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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Towards a Safe Human-Robot Collaboration Using Information on Human Worker Activity.

Luka Orsag1, Tomislav Stipancic1, Leon Koren1

  • 1Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, 10000 Zagreb, Croatia.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study introduces a human-skeleton-based model for recognizing worker activities, enabling safer human-robot collaboration in industrial settings. The system achieves high accuracy, allowing robots to adapt to dynamic environments.

Keywords:
LSTMactivity recognitionadaptive manufacturing systemsdeep learninghuman–robot collaborationroboticssafe HCI

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Area of Science:

  • Robotics
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Industrial robots traditionally operate in isolated environments for safety.
  • Advancements in machine learning facilitate human-robot collaboration (HRC).
  • Intuitive and adaptive human-robot interaction (HRI) is crucial for safe co-working.

Purpose of the Study:

  • To propose a computation model for intuitive and adaptive HRI.
  • To develop a system enabling robots to co-operate with human workers safely and robustly.
  • To enhance the perceptual capabilities of intelligent agents in dynamic industrial settings.

Main Methods:

  • A human-skeleton-based trainable model for spatiotemporal activity recognition.
  • Utilized Long Short-Term Memory (LSTM) networks for activity recognition.
  • Integrated vision-sensing modalities and upper body positions with actions.

Main Results:

  • Achieved a training accuracy of 91.365% for human activity recognition using the InHARD dataset.
  • Demonstrated increased perceptual potential by combining human worker positions and actions.
  • Enabled context-aware human-robot collaboration.

Conclusions:

  • The proposed model enhances intelligent agents' ability to adapt to dynamic and stochastic surroundings.
  • Facilitates safer and more efficient human-robot collaboration in industrial workplaces.
  • Lays the groundwork for future improvements in intelligent robotic systems.