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Related Experiment Video

Updated: Sep 14, 2025

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
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Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

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Multimodal cross-system virtual reality (VR) ball throwing dataset for VR biometrics.

Mingjun Li1,2, Natasha Kholgade Banerjee3, Sean Banerjee3

  • 1Department of Computing Sciences, University of Hartford, 200 Bloomfield Avenue, West Hartford, CT 06117, USA.

Data in Brief
|July 21, 2025
PubMed
Summary

This study introduces a unique multimodal dataset for virtual reality (VR) biometrics, capturing synchronized data from multiple VR systems and external video. This resource facilitates advanced research in cross-system VR biometrics and motion analysis.

Keywords:
AuthenticationDeep learningExtended realityIdentificationMultimodal dataSecurity

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Area of Science:

  • Biometrics
  • Virtual Reality (VR)
  • Human-Computer Interaction (HCI)

Background:

  • Virtual Reality (VR) systems offer immersive experiences but present challenges in consistent biometric data collection across different hardware.
  • Existing VR biometric datasets often lack multi-system synchronization and external user-view data, limiting comprehensive analysis.
  • Understanding user movement patterns in VR is crucial for security, personalization, and performance optimization.

Purpose of the Study:

  • To introduce a novel multimodal, cross-system dataset for virtual reality (VR) biometrics.
  • To provide synchronized data from multiple VR systems (Meta Quest, HTC Vive, HTC Vive Cosmos) and external video capture.
  • To enable research into cross-system VR biometrics, body keypoint analysis, and motion trajectory prediction.

Main Methods:

  • Collected data from 41 participants performing a ball-throwing task across multiple VR systems.
  • Utilized Meta Quest, HTC Vive, and HTC Vive Cosmos, capturing headset and controller data (position, orientation).
  • Integrated external video (GoPro Hero 7) to capture full-body movements, generating COCO body keypoints (OpenPose/MMPose) and synchronized with VR data.

Main Results:

  • The dataset is the first known to combine multi-VR system data with external user video for biometrics.
  • Includes synchronized VR system data (NumPy), external video (MP4), and body keypoints (JSON).
  • Provides comprehensive participant demographics and temporal data for advanced analysis.

Conclusions:

  • The dataset supports research in cross-system VR biometrics, leveraging both internal VR tracking and external body keypoints.
  • Enables the study of demographic impacts on VR biometrics and short-term movement evolution.
  • Facilitates the development of 2D-to-3D and 3D-to-2D motion trajectory prediction models.