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Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

机译:仅使用惯性运动捕捉估算步态中的地面反作用力和力矩

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摘要

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.
机译:地面反作用力和力矩(GRF&M)是用作生物力学分析中的输入量以评估关节动力学的重要指标,通常用于推断许多肌肉骨骼疾病的信息。传统上,他们的评估是使用无法用于日常生活监测的基于实验室的设备来完成的。在这项研究中,我们提出了一种使用完全动态惯性运动捕获(IMC)专有的运动学信息预测步行过程中GRF&M的方法。从运动方程式中,我们得出总的外力和力矩。然后,我们使用基于平滑过渡假设的分布算法解决了双重立场期间的不确定性问题。在正常步行速度下,IMC预测的参考GRF&M与参考的GRF&M之间的一致性分为垂直(ρ= 0.992,rRMSE = 5.3%),前向(ρ= 0.965,rRMSE = 9.4%)和矢状(ρ= 0.933, rRMSE = 12.4%)GRF&M分量,并且对于侧面(ρ= 0.862,rRMSE = 13.1%),正面(ρ= 0.710,rRMSE = 29.6%)和横向GRF&M分量(ρ= 0.826,rRMSE = 18.2%)具有相同的强度。对输入运动学滤波中使用的截止频率的影响以及步态事件检测算法的阈值速度进行了敏感性分析。这项研究是第一个仅使用惯性运动捕获来估计步态中3D GRF&M的研究,可提供与光学运动捕获预测相当的准确性。这种方法使需要在步态实验室外行走时需要估算动力学的应用成为可能。

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