Department of Mechanical Systems Engineering, Tokyo Metropolitan University, Tokyo 191-0065, Japan.
Sensors (Basel). 2024 Apr 24;24(9):2706. doi: 10.3390/s24092706.
Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis.
旋转跳跃是体育竞赛中的关键技术。通过基于生物力学模型的方法来估算地面反作用力（GRF），即使在无法使用力板或机器学习训练数据的环境中，也可以进行分析。在这项研究中，使用惯性测量单元（IMU）测量了涉及陆地扭转的旋转跳跃动作，并使用 3D 正向动力学模型估算了 GRF 和身体负荷。我们的正向动力学和优化计算估算方法使用由运动测量和内部身体负荷定义的成本函数生成和优化身体运动。为了减少优化计算中动态加速度的影响，我们使用包含来自 IMU 的加速度和角速度数据以及扩展卡尔曼滤波器的传感器融合来估计 3D 方向。因此，通过生成基于成本函数的运动，我们可以在遵循测量运动的情况下计算出符合生物力学的 GRF，即使并非所有关节都被 IMU 覆盖。本研究中开发的估计方法允许进行不受测量条件或训练数据影响的 3D 运动分析。
