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A multi-sensor dataset of human-human handover.

Alessandro Carfì1, Francesco Foglino1, Barbara Bruno1

  • 1Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, 16145 Genoa, Italy.

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Biomechanics and Motion Analysis
  • Human-Robot Interaction

Background:

  • Understanding human-human handovers is crucial for developing intuitive human-robot collaboration.
  • Existing datasets often lack the multi-modal sensor richness required for detailed kinematic and dynamic analysis.

Purpose of the Study:

  • To present a comprehensive, multi-sensor dataset of human-human handover interactions.
  • To provide a rich resource for research in human motion analysis, robotics, and human-computer interaction.

Main Methods:

  • Collected over 1000 recordings from 18 volunteers across 76 test configurations.
  • Utilized 6-axis inertial data from smartwatches, an RGB-D camera for skeleton tracking, and 20 motion capture markers.
  • Acquired upper-body models and detailed joint data for both participants.

Main Results:

  • The dataset captures diverse handover scenarios, including variations in starting positions, roles, objects, and motion strategies.
  • Multi-modal sensor data provides a detailed view of the kinematics and dynamics of human handovers.
  • Recordings are supplemented with video and questionnaire data on perceived handover characteristics.

Conclusions:

  • The dataset offers a valuable foundation for developing and evaluating robotic systems capable of naturalistic object handover.
  • Enables research into the biomechanics and perception of human-human interaction, transferable to human-robot scenarios.