Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America.
M-Sense Research Group, University of Vermont, Burlington, VT, United States of America.
PLoS One. 2021 Apr 20;16(4):e0249577. doi: 10.1371/journal.pone.0249577. eCollection 2021.
Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.
人体下肢运动学测量在许多应用中至关重要，包括步态分析、提高运动员表现、降低或监测受伤风险、增强作战人员表现以及监测老年人跌倒风险等。我们提出了一种使用误差状态卡尔曼滤波器估计下肢运动学的新方法，该方法利用了一系列佩戴在身体上的惯性测量单元（IMU）和四个运动学约束条件。我们使用模拟和实验数据对简化的下肢（骨盆和两条腿）三体模型进行了方法评估。对该三体模型的评估允许直接评估 ErKF 方法，而无需考虑人体受试者的几个混杂误差源（例如软组织伪影和解剖框架的确定）。与模拟相比，三个估计的髋关节角度的 RMS 差异均保持在 0.2 度以下，与实验光学运动捕捉（MOCAP）相比，RMS 差异保持在 1.4 度以下。与模拟相比，步长和步宽的 RMS 差异分别保持在 1%和 4%以内，与实验（MOCAP）相比，分别保持在 7%和 5%以内。这些结果尤为重要，因为它们预示着未来在更复杂的人类运动模型中推进这种方法的成功。特别是，我们未来的工作旨在将这种方法扩展到由骨盆、大腿、小腿和脚组成的人类下肢的七体模型。
