Determining anatomical frames via inertial motion capture: A survey of methods

Abstract

Despite the exponential growth in using inertial measurement units (IMUs) for biomechanical studies, future growth in "inertial motion capture" is stymied by a fundamental challenge - how to estimate the orientation of underlying bony anatomy using skin-mounted IMUs. This challenge is of paramount importance given the need to deduce the orientation of the bony anatomy to estimate joint angles. This paper systematically surveys a large number (N = 112) of studies from 2000 to 2018 that employ four broad categories of methods to address this challenge across a range of body segments and joints. We categorize these methods as: (1) Assumed Alignment methods, (2) Functional Alignment methods, (3) Model Based methods, and (4) Augmented Data methods. Assumed Alignment methods, which are simple and commonly used, require the researcher to visually align the IMU sense axes with the underlying anatomical axes. Functional Alignment methods, also commonly used, relax the need for visual alignment but require the subject to complete prescribed movements. Model Based methods further relax the need for prescribed movements but instead assume a model for the joint. Finally, Augmented Data methods shed all of the above assumptions, but require data from additional sensors. Significantly different estimates of the underlying anatomical axes arise both across and within these categories, and to a degree that renders it difficult, if not impossible, to compare results across studies. Consequently, a significant future need remains for creating and adopting a standard for defining anatomical axes via inertial motion capture to fully realize this technology's potential for biomechanical studies.

Keywords: Anatomical frame; Inertial measurement units; Inertial motion capture; Sensor-to-segment.