Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
ISSN: 1303 - 2968   
Ios-APP Journal of Sports Science and Medicine
Androit-APP Journal of Sports Science and Medicine
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©Journal of Sports Science and Medicine (2024) 23, 515 - 525   DOI: https://doi.org/10.52082/jssm.2024.515

Research article
Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test
Takumi Ino1,2, Mina Samukawa3, , Tomoya Ishida3, Naofumi Wada4, Yuta Koshino3, Satoshi Kasahara3, Harukazu Tohyama3
Author Information
1 Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan
2 Department of Physical Therapy, Faculty of Health Sciences, Hokkaido University of Science, Sapporo, Japan
3 Faculty of Health Sciences, Hokkaido University, Sapporo, Japan
4 Department of Information and Computer Science, Faculty of Engineering, Hokkaido University of Science, Sapporo, Japan

Mina Samukawa
✉ Faculty of Health Sciences, Hokkaido University, North 12, West 5, Kita-ku, Sapporo, 060-0812, Japan
Email: mina@hs.hokudai.ac.jp
Publish Date
Received: 03-07-2023
Accepted: 14-06-2024
Published (online): 01-09-2024
 
 
ABSTRACT

OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and human visual detection-based motion analysis (Human-MA), including costly equipment, time-consuming analysis, and restricted experimental settings. This study aims to assess the precision of OpenPose-MA in comparison to Human-MA, using 3D-MA as the reference standard. The study involved a cohort of 21 young and healthy adults. OpenPose-MA employed the OpenPose algorithm, a deep learning-based open-source two-dimensional (2D) pose estimation method. Human-MA was conducted by a skilled physiotherapist. The knee valgus angle during a drop vertical jump task was computed by OpenPose-MA and Human-MA using the same frontal-plane video image, with 3D-MA serving as the reference standard. Various metrics were utilized to assess the reproducibility, accuracy and similarity of the knee valgus angle between the different methods, including the intraclass correlation coefficient (ICC) (1, 3), mean absolute error (MAE), coefficient of multiple correlation (CMC) for waveform pattern similarity, and Pearson’s correlation coefficients (OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA). Unpaired t-tests were conducted to compare MAEs and CMCs between OpenPose-MA and Human-MA. The ICCs (1,3) for OpenPose-MA, Human-MA, and 3D-MA demonstrated excellent reproducibility in the DVJ trial. No significant difference between OpenPose-MA and Human-MA was observed in terms of the MAEs (OpenPose: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) or CMCs (OpenPose: 0.83 [range: 0.99-0.53], Human: 0.87 [range: 0.24-0.98]) of knee valgus angles. The Pearson’s correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively. This study demonstrated that OpenPose-MA achieved satisfactory reproducibility, accuracy and exhibited waveform similarity comparable to 3D-MA, similar to Human-MA. Both OpenPose-MA and Human-MA showed a strong correlation with 3D-MA in terms of knee valgus angle excursion.

Key words: Artificial intelligence, Human pose estimation, landing, marker-less, human detection, 3D motion analysis


           Key Points
  • This study evaluated the accuracy of artificial intelligence-based motion analysis employed the OpenPose algorithm (OpenPose-MA) compared with human visual detection-based motion analysis (Human-MA) with reference to three-dimensional motion analysis (3D-MA).
  • No significant difference between OpenPose-MA and Human-MA was observed in terms of the mean absolute error (AI: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) of the knee valgus angles.
  • The Pearson’s correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively.
  • This study revealed that OpenPose-MA exhibited satisfactory accuracy compared with 3D-MA, similar to the conventional Human-MA.
  • Compared with conventional motion analysis, OpenPose-MA affords great advantages in terms of time-consumption and cost-saving.
 
 
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