Document Detail

Predicting muscle forces in gait from EMG signals and musculotendon kinematics.
MedLine Citation:
PMID:  20719615     Owner:  NLM     Status:  In-Data-Review    
An EMG-driven muscle model for determining muscle force-time histories during gait is presented. The model, based on Hill's equation (1938), incorporates morphological data and accounts for changes in musculotendon length, velocity, and the level of muscle excitation for both concentric and eccentric contractions. Musculotendon kinematics were calculated using three-dimensional cinematography with a model of the musculoskeletal system. Muscle force-length-EMG relations were established from slow isokinetic calibrations. Walking muscle force-time histories were determined for two subjects. Joint moments calculated from the predicted muscle forces were compared with moments calculated using a linked segment, inverse dynamics approach. Moment curve correlations ranged from r = 0.72 to r = 0.97 and the root mean square (RMS) differences were from 10 to 20 Nm. Expressed as a relative RMS, the moment differences ranged from a low of 23% at the ankle to a high of 72% at the hip. No single reason for the differences between the two moment curves could be identified. Possible explanations discussed include the linear EMG-to-force assumption and how well the EMG-to-force calibration represented excitation for the whole muscle during gait, assumptions incorporated in the muscle modeling procedure, and errors inherent in validating joint moments predicted from the model to moments calculated using linked segment, inverse dynamics. The closeness with which the joint moment curves matched in the present study supports using the modeling approach proposed to determine muscle forces in gait.
S C White; D A Winter
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology     Volume:  2     ISSN:  1050-6411     ISO Abbreviation:  J Electromyogr Kinesiol     Publication Date:  1992  
Date Detail:
Created Date:  2010-08-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9109125     Medline TA:  J Electromyogr Kinesiol     Country:  England    
Other Details:
Languages:  eng     Pagination:  217-31     Citation Subset:  -    
Copyright Information:
Copyright © 1993. Published by Elsevier Ltd.
Department of Physical Therapy and Exercise Science, State University of New York at Buffalo, Buffalo, New York, U.S.A.
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