Karatsidis Angelos, Bellusci Giovanni, Schepers H Martin, de Zee Mark, Andersen Michael S, Veltink Peter H
Xsens Technologies B.V., Enschede 7521 PR, The Netherlands.
Institute for Biomedical Technology and Technical Medicine (MIRA), University of Twente, Enschede 7500 AE, The Netherlands.
Sensors (Basel). 2016 Dec 31;17(1):75. doi: 10.3390/s17010075.
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 ( = 0.992, rRMSE = 5.3%), anterior ( = 0.965, rRMSE = 9.4%) and sagittal ( = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral ( = 0.862, rRMSE = 13.1%), frontal ( = 0.710, rRMSE = 29.6%), and transverse GRF&M ( = 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.
地面反作用力和力矩(GRF&M)是生物力学分析中重要的测量指标,用于估计关节动力学,而关节动力学常被用于推断许多肌肉骨骼疾病的相关信息。传统上,它们的评估是通过基于实验室的设备来实现的,这些设备无法应用于日常生活监测。在本研究中,我们提出了一种仅使用全动态惯性运动捕捉(IMC)的运动学信息来预测步行过程中GRF&M的方法。根据运动方程,我们推导出总外力和力矩。然后,我们使用基于平滑过渡假设的分布算法来解决双支撑阶段的不确定性问题。IMC预测的GRF&M与参考值之间的一致性在正常步行速度下,垂直GRF&M分量( = 0.992,rRMSE = 5.3%)、前向GRF&M分量( = 0.965,rRMSE = 9.4%)和矢状面GRF&M分量( = 0.933,rRMSE = 12.4%)的一致性为优秀,横向GRF&M分量( = 0.862,rRMSE = 13.1%)、额状面GRF&M分量( = 0.710,rRMSE = 29.6%)和横向GRF&M分量( = 0.826,rRMSE = 18.2%)的一致性为较强。我们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敏感性分析。本研究首次仅使用惯性运动捕捉来估计步态期间的三维GRF&M,提供了与光学运动捕捉预测相当的准确性。这种方法能够应用于需要在步态实验室之外估计步行过程中动力学的情况。