School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Department of Automation, University of Science and Technology of China, Hefei, China.
J Biomech. 2021 Jul 19;124:110549. doi: 10.1016/j.jbiomech.2021.110549. Epub 2021 Jun 13.
Anterior cruciate ligament (ACL) injury is a common and severe knee injury in sports. Knee flexion, abduction and internal rotation angles are considered crucial biomechanical indicators of the ACL injury risk but currently are computed in a laboratory with an optical motion capture. This paper introduces an inertial measurement unit (IMU) based algorithm for knee flexion, abduction and internal rotation estimation during ACL injury risk assessment tests, including drop landing and cutting tasks. This algorithm includes a special two-step complementary-based orientation filter and a special single-pose sensor-to-segment calibration procedure. Fourteen healthy subjects performed double-leg, single-leg drop landing and cutting tasks. Each subject wore four IMUs and reflective marker clusters on their thighs and shanks. For the presented knee angles algorithm with an empirical initial segment orientation, the root mean square errors (RMSEs) of the estimated continuous knee flexion, abduction and internal rotation cross all the movement tasks were 1.07°, 2.87° and 2.64°, and RMSEs of the peak knee flexion and peak knee abduction errors were 1.22° and 3.82°. The knee angles algorithm was capable of estimating knee abduction and internal rotation angles during drop landing and cutting tasks, and knee flexion estimation was substantially more accurate than previously reported approaches. Additionally, we found that for the presented algorithm, the accuracy of initial segment orientation was a critical factor for knee abduction and internal rotation estimations. The presented IMU-based knee angles algorithm could serve as a foundation to enable in-field biomechanical ACL injury risk assessment.
前交叉韧带（ACL）损伤是运动中常见且严重的膝关节损伤。膝关节的屈曲、外展和内旋角度被认为是 ACL 损伤风险的关键生物力学指标，但目前是在实验室中使用光学运动捕捉来计算的。本文介绍了一种基于惯性测量单元（IMU）的算法，用于在 ACL 损伤风险评估测试中估计膝关节的屈曲、外展和内旋，包括跳落和切割任务。该算法包括一个特殊的两步互补定向滤波器和一个特殊的单姿态传感器到节段校准过程。14 名健康受试者进行了双腿、单腿跳落和切割任务。每个受试者都在大腿和小腿上佩戴了四个 IMU 和反光标记簇。对于具有经验初始段取向的提出的膝关节角度算法，估计连续膝关节屈曲、外展和内旋的均方根误差（RMSE）在所有运动任务中都为 1.07°、2.87°和 2.64°，而峰值膝关节屈曲和峰值膝关节外展误差的 RMSE 分别为 1.22°和 3.82°。膝关节角度算法能够在跳落和切割任务中估计膝关节外展和内旋角度，并且膝关节屈曲的估计比以前报道的方法准确得多。此外，我们发现对于所提出的算法，初始段取向的准确性是外展和内旋估计的关键因素。所提出的基于 IMU 的膝关节角度算法可以作为现场生物力学 ACL 损伤风险评估的基础。
