矢状面
运动学
脚踝
膝关节
逆动力学
地面反作用力
接触力
接头(建筑物)
生物力学
部队平台
均方误差
步态
计算机科学
物理医学与康复
数学
医学
结构工程
物理
工程类
解剖
外科
统计
经典力学
量子力学
作者
Hunter J. Bennett,Kaileigh Estler,Kevin A. Valenzuela,Joshua T. Weinhandl
出处
期刊:Journal of biomechanical engineering
[ASME International]
日期:2024-01-25
卷期号:: 1-38
摘要
Abstract 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's 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 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=0.27BW) and required only frontal hip and knee and sagittal hip and ankle kinematics. Modeled medial force predictions were also excellent (R2=0.77, RMSE=0.33BW). 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.
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