A Comprehensive Open-Source Toolbox for Analyzing and Improving Orientation Estimation From Inertial Sensors for Biomechanical Applications

Drift remains a significant challenge in using inertial measurement units (IMUs) for human movement analysis. Drift reduction in orientation estimation is of particular interest, since inaccuracies in these estimates will negatively affect estimated linear kinematics (e.g., position). To address this, we developed an open-source toolbox designed to investigate the effects of signal characteristics, sampling frequencies, and integration orders on orientation estimation accuracy. The toolbox uses Taylor series approximations to estimate the change in orientation from angular velocity, and contains two pipelines: A reference-based pipeline that compares the estimated orientation against a known ground truth orientation, and a reference-free pipeline that does not rely on a ground truth orientation. We demonstrate the toolbox’s capabilities with three case studies. These case studies are also used to investigate effects of signal characteristics, sampling frequency and integration order on orientation estimation accuracy. Results show improved orientation estimation for slower movements, higher sampling frequencies, and higher integration orders. Additionally, through the case studies, we highlight how the toolbox can guide decisions on sampling frequencies and data processing strategies for specific application scenarios.