Department of Computer Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany.
Department of Sport Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany.
PLoS One. 2019 Feb 28;14(2):e0213064. doi: 10.1371/journal.pone.0213064. eCollection 2019.
3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a promising alternative. The aim of this study was to validate IMU-based 3D joint kinematics of the lower extremities during different movements. Twenty-eight healthy subjects participated in this study. They performed bilateral squats (SQ), single-leg squats (SLS) and countermovement jumps (CMJ). The IMU kinematics was calculated using a recently-described sensor-fusion algorithm. A marker based OMC system served as a reference. Only the technical error based on algorithm performance was considered, incorporating OMC data for the calibration, initialization, and a biomechanical model. To evaluate the validity of IMU-based 3D joint kinematics, root mean squared error (RMSE), range of motion error (ROME), Bland-Altman (BA) analysis as well as the coefficient of multiple correlation (CMC) were calculated. The evaluation was twofold. First, the IMU data was compared to OMC data based on marker clusters; and, second based on skin markers attached to anatomical landmarks. The first evaluation revealed means for RMSE and ROME for all joints and tasks below 3°. The more dynamic task, CMJ, revealed error measures approximately 1° higher than the remaining tasks. Mean CMC values ranged from 0.77 to 1 over all joint angles and all tasks. The second evaluation showed an increase in the RMSE of 2.28°- 2.58° on average for all joints and tasks. Hip flexion revealed the highest average RMSE in all tasks (4.87°- 8.27°). The present study revealed a valid IMU-based approach for the measurement of 3D joint kinematics in functional movements of varying demands. The high validity of the results encourages further development and the extension of the present approach into clinical settings.
3D 关节运动学可以提供有关运动质量的重要信息。光学运动捕捉系统（OMC）被认为是运动分析的金标准。然而，近年来，惯性测量单元（IMU）已成为一种有前途的替代方法。本研究的目的是验证在不同运动中基于 IMU 的下肢 3D 关节运动学。28 名健康受试者参加了这项研究。他们进行了双侧深蹲（SQ）、单腿深蹲（SLS）和反向跳跃（CMJ）。使用最近描述的传感器融合算法计算 IMU 运动学。一个基于标记的 OMC 系统作为参考。仅考虑基于算法性能的技术误差，包括用于校准、初始化和生物力学模型的 OMC 数据。为了评估基于 IMU 的 3D 关节运动学的有效性，计算了均方根误差（RMSE）、运动范围误差（ROME）、Bland-Altman（BA）分析以及多重相关系数（CMC）。评估分为两个方面。首先，将 IMU 数据与基于标记集群的 OMC 数据进行比较；其次，基于附着在解剖学标志上的皮肤标记进行比较。第一项评估显示了所有关节和任务的 RMSE 和 ROME 的平均值低于 3°。更动态的任务，CMJ，显示的误差测量值比其余任务高约 1°。所有关节角度和所有任务的平均 CMC 值均在 0.77 到 1 之间。第二项评估显示，所有关节和任务的 RMSE 平均增加了 2.28°-2.58°。所有任务中髋关节屈曲的 RMSE 平均值最高（4.87°-8.27°）。本研究揭示了一种基于 IMU 的有效方法，用于测量不同需求的功能性运动中的 3D 关节运动学。结果的高度有效性鼓励进一步开发并将本方法扩展到临床环境中。
