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.