneural networkobject recognitiontext-to-speech

Related Experiment Videos

  • The system enables users to explore and identify objects in their environment.
  • Real-time classification of acquired images was achieved.
  • Enhanced user awareness of the surrounding environment was demonstrated.

Conclusions:

  • The proposed wearable computer vision system effectively aids visually impaired users.
  • Deep learning frameworks like TensorFlow are crucial for developing advanced assistive technologies.
  • The system offers a promising solution for improving environmental interaction for the visually impaired.