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A Framework for Real-Time Gestural Recognition and Augmented Reality for Industrial Applications.

Winnie Torres1, Lilian Santos2, Gustavo Melo1

  • 1Electrical Engineering Department, Center of Technology, Federal University of Rio Grande do Norte-UFRN, Natal 59072-970, Brazil.

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This study introduces a web-based system integrating augmented reality (AR) and gesture recognition for intuitive industrial equipment control. This approach enhances safety and efficiency in industrial operations through natural hand movements.

Keywords:
A-Frameaugmented realitygesture recognitionweb application

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Area of Science:

  • Human-Computer Interaction
  • Industrial Automation
  • Computer Vision

Background:

  • Technological advancements are transforming industries, emphasizing automation and safety.
  • Augmented Reality (AR) and gesture recognition offer innovative interactive environments for industrial equipment.
  • Intuitive interaction is key for enhancing user experience with complex machinery.

Purpose of the Study:

  • To present a web-based architecture integrating AR and gesture recognition for industrial equipment interaction.
  • To develop a hardware-agnostic system enabling natural gesture control of industrial equipment.
  • To validate the practical viability of AR and gesture recognition in industrial settings.

Main Methods:

  • Developed a web-based architecture for AR and gesture recognition integration.
  • Implemented software optimization techniques including normalization, clamping, conversion, and filtering.
  • Conducted experimental validation using Google Glass for real-world industrial operations.

Main Results:

  • Demonstrated the practical viability of the proposed AR and gesture recognition system.
  • Achieved accurate and reliable gesture recognition under various usage conditions.
  • Validated the potential for safer and more efficient industrial operations.

Conclusions:

  • The integrated AR and gesture recognition system offers an intuitive and efficient method for industrial equipment control.
  • The hardware-agnostic approach enhances accessibility and applicability across different industrial setups.
  • Further research will focus on improving gesture recognition accuracy and expanding platform integration.