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Using skeletal position to estimate human error rates in telemanipulator operators.

Thomas Piercy1, Guido Herrmann1, Angelo Cangelosi1

  • 1Faculty of Science and Engineering, The University of Manchester, Manchester, United Kingdom.

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This study introduces a non-invasive method to estimate human error in telerobotics by analyzing operator biomechanics. Machine learning models predict collision rates, aiming to enhance safety through real-time feedback.

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applications in industrial activitiesbio-mechanical modellingfeedback systemspsychologysensory integration

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Biomechanics

Background:

  • Telerobotic operations carry inherent risks due to complex tasks and potential for operator error.
  • Existing methods for operator monitoring lack real-time, data-driven behavioral insights.
  • Improving safety in teleoperation requires advanced systems for predicting and mitigating human error.

Purpose of the Study:

  • To develop and trial a non-invasive system for capturing biomechanical features of teleoperators.
  • To create novel human-error rate estimators for industrial teleoperation.
  • To lay the groundwork for future systems providing operator feedback to enhance safety.

Main Methods:

  • Utilized 3D point-cloud data from depth cameras to estimate operator skeletal pose.
  • Conducted in-situ monitoring studies with 14 operators using the MASCOT teleoperation system.
  • Employed statistical and machine learning regression techniques (SVR) to estimate collision rates from captured data.

Main Results:

  • Successfully captured skeletal pose and collision statistics from 8 hours of operator data.
  • Demonstrated the feasibility of using data-driven analysis to estimate collision rates.
  • Performed sensitivity analysis on selected input features for the regression models.

Conclusions:

  • A non-invasive biomechanical feature capture method is effective for analyzing teleoperator performance.
  • Data-driven approaches, particularly SVR, show promise in estimating human error rates in teleoperation.
  • The developed system provides a foundation for future research into real-time operator feedback to improve safety.