Thiele Federico, Paternoster Florian, Hummel Chris, Stöcker Fabian, Holzer Denis
Department of Sport and Health Sciences, Technical University of Munich, Munich, BY, GERMANY.
Applied Sports Science, Department Health and Sports Sciences, Technical University of Munich, Munich, BY, GERMANY.
Int J Exerc Sci. 2024 Dec 1;17(1):1629-1647. doi: 10.70252/PRVV4165. eCollection 2024.
In weightlifting, quantitative kinematic analysis is essential for evaluating snatch performance. While marker-based (MB) approaches are commonly used, they are impractical for training or competitions. Markerless video-based (VB) systems utilizing deep learning-based pose estimation algorithms could address this issue. This study assessed the comparability and applicability of VB systems in obtaining snatch kinematics by comparing the outcomes to an MB reference system. 21 weightlifters (15 Male, 6 Female) performed 2-3 snatches at 65%, 75%, and 80% of their one-repetition maximum. Snatch kinematics were analyzed using an MB (Vicon Nexus) and VB (Contemplas along with Theia3D) system. Analysis of 131 trials revealed that corresponding lower limb joint center positions of the systems on average differed by 4.7 ± 1.2 cm, and upper limb joint centers by 5.7 ± 1.5 cm. VB and MB lower limb joint angles showed highest agreement in the frontal plane (root mean square difference (RMSD): 11.2 ± 5.9°), followed by the sagittal plane (RMSD: 13.6 ± 4.7°). Statistical Parametric Mapping analysis revealed significant differences throughout most of the movement for all degrees of freedom. Maximum extension angles and velocities during the second pull displayed significant differences (p < .05) for the lower limbs. Our data showed significant differences in estimated kinematics between both systems, indicating a lack of comparability. These differences are likely due to differing models and assumptions, rather than measurement accuracy. However, given the rapid advancements of neural network-based approaches, it holds promise to become a suitable alternative to MB systems in weightlifting analysis.
在举重运动中，定量运动学分析对于评估抓举表现至关重要。虽然基于标记（MB）的方法被广泛使用，但它们在训练或比赛中并不实用。利用基于深度学习的姿态估计算法的无标记视频（VB）系统可以解决这个问题。本研究通过将结果与MB参考系统进行比较，评估了VB系统在获取抓举运动学方面的可比性和适用性。21名举重运动员（15名男性，6名女性）以其一次重复最大值的65%、75%和80%进行了2 - 3次抓举。使用MB（Vicon Nexus）和VB（Contemplas以及Theia3D）系统对抓举运动学进行分析。对131次试验的分析表明，两个系统相应的下肢关节中心位置平均相差4.7±1.2厘米，上肢关节中心相差5.7±1.5厘米。VB和MB下肢关节角度在额平面上的一致性最高（均方根差（RMSD）：11.2±5.9°），其次是矢状平面（RMSD：13.6±4.7°）。统计参数映射分析显示，在所有自由度的大部分运动过程中都存在显著差异。第二次提拉过程中的最大伸展角度和速度在下肢显示出显著差异（p < 0.05）。我们的数据表明两个系统在估计的运动学方面存在显著差异，这表明缺乏可比性。这些差异可能是由于模型和假设不同，而不是测量精度问题。然而，鉴于基于神经网络的方法的快速发展，它有望成为举重分析中MB系统的合适替代方案。
