Ramadan Ahmed, Boss Connor, Choi Jongeun, Peter Reeves N, Cholewicki Jacek, Popovich John M, Radcliffe Clark J
Mem. ASME Department of Mechanical Engineering, MSU Center for Orthopedic Research (MSUCOR), Michigan State University, 428 S. Shaw Ln, East Lansing, MI 48824 e-mail: .
Mem. ASME Department of Electrical and Computer Engineering, MSU Center for Orthopedic Research (MSUCOR), Michigan State University, East Lansing, MI 48824 e-mail: .
J Biomech Eng. 2018 Jul 1;140(7):0745031-8. doi: 10.1115/1.4039677.
Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.
利用有限数据估计生物力学系统的许多参数可能会实现良好的拟合，但也可能会增加参数估计中的95%置信区间。这导致估计问题中的可识别性较差。因此，我们提出了一种新颖的方法来选择应估计的敏感生物力学模型参数，同时将其余参数固定为从初步估计中获得的值。我们的方法依赖于识别测量输出对其最敏感的参数。所提出的方法基于费舍尔信息矩阵（FIM）。将其与非线性最小绝对收缩和选择算子（LASSO）方法进行比较，以指导建模人员了解我们的FIM方法的优缺点。我们展示了一个应用，用于识别十名人类受试者头部位置跟踪任务的生物力学参数模型。使用测量数据，我们的方法（1）通过仅要求估计十二个参数中的五个来降低模型复杂性，（2）将参数95%置信区间显著降低多达原始置信区间的89%，（3）将用解释方差（VAF）衡量的拟合优度保持在82%，（4）减少了计算时间，我们的FIM方法比LASSO方法快164倍，并且（5）选择了与LASSO方法类似的敏感参数，其中FIM和LASSO方法共有的五个选定敏感参数中有三个。
