Calibrated muscle models improve tracking simulations without enhancing gait predictions.

<h4>Objectives</h4>This study presents two main aims: (i) to assess functionally-calibrated musculoskeletal models (FCMs) in both tracking and predictive simulations of human motion, against non-linearly scaled models (NSMs), and (ii) to examine the effects of three different variations...

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Main Authors: Filippo Maceratesi, Míriam Febrer-Nafría, Josep M Font-Llagunes
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0327172
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author Filippo Maceratesi
Míriam Febrer-Nafría
Josep M Font-Llagunes
author_facet Filippo Maceratesi
Míriam Febrer-Nafría
Josep M Font-Llagunes
author_sort Filippo Maceratesi
collection DOAJ
description <h4>Objectives</h4>This study presents two main aims: (i) to assess functionally-calibrated musculoskeletal models (FCMs) in both tracking and predictive simulations of human motion, against non-linearly scaled models (NSMs), and (ii) to examine the effects of three different variations of our baseline functional calibration approach on the results of tracking and predictive simulations.<h4>Methods</h4>Motion capture experiments of six functional activities were performed with three healthy subjects. The musculotendon (MT) parameters of 18 muscles per leg were estimated using an optimal control problem. A baseline problem formulation and three variations were developed to generate four different FCMs per subject. Then, the FCMs were compared against NSMs in tracking simulations of the motions excluded from the calibration and fully-predictive simulations of gait.<h4>Results</h4>In the tracking simulations, the FCMs led to more accurate joint torques estimations. Including gait in the calibration problems improved the knee torques accuracy (normalised root mean square error: 0.31 [Formula: see text] 0.11), compared to the baseline calibration (normalised root mean square error: 0.70 [Formula: see text] 0.21). Regarding the gait predictive simulations, the NSMs consistently yielded more accurate subtalar inversion/eversion torques and knee flexion angles, compared to the FCMs. The accuracy of the predicted muscle excitations was generally consistent between NSMs and FCMs.<h4>Conclusion</h4>The results suggest that, while the FCMs led to more accurate joint torques estimations in the tracking simulations, they did not outperform the NSMs in the fully-predictive gait simulations.
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spelling doaj-art-aea5dbcd3f3e4c0386497f4ea66419c92025-08-20T03:50:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032717210.1371/journal.pone.0327172Calibrated muscle models improve tracking simulations without enhancing gait predictions.Filippo MaceratesiMíriam Febrer-NafríaJosep M Font-Llagunes<h4>Objectives</h4>This study presents two main aims: (i) to assess functionally-calibrated musculoskeletal models (FCMs) in both tracking and predictive simulations of human motion, against non-linearly scaled models (NSMs), and (ii) to examine the effects of three different variations of our baseline functional calibration approach on the results of tracking and predictive simulations.<h4>Methods</h4>Motion capture experiments of six functional activities were performed with three healthy subjects. The musculotendon (MT) parameters of 18 muscles per leg were estimated using an optimal control problem. A baseline problem formulation and three variations were developed to generate four different FCMs per subject. Then, the FCMs were compared against NSMs in tracking simulations of the motions excluded from the calibration and fully-predictive simulations of gait.<h4>Results</h4>In the tracking simulations, the FCMs led to more accurate joint torques estimations. Including gait in the calibration problems improved the knee torques accuracy (normalised root mean square error: 0.31 [Formula: see text] 0.11), compared to the baseline calibration (normalised root mean square error: 0.70 [Formula: see text] 0.21). Regarding the gait predictive simulations, the NSMs consistently yielded more accurate subtalar inversion/eversion torques and knee flexion angles, compared to the FCMs. The accuracy of the predicted muscle excitations was generally consistent between NSMs and FCMs.<h4>Conclusion</h4>The results suggest that, while the FCMs led to more accurate joint torques estimations in the tracking simulations, they did not outperform the NSMs in the fully-predictive gait simulations.https://doi.org/10.1371/journal.pone.0327172
spellingShingle Filippo Maceratesi
Míriam Febrer-Nafría
Josep M Font-Llagunes
Calibrated muscle models improve tracking simulations without enhancing gait predictions.
PLoS ONE
title Calibrated muscle models improve tracking simulations without enhancing gait predictions.
title_full Calibrated muscle models improve tracking simulations without enhancing gait predictions.
title_fullStr Calibrated muscle models improve tracking simulations without enhancing gait predictions.
title_full_unstemmed Calibrated muscle models improve tracking simulations without enhancing gait predictions.
title_short Calibrated muscle models improve tracking simulations without enhancing gait predictions.
title_sort calibrated muscle models improve tracking simulations without enhancing gait predictions
url https://doi.org/10.1371/journal.pone.0327172
work_keys_str_mv AT filippomaceratesi calibratedmusclemodelsimprovetrackingsimulationswithoutenhancinggaitpredictions
AT miriamfebrernafria calibratedmusclemodelsimprovetrackingsimulationswithoutenhancinggaitpredictions
AT josepmfontllagunes calibratedmusclemodelsimprovetrackingsimulationswithoutenhancinggaitpredictions