Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture.
Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture.
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DOI：
10.3390/s17010075
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发表时间：
2016-12-31
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影响因子：
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通讯作者：
Veltink PH
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作者：
Karatsidis A;Bellusci G;Schepers HM;de Zee M;Andersen MS;Veltink PH
地面反作用力和力矩（GRF&M）是生物力学分析中用于估计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。它们的评估通常是使用无法应用于日常生活监测的实验室设备来实现的。在这项研究中，我们提出了一种方法来预测GRF&M在步行过程中，完全使用动态惯性运动捕捉（IMC）的运动学信息。从运动方程中，我们导出了总的外力和力矩.然后，我们解决了不确定性问题，在双姿态使用分布算法的基础上的平滑过渡的假设。IMC预测和参考GRF&M之间的协议被归类为优秀的垂直步行速度超过正常步行速度（ρ = 0.992，rRMSE = 5.3%），前部（ρ = 0.965，rRMSE = 9.4%）和矢状面（ρ = 0.933，rRMSE = 12.4%）GRF&M分量和横向分量一样强（ρ = 0.862，rRMSE = 13.1%）、正面（ρ = 0.710，rRMSE = 29.6%）和横向GRF&M（ρ = 0.826，rRMSE = 18.2%）。对输入运动学滤波中使用的截止频率以及步态事件检测算法的阈值速度的影响进行了灵敏度分析。这项研究是第一个仅使用惯性运动捕捉来估计步态期间的3D GRF&M，提供与光学运动捕捉预测相当的准确性。这种方法使应用程序，需要在步态实验室外行走的动力学估计。
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.