Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

Abstract

Inertial measurement units (IMUs) have become the mainstay in motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; the second was to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion–extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction–retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapular locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.

Assessing the Validity of the Ergotex IMU in Joint Angle Measurement: A Comparative Study with Optical Tracking Systems

“…Most studies have analyzed the validity and/or reliability of different IMUs for measuring joint motion during several movements and/or activities [ 2 , 7 , 26 , 27 ]. However, there is a broad consensus that the more complex the joint motion analyzed (e.g., involving more anatomical planes, higher intensities/speeds/accelerations during movement, etc.…”

Intraoperative Angle Measurement of Anatomical Structures: A Systematic Review

“…Inertial sensors require frequent calibration for accurate measurements [ 61 , 126 ], and in the case those calibrations are lost, the measuring process, and consequently the surgical procedure, may be compromised. Even when calibrated, orientation estimation requires some processing of the sensor data, usually in the form of a sensor fusion algorithm, to obtain useful measurements as noise, bias, and drift (mainly from the gyroscope) are well-reported issues in the literature [ 127 , 128 ]. Furthermore, inertial-based sensors typically present good accuracy in angle measurements.…”

Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation

“…We advise the use of FC when the focus of the measurement is on achieving the most accurate elbow flexion angle. • MA requires accurate sensor positioning and alignment of the sensors on the wearer’s body segments ( Fang et al, 2023 ), requiring extensive training of the operator. MA displays good accuracy and low errors across a multitude of joint angle axes and during both single-plane and multi-plane tasks.…”

“…To accurately identify the two main elbow rotation axes, scientific literature presents several techniques that are most commonly adopted to perform IMU calibration, which are more extensively described in ( Fang et al, 2023 ). In short, these are 1) N-Pose calibration (NP), which involves holding a known pose to align each sensor reference frame to the reference of the bone underneath ( Zhang and Wu, 2011 ; Liu et al, 2019 ; Humadi et al, 2021 ); 2) Functional calibration (FC), which consists of performing single-plane elbow flexion-extension and pronation-supination movements to estimate the relative joint rotation axis ( Cutti et al, 2008 ; Ligorio et al, 2017 ); 3) Manual alignment (MA) calibration, where each sensor is accurately positioned on the body segment to assume a perfect match between the sensor reference frame and the bone-embedded reference frame ( Bouvier et al, 2015 ; Höglund et al, 2021 ).…”