Nam Yunhyoung, Do Youngkyung, Kim Jaehoon, Lee Heonyong, Kim Do-Nyun
Department of Mechanical Engineering, Seoul National University, Seoul, Korea.
Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea.
Eur J Sport Sci. 2023 Feb;23(2):221-230. doi: 10.1080/17461391.2022.2028013. Epub 2022 Feb 14.
The aim of this paper is to propose a hybrid framework that combines a data-driven pose estimation with model-based force calculation in order to predict the ski jumping force from a recorded motion video. A skeletal model consisting of five joints (ear, hip, knee, ankle, and toe) and four rigid segments (head/arm/trunk or HAT, thigh, shank, and foot) connecting each joint is developed. The joint forces are calculated from the dynamic equilibrium equations, which requires the time history of joint coordinates. They are estimated from a recorded motion video using a deep neural network for pose estimation trained with human motion data. Joint coordinates can be obtained by the proposed deep neural network directly from images of jumping motion without using any markers. The validity and usefulness of the proposed method are confirmed in lab experiments. Further, our method is practically applicable to the study in a real competition environment because it is not required to attach any sensor or marker to athletes. A method to predict the ski jumping force from a recorded motion video is proposed.It combines a data-driven pose estimation with a model-based force calculation.The proposed method does not require any markers and sensors to be attached to athletes.In a laboratory environment, the relative error in the maximum jumping force is less than 7%.The method can be easily applied to a field study in a real competition environment.
本文的目的是提出一种混合框架，该框架将数据驱动的姿势估计与基于模型的力计算相结合，以便从录制的运动视频中预测跳台滑雪的力。开发了一个由五个关节（耳朵、臀部、膝盖、脚踝和脚趾）和连接每个关节的四个刚性部分（头部/手臂/躯干或HAT、大腿、小腿和脚）组成的骨骼模型。关节力由动态平衡方程计算得出，这需要关节坐标的时间历程。它们是通过使用经过人体运动数据训练的用于姿势估计的深度神经网络从录制的运动视频中估计出来的。关节坐标可以通过所提出的深度神经网络直接从跳跃运动的图像中获得，而无需使用任何标记。所提出方法的有效性和实用性在实验室实验中得到了证实。此外，我们的方法在实际比赛环境中也切实适用于该研究，因为它不需要在运动员身上附着任何传感器或标记。提出了一种从录制的运动视频中预测跳台滑雪力的方法。它将数据驱动的姿势估计与基于模型的力计算相结合。所提出的方法不需要在运动员身上附着任何标记和传感器。在实验室环境中，最大跳跃力的相对误差小于7%。该方法可以很容易地应用于实际比赛环境中的现场研究。
