Neuromechanics Laboratory, Old Dominion University, 1007 Student Recreation Center, Norfolk, VA 23529.
Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN 37996.
J Biomech Eng. 2024 Aug 1;146(8). doi: 10.1115/1.4064550.
Knee joint contact forces are commonly estimated via surrogate measures (i.e., external knee adduction moments or musculoskeletal modeling). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience. The purpose of this study was to design a novel prediction method for knee joint contact forces that is simplistic in terms of required inputs. This study included marker trajectories and instrumented knee forces during normal walking from the "Grand Challenge" (n = 6) and "CAMS" (n = 2) datasets. Inverse kinematics were used to derive stance phase hip (sagittal, frontal, transverse), knee (sagittal, frontal), ankle (sagittal), and trunk (frontal) kinematics. A long-short term memory network (LSTM) was created using matlab to predict medial and lateral knee force waveforms using combinations of the kinematics. The Grand Challenge and CAMS datasets trained and tested the network, respectively. Musculoskeletal modeling forces were derived using static optimization and joint reaction tools in OpenSim. Waveform accuracy was determined as the proportion of variance and root-mean-square error between network predictions and in vivo data. The LSTM network was highly accurate for medial forces (R2 = 0.77, RMSE = 0.27 BW) and required only frontal hip and knee and sagittal hip and ankle kinematics. Modeled medial force predictions were excellent (R2 = 0.77, RMSE = 0.33 BW). Lateral force predictions were poor for both methods (LSTM R2 = 0.18, RMSE = 0.08 BW; modeling R2 = 0.21, RMSE = 0.54 BW). The designed LSTM network outperformed most reports of musculoskeletal modeling, including those reached in this study, revealing knee joint forces can accurately be predicted by using only kinematic input variables.
膝关节接触力通常通过替代测量（即外部膝关节内收力矩或肌肉骨骼建模）来估计。尽管建模具有这种能力，但对于临床医生或经验有限的人来说，它并不是最佳选择。本研究的目的是设计一种新的膝关节接触力预测方法，该方法在所需输入方面非常简单。本研究包括来自“Grand Challenge”（n=6）和“CAMS”（n=2）数据集的正常行走时的标记轨迹和仪器测量的膝关节力。逆运动学用于推导站立阶段髋部（矢状面、额状面、横断面）、膝部（矢状面、额状面）、踝部（矢状面）和躯干（额状面）运动学。使用 matlab 创建了一个长短期记忆网络（LSTM），该网络使用运动学的组合来预测内侧和外侧膝关节力波形。Grand Challenge 和 CAMS 数据集分别用于训练和测试网络。肌肉骨骼建模力使用 OpenSim 中的静态优化和关节反作用力工具得出。波形准确性确定为网络预测值与体内数据之间的方差和均方根误差的比例。LSTM 网络对内侧力具有很高的准确性（R2=0.77，RMSE=0.27 BW），仅需要额状面髋部和膝部以及矢状面髋部和踝部运动学。模型化的内侧力预测值非常出色（R2=0.77，RMSE=0.33 BW）。对于两种方法，外侧力预测都很差（LSTM R2=0.18，RMSE=0.08 BW；建模 R2=0.21，RMSE=0.54 BW）。设计的 LSTM 网络优于大多数肌肉骨骼建模报告，包括本研究中的报告，这表明仅使用运动学输入变量即可准确预测膝关节力。
