Simulating effects of sensor-to-segment alignment errors on IMU-based estimates of lower limb joint angles during running

Abstract

Wearable inertial measurement units offer opportunities to monitor and study running kinematics in relatively unconstrained environments. However, there remain many challenges for accurately estimating joint angles from inertial measurement unit sensor data. One important challenge involves determining the sensor-to-segment alignment parameters which specify the relative positions and orientations between the sensor and anatomical coordinate frames. Errors in these parameters can lead to errors in joint angle estimates, so it is important for practitioners, researchers, and algorithm developers to understand the required accuracy of sensor-to-segment alignment parameters for different applications. In this study, 480,000 simulations were used to investigate the effects of varying levels of simultaneous sensor-to-segment alignment errors on the accuracy of joint angle estimates from an inertial measurement unit-based method for running. The results demonstrate that accurate lower limb joint angle estimates are obtainable with this method when sensor-to-segment alignment errors are low, but these estimates rapidly degrade as errors in the relative orientations between frames grow. The results give guidance on how accurate sensor-to-segment alignment parameters must be for different applications. The methods used in this paper may also provide a valuable framework for assessing the impact of simultaneous sensor-to-segment alignment errors for other inertial measurement unit based algorithms and activities.