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Video-based quantification of human movement frequency using pose estimation: A pilot study.

Hannah L Cornman1,2,3, Jan Stenum1,2, Ryan T Roemmich1,2

  • 1Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD, United States of America.

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|December 20, 2021
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Summary
This summary is machine-generated.

Computer vision accurately measures repetitive movement frequency from smartphone videos. This pose estimation approach offers a quantitative, remote alternative to traditional visual inspection for motor assessments.

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Neuroscience

Background:

  • Repetitive movement assessment is crucial for neurologic evaluations.
  • Traditional visual inspection by human raters is subjective and time-consuming.
  • Computer vision offers potential for objective, remote motor assessment.

Purpose of the Study:

  • To evaluate a pose estimation workflow for measuring human movement frequency from smartphone videos.
  • To compare pose estimation-based frequency measurements against manual, frame-by-frame analysis.
  • To assess the accuracy and reliability of this technology across various movement tasks and frequencies.

Main Methods:

  • Utilized OpenPose, a whole-body pose estimation algorithm, on smartphone videos.
  • Recorded ten healthy participants performing five repetitive motor tasks (finger tapping, hand open/close, etc.) at 1-4 Hz.
  • Compared pose estimation results with manual frame-by-frame measurements using ANOVA, correlation, and intraclass coefficients.

Main Results:

  • The pose estimation workflow demonstrated largely accurate frequency estimations across tasks.
  • No significant differences were found between pose estimation and manual measurements for most tasks.
  • Pose estimation showed strong correlations (r>0.99) with manual event detections.

Conclusions:

  • A pose estimation workflow can accurately quantify repetitive movements from smartphone videos.
  • This technology provides a fast, quantitative, and remote method for motor assessment.
  • Future research should explore its application in clinical populations for neurologic assessments.