A Data-based Approach to Simultaneously Align Local and Global Frames between an Inertial Measurement Unit (IMU) and an Optical Motion Capture System

Motion tracking algorithms using inertial measurement units (IMU) are commonly evaluated against ground truth measurements from optical motion capture systems (OMC). A fair comparison between IMU and OMC requires accurate frame alignment between the two systems. Existing methods address the local and global frame misalignments as separate issues, either relying on various assumptions or precise calibration measurements. In this work, we propose an assumption-free data-based method that simultaneously aligns both local and global frames via quaternion-based least squares optimization. For performance evaluation, we compared the proposed method with methods based on commonly accepted assumptions. Using human kinematics data from 6 participants, the proposed method produced the best alignment results with error less than 1.5° and its error profile was the least correlated with motion. We then conducted sensitivity analysis on input data characteristics and showed the necessity of using data with sufficient range of motion to ensure alignment accuracy. Lastly, we demonstrated that the proposed method could be used for isolating IMU drift during dynamic movements. The proposed alignment method could serve as a valuable tool for developing and evaluating IMU-based motion tracking algorithms.