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Functional movement screen dataset collected with two Azure Kinect depth sensors.

Qing-Jun Xing1, Yuan-Yuan Shen2, Run Cao3

  • 1Beijing Sport University, School of Sport Science, Beijing, 100084, China.

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|March 26, 2022
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Summary

This study introduces a new dataset for automated Functional Movement Screen (FMS) analysis using computer vision. The multimodal, multiview data enables objective evaluation of movement quality.

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Area of Science:

  • Biomechanics
  • Computer Vision
  • Human Movement Analysis

Background:

  • The Functional Movement Screen (FMS) is a key tool for assessing movement quality and identifying asymmetries.
  • Objective, automated assessment of FMS performance is challenging due to the complexity of human motion.
  • Existing datasets may lack the multimodal and multiview data necessary for robust automated analysis.

Purpose of the Study:

  • To present a comprehensive, multimodal, and multiview dataset for vision-based autonomous Functional Movement Screen (FMS) analysis.
  • To facilitate the development and validation of algorithms for automatic action quality evaluation of FMS movements.
  • To provide a rich resource for research in human motion analysis, biomechanics, and computer vision.

Main Methods:

  • Collected multimodal data (color, depth, quaternions, 3D skeleton, 2D trajectories) from 45 subjects performing 7 FMS movements.
  • Utilized two synchronized Azure Kinect sensors for multiview data acquisition.
  • Annotated movement quality (0-3) by three FMS experts for 1812 recordings (3624 episodes).

Main Results:

  • A large-scale dataset (190 GB) comprising diverse human movement data.
  • Multimodal and multiview data capture provides rich information for detailed movement analysis.
  • Expert annotations offer ground truth for training and evaluating automated systems.

Conclusions:

  • The presented dataset is a valuable resource for advancing automated FMS assessment.
  • Enables research into computer vision and machine learning techniques for objective movement quality evaluation.
  • Has the potential to improve injury prevention and performance optimization through automated movement screening.