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Musculoskeletal simulation of upper ...

University of Delaware., Department of Mechanical Engineering.

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Musculoskeletal simulation of upper extremity motion: Effect of selective muscle weakness and application to rehabilitation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Musculoskeletal simulation of upper extremity motion: Effect of selective muscle weakness and application to rehabilitation./
Author:	Shah, Shridhar.
Description:	146 p.
Notes:	Adviser: Jill S. Higginson.
Contained By:	Masters Abstracts International48-01.
Subject:	Biophysics, Biomechanics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1469608
ISBN:	9781109393781

Musculoskeletal simulation of upper extremity motion: Effect of selective muscle weakness and application to rehabilitation.
Shah, Shridhar.

Musculoskeletal simulation of upper extremity motion: Effect of selective muscle weakness and application to rehabilitation. - 146 p.

Adviser: Jill S. Higginson.

Thesis (M.S.M.E.)--University of Delaware, 2009.

Previous research has shown that patients with weak or paralyzed muscles use compensatory strategies to perform functional tasks. The knowledge of compensatory strategies can help rehabilitation experts identify alternate muscles which can help improve range of motion upon strengthening. It is difficult to study such compensatory strategies for individual muscle weakness in vivo. Computational models have proven to be very useful in upper extremity biomechanics for the study of joint stability, muscle coordination and design of neuroprosthetic devices. However, these studies used inverse dynamic simulations which have limited capability to study the causal relationships between model parameters. A forward dynamic simulation could be useful to elucidate relationships between model parameters and to study compensatory strategies of the musculoskeletal system.

ISBN: 9781109393781Subjects--Topical Terms:

1035342
Biophysics, Biomechanics.

Musculoskeletal simulation of upper extremity motion: Effect of selective muscle weakness and application to rehabilitation.
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008 100802s2009 ||||||||||||||||| ||eng d
020 $a 9781109393781
035 $a (UMI)AAI1469608
035 $a AAI1469608
040 $a UMI $c UMI
100 1 $a Shah, Shridhar. $3 1035553
245 1 0 $a Musculoskeletal simulation of upper extremity motion: Effect of selective muscle weakness and application to rehabilitation.
300 $a 146 p.
500 $a Adviser: Jill S. Higginson.
500 $a Source: Masters Abstracts International, Volume: 48-01, page: .
502 $a Thesis (M.S.M.E.)--University of Delaware, 2009.
520 $a Previous research has shown that patients with weak or paralyzed muscles use compensatory strategies to perform functional tasks. The knowledge of compensatory strategies can help rehabilitation experts identify alternate muscles which can help improve range of motion upon strengthening. It is difficult to study such compensatory strategies for individual muscle weakness in vivo. Computational models have proven to be very useful in upper extremity biomechanics for the study of joint stability, muscle coordination and design of neuroprosthetic devices. However, these studies used inverse dynamic simulations which have limited capability to study the causal relationships between model parameters. A forward dynamic simulation could be useful to elucidate relationships between model parameters and to study compensatory strategies of the musculoskeletal system.
520 $a We developed a computational tool using optimization and forward dynamic simulations of upper extremity movements to estimate muscle activation patterns, study effects of selective muscle weakness and examine possible applications in designing rehabilitation protocols. The musculoskeletal model was actuated by 26 muscle elements which crossed the shoulder complex and elbow joint. The simulation technique for estimating activation patterns was verified by comparing its outputs with recorded kinematics and EMG data for four different activities. The four activities were shoulder abduction in three planes of elevation and an activity of daily living which involved reaching to a shelf. Simulated annealing optimization was used and the output activations were compared with recorded EMG for 7 muscles with an average cross-correlation of 0.835 across four activities. This approach provided a useful tool which can be applied to the study of coordination patterns with altered musculoskeletal properties or in designing rehabilitation protocols like strength training.
520 $a The simulations with individual muscle weakness estimated muscle activations required to track kinematic data of an able-bodied subject for shoulder abduction in three different elevation planes. The comparison of the results with normal activation patterns reflected the strategy used to compensate for selective muscle weakness. Weakness in middle deltoid was found to be compensated by supraspinatus and weakness in subscapularis was compensated by anterior deltoid, while weakness in infraspinatus required changes in activation of multiple muscles.
520 $a We reduced the strength of multiple muscles to simulate the effects of aging and predicted the muscles which could improve shoulder range of motion if strengthened. The results showed that an increase in the strength of middle deltoid, posterior deltoid and supraspinatus by 5%, 10% and 16% respectively would achieve 10.5% increase in shoulder elevation compared to the aged condition. These results provide quantitative information on optimal muscles to strengthen to achieve normal abduction, which could be further useful for the effective design of strength training protocols.
590 $a School code: 0060.
650 4 $a Biophysics, Biomechanics. $3 1035342
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Health Sciences, Rehabilitation and Therapy. $3 1017926
690 $a 0382
690 $a 0541
690 $a 0648
710 2 $a University of Delaware. $b Department of Mechanical Engineering. $3 1023760
773 0 $t Masters Abstracts International $g 48-01.
790 $a 0060
790 1 0 $a Agrawal, Sunil K. $e committee member
790 1 0 $a Higginson, Jill S., $e advisor
790 1 0 $a Novotny, John E. $e committee member
791 $a M.S.M.E.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1469608