Department of Biomedical Engineering, The University of Melbourne, Melbourne 3052, Australia.
Department of Mechanical Engineering, The University of Melbourne, Melbourne 3052, Australia.
Sensors (Basel). 2023 Jul 19;23(14):6535. doi: 10.3390/s23146535.
Inertial measurement units (IMUs) have become the mainstay in human 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; and secondly, 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 joint motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapula 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.
惯性测量单元 (IMU) 已成为实验室外人体运动评估的主要手段；然而，使用 IMU 量化三维上肢运动仍然具有挑战性。本系统评价的目的有二。首先，评估将 IMU 数据转换为上肢关节角度的计算方法，包括肩胛骨-胸壁、肱骨-胸廓、盂肱和肘关节；其次，量化这些方法与光电运动分析相比的准确性。共纳入 52 项研究。使用 Euler 角分解和基于卡尔曼滤波器的方法可实现最大关节运动测量精度。这导致 IMU 和光电运动分析在肱骨运动所有自由度上的差异为 4°。通过功能关节轴校准任务和对陀螺仪数据使用运动学约束，在肘关节上实现了更高的精度，使得 IMU 和光电运动分析之间的屈伸运动 RMS 误差低至 2°。对于盂肱关节，使用 RMS 误差为 6°及更高的方法来描述三维关节运动。相比之下，肩胛骨-胸壁关节运动跟踪在前伸-回缩和前-后倾斜方向上产生超过 10°的 RMS 误差。本研究的结果表明，使用 IMU 可以实现高质量的三维肱骨和肘关节运动测量能力，并强调了肩胛骨和盂肱关节运动分析中皮肤运动伪影的挑战。未来的研究应该实施功能关节轴校准和基于 IMU 的肩胛骨定位器，以解决肩胛骨上的皮肤运动伪影问题，并探索使用人工神经网络和数据驱动方法直接将 IMU 数据转换为关节角度。
