Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction
Musculoskeletal models represent a powerful tool to gain knowledge on the internal forces acting at the joint level in a non-invasive way. However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is...
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| Veröffentlicht in: | PloS one 2018-09, Vol.13 (9), p.e0204109-e0204109 |
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| creator | De Pieri, Enrico Lund, Morten E Gopalakrishnan, Anantharaman Rasmussen, Kasper P Lunn, David E Ferguson, Stephen J |
| description | Musculoskeletal models represent a powerful tool to gain knowledge on the internal forces acting at the joint level in a non-invasive way. However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is necessary to prove the reliability of their predictions. This study documents the development of a generic musculoskeletal model and proposes a working logic and simulation techniques for identifying specific model features in need of refinement; as well as providing a quantitative validation for the prediction of hip contact forces (HCF). The model, implemented in the AnyBody Modeling System and based on the cadaveric dataset TLEM 2.0, was scaled to match the anthropometry of a patient fitted with an instrumented hip implant and to reproduce gait kinematics based on motion capture data. The relative contribution of individual muscle elements to the HCF and joint moments was analyzed to identify critical geometries, which were then compared to muscle magnetic resonance imaging (MRI) scans and, in case of inconsistencies, were modified to better match the volumetric scans. The predicted HCF showed good agreement with the overall trend and timing of the measured HCF from the instrumented prosthesis. The average root mean square error (RMSE), calculated for the total HCF was found to be 0.298*BW. Refining the geometries of the muscles thus identified reduced RMSE on HCF magnitudes by 17% (from 0.359*BW to 0.298*BW) over the whole gait cycle. The detailed study of individual muscle contributions to the HCF succeeded in identifying muscles with incorrect anatomy, which would have been difficult to intuitively identify otherwise. Despite a certain residual over-prediction of the final hip contact forces in the stance phase, a satisfactory level of geometrical accuracy of muscle paths has been achieved with the refinement of this model. |
| doi_str_mv | 10.1371/journal.pone.0204109 |
| format | Article |
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However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is necessary to prove the reliability of their predictions. This study documents the development of a generic musculoskeletal model and proposes a working logic and simulation techniques for identifying specific model features in need of refinement; as well as providing a quantitative validation for the prediction of hip contact forces (HCF). The model, implemented in the AnyBody Modeling System and based on the cadaveric dataset TLEM 2.0, was scaled to match the anthropometry of a patient fitted with an instrumented hip implant and to reproduce gait kinematics based on motion capture data. The relative contribution of individual muscle elements to the HCF and joint moments was analyzed to identify critical geometries, which were then compared to muscle magnetic resonance imaging (MRI) scans and, in case of inconsistencies, were modified to better match the volumetric scans. The predicted HCF showed good agreement with the overall trend and timing of the measured HCF from the instrumented prosthesis. The average root mean square error (RMSE), calculated for the total HCF was found to be 0.298*BW. Refining the geometries of the muscles thus identified reduced RMSE on HCF magnitudes by 17% (from 0.359*BW to 0.298*BW) over the whole gait cycle. The detailed study of individual muscle contributions to the HCF succeeded in identifying muscles with incorrect anatomy, which would have been difficult to intuitively identify otherwise. Despite a certain residual over-prediction of the final hip contact forces in the stance phase, a satisfactory level of geometrical accuracy of muscle paths has been achieved with the refinement of this model.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0204109</identifier><identifier>PMID: 30222777</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Anthropometry ; Biology and Life Sciences ; Biomechanics ; Cadavers ; Computer simulation ; Contact force ; Datasets ; Electromyography ; Gait ; Geometric accuracy ; Geometry ; Hip ; Hip joint ; Joint surgery ; Kinematics ; Magnetic resonance ; Magnetic resonance imaging ; Mathematical models ; Medicine and Health Sciences ; Motion capture ; Muscles ; Optimization techniques ; Physical Sciences ; Posture ; Predictions ; Prostheses ; Research and Analysis Methods ; Root-mean-square errors ; Systematic review</subject><ispartof>PloS one, 2018-09, Vol.13 (9), p.e0204109-e0204109</ispartof><rights>2018 De Pieri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is necessary to prove the reliability of their predictions. This study documents the development of a generic musculoskeletal model and proposes a working logic and simulation techniques for identifying specific model features in need of refinement; as well as providing a quantitative validation for the prediction of hip contact forces (HCF). The model, implemented in the AnyBody Modeling System and based on the cadaveric dataset TLEM 2.0, was scaled to match the anthropometry of a patient fitted with an instrumented hip implant and to reproduce gait kinematics based on motion capture data. The relative contribution of individual muscle elements to the HCF and joint moments was analyzed to identify critical geometries, which were then compared to muscle magnetic resonance imaging (MRI) scans and, in case of inconsistencies, were modified to better match the volumetric scans. The predicted HCF showed good agreement with the overall trend and timing of the measured HCF from the instrumented prosthesis. The average root mean square error (RMSE), calculated for the total HCF was found to be 0.298*BW. Refining the geometries of the muscles thus identified reduced RMSE on HCF magnitudes by 17% (from 0.359*BW to 0.298*BW) over the whole gait cycle. The detailed study of individual muscle contributions to the HCF succeeded in identifying muscles with incorrect anatomy, which would have been difficult to intuitively identify otherwise. Despite a certain residual over-prediction of the final hip contact forces in the stance phase, a satisfactory level of geometrical accuracy of muscle paths has been achieved with the refinement of this model.</description><subject>Accuracy</subject><subject>Anthropometry</subject><subject>Biology and Life Sciences</subject><subject>Biomechanics</subject><subject>Cadavers</subject><subject>Computer simulation</subject><subject>Contact force</subject><subject>Datasets</subject><subject>Electromyography</subject><subject>Gait</subject><subject>Geometric accuracy</subject><subject>Geometry</subject><subject>Hip</subject><subject>Hip joint</subject><subject>Joint surgery</subject><subject>Kinematics</subject><subject>Magnetic resonance</subject><subject>Magnetic resonance imaging</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Motion capture</subject><subject>Muscles</subject><subject>Optimization techniques</subject><subject>Physical 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titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>De Pieri, Enrico</au><au>Lund, Morten E</au><au>Gopalakrishnan, Anantharaman</au><au>Rasmussen, Kasper P</au><au>Lunn, David E</au><au>Ferguson, Stephen J</au><au>Brandon, Scott</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-09-17</date><risdate>2018</risdate><volume>13</volume><issue>9</issue><spage>e0204109</spage><epage>e0204109</epage><pages>e0204109-e0204109</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Musculoskeletal models represent a powerful tool to gain knowledge on the internal forces acting at the joint level in a non-invasive way. However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is necessary to prove the reliability of their predictions. This study documents the development of a generic musculoskeletal model and proposes a working logic and simulation techniques for identifying specific model features in need of refinement; as well as providing a quantitative validation for the prediction of hip contact forces (HCF). The model, implemented in the AnyBody Modeling System and based on the cadaveric dataset TLEM 2.0, was scaled to match the anthropometry of a patient fitted with an instrumented hip implant and to reproduce gait kinematics based on motion capture data. The relative contribution of individual muscle elements to the HCF and joint moments was analyzed to identify critical geometries, which were then compared to muscle magnetic resonance imaging (MRI) scans and, in case of inconsistencies, were modified to better match the volumetric scans. The predicted HCF showed good agreement with the overall trend and timing of the measured HCF from the instrumented prosthesis. The average root mean square error (RMSE), calculated for the total HCF was found to be 0.298*BW. Refining the geometries of the muscles thus identified reduced RMSE on HCF magnitudes by 17% (from 0.359*BW to 0.298*BW) over the whole gait cycle. The detailed study of individual muscle contributions to the HCF succeeded in identifying muscles with incorrect anatomy, which would have been difficult to intuitively identify otherwise. Despite a certain residual over-prediction of the final hip contact forces in the stance phase, a satisfactory level of geometrical accuracy of muscle paths has been achieved with the refinement of this model.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30222777</pmid><doi>10.1371/journal.pone.0204109</doi><orcidid>https://orcid.org/0000-0003-1077-6294</orcidid><oa>free_for_read</oa></addata></record> |
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| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Accuracy Anthropometry Biology and Life Sciences Biomechanics Cadavers Computer simulation Contact force Datasets Electromyography Gait Geometric accuracy Geometry Hip Hip joint Joint surgery Kinematics Magnetic resonance Magnetic resonance imaging Mathematical models Medicine and Health Sciences Motion capture Muscles Optimization techniques Physical Sciences Posture Predictions Prostheses Research and Analysis Methods Root-mean-square errors Systematic review |
| title | Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction |
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