Dataset: Optimal Control Simulations Tracking Wearable Sensors Provides Comparable Running Gait Kinematics to Marker-Based Motion Capture

The objective of this study was to compare two IMU-based modeling approaches with optical marker-based motion capture in reconstructing running gait joint kinematics. The first was a conventional approach using inverse kinematics while the second was a novel method leveraging optimal control simulations. Six subjects performed treadmill running at three speeds whilst marker trajectories and IMU signals were collected concurrently. A subject-specific biomechanical model consisted of a 3D representation of the lower body and torso was used, incorporating contact spheres to simulate ground contact in in the optimal control simulations. The primary objective of the optimal control simulations was tracking accelerations, angular velocities, and orientations of 8 sensors with the simulated signals from the model sensors. Additional constraints, reflecting physiological and biomechanical principles and acheiving dynamic consistency were enforced. The objective of the IMU-based inverse kinematics was to minimize the difference between input and simulated sensor orientations. The joint kinematics derived from both methods were evaluated against optical marker-based motion capture across a range of running speeds.

本研究旨在对比两种基于惯性测量单元（Inertial Measurement Unit, IMU）的建模方法与基于光学标记点的动作捕捉技术在重建跑步步态关节运动学数据中的表现。第一种为采用逆运动学的传统方法，第二种则是借助最优控制仿真的新型方法。6名受试者在三种不同速度下开展跑步机跑步实验，同时同步采集标记点轨迹与IMU信号。本研究采用受试者特异性生物力学模型，该模型包含下半身与躯干的三维表征，并在最优控制仿真中引入接触球体以模拟地面接触。最优控制仿真的核心目标是使模型传感器生成的仿真信号匹配8个传感器的加速度、角速度与姿态数据。此外，研究还施加了反映生理学与生物力学原理、可实现动力学一致性的额外约束条件。基于IMU的逆运动学方法的目标则是最小化输入传感器姿态与仿真传感器姿态之间的差值。两种方法得到的关节运动学结果，将在不同跑步速度范围内与基于光学标记点的动作捕捉结果进行对比评估。