Estimation of Vertical Ground Reaction Force during Single-leg Landing Using Two-dimensional Video Images and Pose Estimation Artificial Intelligence
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- ISHIDA Tomoya
- Faculty of Health Sciences, Hokkaido University, Japan
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- INO Takumi
- Faculty of Health Sciences, Hokkaido University of Science, Japan
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- YAMAKAWA Yoshiki
- Faculty of Health Sciences, Hokkaido University, Japan
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- WADA Naofumi
- Faculty of Engineering, Hokkaido University of Science, Japan
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- KOSHINO Yuta
- Faculty of Health Sciences, Hokkaido University, Japan
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- SAMUKAWA Mina
- Faculty of Health Sciences, Hokkaido University, Japan
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- KASAHARA Satoshi
- Faculty of Health Sciences, Hokkaido University, Japan
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- TOHYAMA Harukazu
- Faculty of Health Sciences, Hokkaido University, Japan
抄録
<p>Objective: Assessment of the vertical ground reaction force (VGRF) during landing tasks is crucial for physical therapy in sports. The purpose of this study was to determine whether the VGRF during a single-leg landing can be estimated from a two-dimensional (2D) video image and pose estimation artificial intelligence (AI). Methods: Eighteen healthy male participants (age: 23.0 ± 1.6 years) performed a single-leg landing task from a 30-cm height. The VGRF was measured using a force plate and estimated using center of mass (COM) position data from a 2D video image with pose estimation AI (2D-AI) and three-dimensional optical motion capture (3D-Mocap). The measured and estimated peak VGRFs were compared using a paired t-test and Pearson’s correlation coefficient. The absolute errors of the peak VGRF were also compared between the two estimations. Results: No significant difference in the peak VGRF was found between the force plate measured VGRF and the 2D-AI or 3D-Mocap estimated VGRF (force plate: 3.37 ± 0.42 body weight [BW], 2D-AI: 3.32 ± 0.42 BW, 3D-Mocap: 3.50 ± 0.42 BW). There was no significant difference in the absolute error of the peak VGRF between the 2D-AI and 3D-Mocap estimations (2D-AI: 0.20 ± 0.16 BW, 3D-Mocap: 0.13 ± 0.09 BW, P = 0.163). The measured peak VGRF was significantly correlated with the estimated peak by 2D-AI (R = 0.835, P <0.001). Conclusion: The results of this study indicate that peak VGRF estimation using 2D video images and pose estimation AI is useful for the clinical assessment of single-leg landing.</p>
収録刊行物
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- Physical Therapy Research
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Physical Therapy Research 27 (1), 35-41, 2024-04-20
一般社団法人日本理学療法学会連合
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詳細情報 詳細情報について
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- CRID
- 1390862853908342016
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- ISSN
- 21898448
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可