Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait
Detailed knowledge of knee kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. This study used publicly available data provided by the "Grand Challenge Competition to P...
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description | Detailed knowledge of knee kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. This study used publicly available data provided by the "Grand Challenge Competition to Predict in-vivo Knee Loads" for the 2013 American Society of Mechanical Engineers Summer Bioengineering Conference (Fregly et al., 2012, "Grand Challenge Competition to Predict in vivo Knee Loads," J. Orthop. Res., 30, pp. 503-513) to develop a full body, musculoskeletal model with subject specific right leg geometries that can concurrently predict muscle forces, ligament forces, and knee and ground contact forces. The model includes representation of foot/floor interactions and predicted tibiofemoral joint loads were compared to measured tibial loads for two different cycles of treadmill gait. The model used anthropometric data (height and weight) to scale the joint center locations and mass properties of a generic model and then used subject bone geometries to more accurately position the hip and ankle. The musculoskeletal model included 44 muscles on the right leg, and subject specific geometries were used to create a 12 degrees-of-freedom anatomical right knee that included both patellofemoral and tibiofemoral articulations. Tibiofemoral motion was constrained by deformable contacts defined between the tibial insert and femoral component geometries and by ligaments. Patellofemoral motion was constrained by contact between the patellar button and femoral component geometries and the patellar tendon. Shoe geometries were added to the feet, and shoe motion was constrained by contact between three shoe segments per foot and the treadmill surface. Six-axis springs constrained motion between the feet and shoe segments. Experimental motion capture data provided input to an inverse kinematics stage, and the final forward dynamics simulations tracked joint angle errors for the left leg and upper body and tracked muscle length errors for the right leg. The one cycle RMS errors between the predicted and measured tibia contact were 178 N and 168 N for the medial and lateral sides for the first gait cycle and 209 N and 228 N for the medial and lateral sides for the faster second gait cycle. One cycle RMS errors between predicted and measured ground reaction forces were 12 N, 13 N, and 65 N in the anterior-posterior, medial-lateral, and vertical directions for the first gait cy |
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This study used publicly available data provided by the "Grand Challenge Competition to Predict in-vivo Knee Loads" for the 2013 American Society of Mechanical Engineers Summer Bioengineering Conference (Fregly et al., 2012, "Grand Challenge Competition to Predict in vivo Knee Loads," J. Orthop. Res., 30, pp. 503-513) to develop a full body, musculoskeletal model with subject specific right leg geometries that can concurrently predict muscle forces, ligament forces, and knee and ground contact forces. The model includes representation of foot/floor interactions and predicted tibiofemoral joint loads were compared to measured tibial loads for two different cycles of treadmill gait. The model used anthropometric data (height and weight) to scale the joint center locations and mass properties of a generic model and then used subject bone geometries to more accurately position the hip and ankle. The musculoskeletal model included 44 muscles on the right leg, and subject specific geometries were used to create a 12 degrees-of-freedom anatomical right knee that included both patellofemoral and tibiofemoral articulations. Tibiofemoral motion was constrained by deformable contacts defined between the tibial insert and femoral component geometries and by ligaments. Patellofemoral motion was constrained by contact between the patellar button and femoral component geometries and the patellar tendon. Shoe geometries were added to the feet, and shoe motion was constrained by contact between three shoe segments per foot and the treadmill surface. Six-axis springs constrained motion between the feet and shoe segments. Experimental motion capture data provided input to an inverse kinematics stage, and the final forward dynamics simulations tracked joint angle errors for the left leg and upper body and tracked muscle length errors for the right leg. The one cycle RMS errors between the predicted and measured tibia contact were 178 N and 168 N for the medial and lateral sides for the first gait cycle and 209 N and 228 N for the medial and lateral sides for the faster second gait cycle. One cycle RMS errors between predicted and measured ground reaction forces were 12 N, 13 N, and 65 N in the anterior-posterior, medial-lateral, and vertical directions for the first gait cycle and 43 N, 15 N, and 96 N in the anterior-posterior, medial-lateral, and vertical directions for the second gait cycle.</description><identifier>ISSN: 0148-0731</identifier><identifier>EISSN: 1528-8951</identifier><identifier>DOI: 10.1115/1.4026359</identifier><identifier>PMID: 24389997</identifier><language>eng</language><publisher>United States: ASME</publisher><subject>Computer Simulation ; Constraints ; Contact ; Errors ; Exercise Test ; Female ; Femur - physiology ; Friction ; Gait ; Gait - physiology ; Humans ; Knee Joint - physiology ; Knees ; Loads (forces) ; Male ; Mathematical models ; Models, Biological ; Muscle Contraction - physiology ; Muscle, Skeletal - physiology ; Muscles ; Physical Exertion - physiology ; Research Papers ; Stress, Mechanical ; Tibia - physiology ; Young Adult</subject><ispartof>Journal of biomechanical engineering, 2014-02, Vol.136 (2), p.021032-np</ispartof><rights>Copyright © 2014 by ASME 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a564t-b446e7ef68cb994c8cb7112ba006dbae65bcd473ba2b2260a9fbecb84474e58f3</citedby><cites>FETCH-LOGICAL-a564t-b446e7ef68cb994c8cb7112ba006dbae65bcd473ba2b2260a9fbecb84474e58f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925,38520</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24389997$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Guess, Trent M</creatorcontrib><creatorcontrib>Stylianou, Antonis P</creatorcontrib><creatorcontrib>Kia, Mohammad</creatorcontrib><title>Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait</title><title>Journal of biomechanical engineering</title><addtitle>J Biomech Eng</addtitle><addtitle>J Biomech Eng</addtitle><description>Detailed knowledge of knee kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. This study used publicly available data provided by the "Grand Challenge Competition to Predict in-vivo Knee Loads" for the 2013 American Society of Mechanical Engineers Summer Bioengineering Conference (Fregly et al., 2012, "Grand Challenge Competition to Predict in vivo Knee Loads," J. Orthop. Res., 30, pp. 503-513) to develop a full body, musculoskeletal model with subject specific right leg geometries that can concurrently predict muscle forces, ligament forces, and knee and ground contact forces. The model includes representation of foot/floor interactions and predicted tibiofemoral joint loads were compared to measured tibial loads for two different cycles of treadmill gait. The model used anthropometric data (height and weight) to scale the joint center locations and mass properties of a generic model and then used subject bone geometries to more accurately position the hip and ankle. The musculoskeletal model included 44 muscles on the right leg, and subject specific geometries were used to create a 12 degrees-of-freedom anatomical right knee that included both patellofemoral and tibiofemoral articulations. Tibiofemoral motion was constrained by deformable contacts defined between the tibial insert and femoral component geometries and by ligaments. Patellofemoral motion was constrained by contact between the patellar button and femoral component geometries and the patellar tendon. Shoe geometries were added to the feet, and shoe motion was constrained by contact between three shoe segments per foot and the treadmill surface. Six-axis springs constrained motion between the feet and shoe segments. Experimental motion capture data provided input to an inverse kinematics stage, and the final forward dynamics simulations tracked joint angle errors for the left leg and upper body and tracked muscle length errors for the right leg. The one cycle RMS errors between the predicted and measured tibia contact were 178 N and 168 N for the medial and lateral sides for the first gait cycle and 209 N and 228 N for the medial and lateral sides for the faster second gait cycle. One cycle RMS errors between predicted and measured ground reaction forces were 12 N, 13 N, and 65 N in the anterior-posterior, medial-lateral, and vertical directions for the first gait cycle and 43 N, 15 N, and 96 N in the anterior-posterior, medial-lateral, and vertical directions for the second gait cycle.</description><subject>Computer Simulation</subject><subject>Constraints</subject><subject>Contact</subject><subject>Errors</subject><subject>Exercise Test</subject><subject>Female</subject><subject>Femur - physiology</subject><subject>Friction</subject><subject>Gait</subject><subject>Gait - physiology</subject><subject>Humans</subject><subject>Knee Joint - physiology</subject><subject>Knees</subject><subject>Loads (forces)</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Models, Biological</subject><subject>Muscle Contraction - physiology</subject><subject>Muscle, Skeletal - physiology</subject><subject>Muscles</subject><subject>Physical Exertion - physiology</subject><subject>Research Papers</subject><subject>Stress, Mechanical</subject><subject>Tibia - physiology</subject><subject>Young Adult</subject><issn>0148-0731</issn><issn>1528-8951</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkb1vFDEQxS1ERI5AQY2EXIZig8dfazdI6EICUiIoLrVle2eDo911sHeR-O_Z6I4IKqimmN88vTePkFfAzgBAvYMzybgWyj4hG1DcNMYqeEo2DKRpWCvgmDyv9Y4xACPZM3LMpTDW2nZDbrZ5ikspOM30a8EuxTnlieaeXi81Dkj91NFdCin3OObiB7oezD7O9CKXiJWeLyVNt3RX0HdjGgZ66dP8ghz1fqj48jBPyM3Fx932U3P15fLz9sNV45WWcxOk1Nhir00M1sq4jhaAB8-Y7oJHrULsZCuC54FzzbztA8ZgpGwlKtOLE_J-r3u_hBG7uKZYLbr7kkZffrrsk_t7M6Vv7jb_cOu_hFZmFTg9CJT8fcE6uzHViMPgJ8xLdaBbUFZbJv6NKsGsklqq_0A5E0ZI_qD6do_Gkmst2D-aB-YeynXgDuWu7Js_0z6Sv9tcgdd7wNcR3V1eyrR-30nJrQDxC_MuqHM</recordid><startdate>20140201</startdate><enddate>20140201</enddate><creator>Guess, Trent M</creator><creator>Stylianou, Antonis P</creator><creator>Kia, Mohammad</creator><general>ASME</general><general>American Society of Mechanical Engineers</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope><scope>7TB</scope><scope>7U5</scope><scope>F28</scope><scope>L7M</scope><scope>5PM</scope></search><sort><creationdate>20140201</creationdate><title>Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait</title><author>Guess, Trent M ; Stylianou, Antonis P ; Kia, Mohammad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a564t-b446e7ef68cb994c8cb7112ba006dbae65bcd473ba2b2260a9fbecb84474e58f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Computer Simulation</topic><topic>Constraints</topic><topic>Contact</topic><topic>Errors</topic><topic>Exercise Test</topic><topic>Female</topic><topic>Femur - physiology</topic><topic>Friction</topic><topic>Gait</topic><topic>Gait - physiology</topic><topic>Humans</topic><topic>Knee Joint - physiology</topic><topic>Knees</topic><topic>Loads (forces)</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Models, Biological</topic><topic>Muscle Contraction - physiology</topic><topic>Muscle, Skeletal - physiology</topic><topic>Muscles</topic><topic>Physical Exertion - physiology</topic><topic>Research Papers</topic><topic>Stress, Mechanical</topic><topic>Tibia - physiology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guess, Trent M</creatorcontrib><creatorcontrib>Stylianou, Antonis P</creatorcontrib><creatorcontrib>Kia, Mohammad</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of biomechanical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guess, Trent M</au><au>Stylianou, Antonis P</au><au>Kia, Mohammad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait</atitle><jtitle>Journal of biomechanical engineering</jtitle><stitle>J Biomech Eng</stitle><addtitle>J Biomech Eng</addtitle><date>2014-02-01</date><risdate>2014</risdate><volume>136</volume><issue>2</issue><spage>021032</spage><epage>np</epage><pages>021032-np</pages><issn>0148-0731</issn><eissn>1528-8951</eissn><abstract>Detailed knowledge of knee kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. This study used publicly available data provided by the "Grand Challenge Competition to Predict in-vivo Knee Loads" for the 2013 American Society of Mechanical Engineers Summer Bioengineering Conference (Fregly et al., 2012, "Grand Challenge Competition to Predict in vivo Knee Loads," J. Orthop. Res., 30, pp. 503-513) to develop a full body, musculoskeletal model with subject specific right leg geometries that can concurrently predict muscle forces, ligament forces, and knee and ground contact forces. The model includes representation of foot/floor interactions and predicted tibiofemoral joint loads were compared to measured tibial loads for two different cycles of treadmill gait. The model used anthropometric data (height and weight) to scale the joint center locations and mass properties of a generic model and then used subject bone geometries to more accurately position the hip and ankle. The musculoskeletal model included 44 muscles on the right leg, and subject specific geometries were used to create a 12 degrees-of-freedom anatomical right knee that included both patellofemoral and tibiofemoral articulations. Tibiofemoral motion was constrained by deformable contacts defined between the tibial insert and femoral component geometries and by ligaments. Patellofemoral motion was constrained by contact between the patellar button and femoral component geometries and the patellar tendon. Shoe geometries were added to the feet, and shoe motion was constrained by contact between three shoe segments per foot and the treadmill surface. Six-axis springs constrained motion between the feet and shoe segments. Experimental motion capture data provided input to an inverse kinematics stage, and the final forward dynamics simulations tracked joint angle errors for the left leg and upper body and tracked muscle length errors for the right leg. The one cycle RMS errors between the predicted and measured tibia contact were 178 N and 168 N for the medial and lateral sides for the first gait cycle and 209 N and 228 N for the medial and lateral sides for the faster second gait cycle. One cycle RMS errors between predicted and measured ground reaction forces were 12 N, 13 N, and 65 N in the anterior-posterior, medial-lateral, and vertical directions for the first gait cycle and 43 N, 15 N, and 96 N in the anterior-posterior, medial-lateral, and vertical directions for the second gait cycle.</abstract><cop>United States</cop><pub>ASME</pub><pmid>24389997</pmid><doi>10.1115/1.4026359</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Computer Simulation Constraints Contact Errors Exercise Test Female Femur - physiology Friction Gait Gait - physiology Humans Knee Joint - physiology Knees Loads (forces) Male Mathematical models Models, Biological Muscle Contraction - physiology Muscle, Skeletal - physiology Muscles Physical Exertion - physiology Research Papers Stress, Mechanical Tibia - physiology Young Adult |
title | Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait |
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