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MRI MEASURED MUSCLE PARAMETERS IMPROVED KNEE MOMENT PREDICTION OF AN EMG-DRIVEN MODEL.
MedLine Citation:
PMID:  21775905     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
INTRODUCTION:: Acquisition of muscle anatomic parameters are essential for the development of a musculoskeletal model to estimate muscle forces and joint kinetics, and can be derived 3 ways: 1) use of a generic anatomic model; 2) scaling of a generic model based on anthropometric measures; and 3) direct in-vivo measurements using various imaging techniques. PURPOSE:: The purpose of this study was to investigate how incorporating direct measurements of muscle anatomic parameters using MRI (muscle volumes and moment arms) influence knee moment predictions when compared to generic and scaled models. METHODS:: Joint moment predictions of the 3 modeling approaches were examined by comparing the net knee moments calculated by each model with standard net joint moment measurements (inverse dynamics calculations and dynamometry) obtained while 7 subjects (3 females, 4 males) performed a drop-landing and isokinetic knee extension task. The coefficient of multiple correlation and mean absolute difference were calculated to examine the prediction error and agreement of each model with standard net knee moment measurements. RESULTS:: For both tasks, the model incorporating direct measurements of muscle volumes and moment arms had a higher coefficient of multiple correlation and smaller mean absolute difference than the generic and scaled models (effect size range: 0.99 to 1.37). The scaled model had a lower coefficient of multiple correlation and greater mean absolute difference than the generic model (effect size = 1.36). CONCLUSIONS:: Our findings demonstrate that knee moment predictions from an EMG-driven model can be improved with direct measurements of muscle anatomic parameters. Knee moment predictions did not improve when scaling a generic anatomic model. Musculoskeletal models that incorporate direct measures of muscle anatomic parameters may provide more accurate assessments of joint kinetics when compared to generic and scaled models.
Authors:
Liang-Ching Tsai; Patrick M Colletti; Christopher M Powers
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-7-19
Journal Detail:
Title:  Medicine and science in sports and exercise     Volume:  -     ISSN:  1530-0315     ISO Abbreviation:  -     Publication Date:  2011 Jul 
Date Detail:
Created Date:  2011-7-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8005433     Medline TA:  Med Sci Sports Exerc     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
1Division of Biokinesiology and Physical Therapy, University of Southern California. 2Department of Radiology, University of Southern California.
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