Literature DB >> 20719624

Phasic behavior of EMG signals during gait: Use of multivariate statistics.

B L Davis1, C L Vaughan.   

Abstract

The present study represents an attempt to ascertain whether there are some underlying trends that, in some combination, can explain all the variations in the linear envelopes of 16 EMG signals of selected leg muscles. Two kinds of analyses were performed: (a) a factor analysis of EMG data corresponding to 16 muscles of the lower limb and (b) a "multi-dimensional scaling" (MDS) procedure. The latter technique involved mapping the Cartesian coordinates for 16 points subject to the constraint that the distance between any two points reflected the degree of coactivity for the corresponding muscles. The results of these analyses showed that four factors could account for 91.5% of the variance in the original data set. These factors could be clearly demarcated on the "muscle map," tending to support the notion that there are motor "programs" for groups of muscles that have to perform a given function during locomotion. Further analysis of the loading matrix (i.e., correlations between each EMG vector and each of the four factors) showed groups of muscles that acted in a similar manner. The muscle groups could be divided into those that act at the times of (a) heelstrike, (b) single limb loading response, (c) propulsion phase, or else (d) acted in a biphasic manner.
Copyright © 1993. Published by Elsevier Ltd.

Entities:  

Year:  1993        PMID: 20719624     DOI: 10.1016/1050-6411(93)90023-P

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  30 in total

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Journal:  J Physiol       Date:  2012-03-12       Impact factor: 5.182

2.  Three-dimensional modular control of human walking.

Authors:  Jessica L Allen; Richard R Neptune
Journal:  J Biomech       Date:  2012-06-21       Impact factor: 2.712

3.  Neural regulation of rhythmic arm and leg movement is conserved across human locomotor tasks.

Authors:  E Paul Zehr; Jaclyn E Balter; Daniel P Ferris; Sandra R Hundza; Pamela M Loadman; Rebecca H Stoloff
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4.  Validating the feedback control of intersegmental coordination by fluctuation analysis of disturbed walking.

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Journal:  Exp Brain Res       Date:  2015-02-06       Impact factor: 1.972

Review 5.  How to improve the muscle synergy analysis methodology?

Authors:  Nicolas A Turpin; Stéphane Uriac; Georges Dalleau
Journal:  Eur J Appl Physiol       Date:  2021-01-26       Impact factor: 3.078

6.  The motor repertoire of older adult fallers may constrain their response to balance perturbations.

Authors:  Jessica L Allen; Jason R Franz
Journal:  J Neurophysiol       Date:  2018-08-22       Impact factor: 2.714

7.  Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition.

Authors:  Fiorenzo Artoni; Arnaud Delorme; Scott Makeig
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8.  Neuromuscular adjustments of gait associated with unstable conditions.

Authors:  G Martino; Y P Ivanenko; A d'Avella; M Serrao; A Ranavolo; F Draicchio; G Cappellini; C Casali; F Lacquaniti
Journal:  J Neurophysiol       Date:  2015-09-16       Impact factor: 2.714

9.  Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke.

Authors:  David J Clark; Lena H Ting; Felix E Zajac; Richard R Neptune; Steven A Kautz
Journal:  J Neurophysiol       Date:  2009-12-09       Impact factor: 2.714

10.  Modular control of human walking: Adaptations to altered mechanical demands.

Authors:  Craig P McGowan; Richard R Neptune; David J Clark; Steven A Kautz
Journal:  J Biomech       Date:  2009-10-30       Impact factor: 2.712

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