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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress

Athanasios Babouras1, Patrik Abdelnour2, Thomas Fevens2,3

  • 1Experimental Surgery, McGill University, Montréal, QC, H3A 0G4, Canada. athanasios.babouras@mail.mcgill.ca.

International Journal of Computer Assisted Radiology and Surgery
|May 10, 2024
PubMed
Summary

Google's MediaPipe framework shows promise for assessing knee kinematics during athletic tests, offering a portable and cost-effective method for anterior cruciate ligament (ACL) injury risk assessment.

Keywords:
Computer visionKnee kinematicsPose estimationSports medicine

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Area of Science:

  • Biomechanics
  • Sports Medicine
  • Computer Vision

Background:

  • Anterior cruciate ligament (ACL) injuries are common in athletes.
  • Accurate assessment of knee kinematics is crucial for injury prevention.
  • Current methods for kinematic analysis can be cumbersome and expensive.

Purpose of the Study:

  • To evaluate the accuracy of Google's MediaPipe framework for knee kinematics.
  • To compare MediaPipe's performance against the Microsoft Kinect V2.
  • To explore the potential for smartphone-based ACL injury risk assessment.

Main Methods:

  • 254 varsity athletes were analyzed using Kinect V2 and a smartphone app with MediaPipe.
  • Athletes performed three athletic stress tests at a 2.5m distance.
  • Knee angles were extracted and compared between the two systems, with Kinect V2 as ground truth.

Main Results:

  • Small differences in knee angles were observed in the coronal plane.
  • Moderate differences were found in the sagittal plane.
  • MediaPipe tended to underestimate knee valgus and sagittal angles compared to Kinect V2.

Conclusions:

  • Google's MediaPipe framework demonstrates potential for lower limb kinematics analysis.
  • Smartphone-based applications using MediaPipe could enable widespread, low-cost ACL injury prevention.
  • Further validation is needed, but results suggest a viable alternative to traditional motion capture.