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Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

To compare the accuracy of the Microsoft Kinect V2 with novel pose estimation frameworks, in assessing knee kinematics during athletic stress tests, for fast and portable risk assessment of anterior cruciate ligament (ACL) injury.

Methods

We captured 254 varsity athletes, using the Kinect V2 and a smartphone application utilizing Google’s MediaPipe framework. The devices were placed as close as possible and used to capture a person, facing the cameras, performing one of three athletic stress tests at a distance of 2.5 ms. Custom software translated the results from both frameworks to the same format. We then extracted relevant knee angles at key moments of the jump and compared them, using the Kinect V2 as the ground truth.

Results

The results show relatively small angle differences between the two solutions in the coronal plane and moderate angle differences on the sagittal plane. Overall, the MediaPipe framework results seem to underestimate both knee valgus angles and knee sagittal angles compared to the Kinect V2.

Conclusion

This preliminary study demonstrates the potential for Google’s MediaPipe framework to be used for calculating lower limb kinematics during athletic stress test motions, which can run on most modern smartphones, as it produces similar results to the Kinect V2. A smartphone application similar to the one developed could potentially be used for low cost and widespread ACL injury prevention.

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Acknowledgements

The senior author would like to acknowledge the support of MEDTEQ, Emovi and Consultation Semperform Inc. to this research endeavor.

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Authors and Affiliations

  1. Experimental Surgery, McGill University, Montréal, QC, H3A 0G4, Canada

    Athanasios Babouras, Patrik Abdelnour, Thomas Fevens & Paul A. Martineau

  2. Computer Science and Software Engineering, Concordia University, Montréal, QC, H3G 1M8, Canada

    Thomas Fevens

Authors
  1. Athanasios Babouras
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  2. Patrik Abdelnour
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  3. Thomas Fevens
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  4. Paul A. Martineau
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Corresponding author

Correspondence to Athanasios Babouras.

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The authors declare that they have no Conflict of interest.

Ethical approval

The questionnaire and methodology for this study was approved by the Research Ethics Office of the Faculty of Medicine and Health Sciences of McGill University.

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Informed consent was obtained from all individual participants included in the study.

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Appendix A Initial experiments

Appendix A Initial experiments

Tables 4 and 5 contain the results from our initial experiments, comparing the Kinect V2 coronal angles with both the MediaPipe framework and the proprietary iOS body tracking framework. We used an iPhone 13 Pro for the iOS solution, utilizing ARKit version 5. A single subject performed 30 DVJ in front of all three devices, and the results were analyzed in the same way.

Table 4 Mean angle for Kinect V2, MediaPipe and ARKit
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Table 5 Root mean squared deviation
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Babouras, A., Abdelnour, P., Fevens, T. et al. Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03156-5

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