Predicting Knee Joint Contact Forces During Normal Walking Using Kinematic Inputs With a Long-Short Term Neural NetworkSource: Journal of Biomechanical Engineering:;2024:;volume( 146 ):;issue: 008::page 81004-1DOI: 10.1115/1.4064550Publisher: The American Society of Mechanical Engineers (ASME)
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 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.
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contributor author | Bennett, Hunter J. | |
contributor author | Estler, Kaileigh | |
contributor author | Valenzuela, Kevin | |
contributor author | Weinhandl, Joshua T. | |
date accessioned | 2024-04-24T22:43:05Z | |
date available | 2024-04-24T22:43:05Z | |
date copyright | 3/21/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 0148-0731 | |
identifier other | bio_146_08_081004.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4295742 | |
description 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 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Predicting Knee Joint Contact Forces During Normal Walking Using Kinematic Inputs With a Long-Short Term Neural Network | |
type | Journal Paper | |
journal volume | 146 | |
journal issue | 8 | |
journal title | Journal of Biomechanical Engineering | |
identifier doi | 10.1115/1.4064550 | |
journal fristpage | 81004-1 | |
journal lastpage | 81004-10 | |
page | 10 | |
tree | Journal of Biomechanical Engineering:;2024:;volume( 146 ):;issue: 008 | |
contenttype | Fulltext |