Literature DB >> 9291550

The use of electromyography for the noninvasive prediction of muscle forces. Current issues.

J J Dowling1.   

Abstract

Suitably processed electromyographic (EMG) signals can be combined with Hill-type musculoskeletal models to noninvasively achieve estimations of individual muscle forces. This method has particular advantages over other methods for the assessment of a given performance. The purpose of this review is to report on the current issues facing the human movement scientist who wishes to extend the kinetic information yielded by linked segment models to the kinetics of individual muscles. Such an extension is necessary when considering co-contraction of antagonistic muscles, the role of bi-articular muscles, co-ordination, movement efficiency or bone-on-bone forces. Currently, linked segment models have not been successfully extended to individual muscle forces for diagnostic purposes by using the EMG approach or any other approach. Most models have been designed for a specific purpose and have only been evaluated over a narrow range of movement conditions. More generalised models will require greater complexity and possibly more extensive calibration or an increased number of specific inputs or greater computational effort. This review shows the promise of the EMG approach and presents the challenges, as well as the strategies, that should enable more general, accurate and precise estimates of individual muscle forces.

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Year:  1997        PMID: 9291550     DOI: 10.2165/00007256-199724020-00002

Source DB:  PubMed          Journal:  Sports Med        ISSN: 0112-1642            Impact factor:   11.136


  53 in total

1.  An activation-recruitment scheme for use in muscle modeling.

Authors:  D A Hawkins; M L Hull
Journal:  J Biomech       Date:  1992-12       Impact factor: 2.712

2.  Least-squares identification of the dynamic relation between the electromyogram and joint moment.

Authors:  J Bobet; R W Norman
Journal:  J Biomech       Date:  1990       Impact factor: 2.712

3.  The EMG-force relationships of skeletal muscle; dependence on contraction rate, and motor units control strategy.

Authors:  M Solomonow; R Baratta; H Shoji; R D'Ambrosia
Journal:  Electromyogr Clin Neurophysiol       Date:  1990 Apr-May

4.  Partitioning of the L4-L5 dynamic moment into disc, ligamentous, and muscular components during lifting.

Authors:  S M McGill; R W Norman
Journal:  Spine (Phila Pa 1976)       Date:  1986-09       Impact factor: 3.468

5.  Enhancement of mechanical performance by stretch during tetanic contractions of vertebrate skeletal muscle fibres.

Authors:  K A Edman; G Elzinga; M I Noble
Journal:  J Physiol       Date:  1978-08       Impact factor: 5.182

6.  A back-propagation neural network model of lumbar muscle recruitment during moderate static exertions.

Authors:  M A Nussbaum; D B Chaffin; B J Martin
Journal:  J Biomech       Date:  1995-09       Impact factor: 2.712

7.  Electromyography reliability in maximal and submaximal isometric contractions.

Authors:  J F Yang; D A Winter
Journal:  Arch Phys Med Rehabil       Date:  1983-09       Impact factor: 3.966

8.  Isometric tension development in a human skeletal muscle in relation to its working range of movement: the length-tension relation of biceps brachii muscle.

Authors:  H M Ismail; K W Ranatunga
Journal:  Exp Neurol       Date:  1978-12       Impact factor: 5.330

9.  Evaluation of amplitude and frequency components of the surface EMG as an index of muscle fatigue.

Authors:  J S Petrofsky; R M Glaser; C A Phillips; A R Lind; C Williams
Journal:  Ergonomics       Date:  1982-03       Impact factor: 2.778

10.  Method for evaluation of muscle fatigue and endurance from electromyographic fatigue curves.

Authors:  H A DeVries
Journal:  Am J Phys Med       Date:  1968-06
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  8 in total

Review 1.  Surface electromyogram signal modelling.

Authors:  K C McGill
Journal:  Med Biol Eng Comput       Date:  2004-07       Impact factor: 2.602

2.  Modeling nonlinear errors in surface electromyography due to baseline noise: a new methodology.

Authors:  Laura Frey Law; Chandramouli Krishnan; Keith Avin
Journal:  J Biomech       Date:  2010-09-25       Impact factor: 2.712

3.  Error associated with antagonist muscle activity in isometric knee strength testing.

Authors:  Chandramouli Krishnan; Glenn N Williams
Journal:  Eur J Appl Physiol       Date:  2010-02-20       Impact factor: 3.078

Review 4.  Neural adaptations to resistive exercise: mechanisms and recommendations for training practices.

Authors:  David A Gabriel; Gary Kamen; Gail Frost
Journal:  Sports Med       Date:  2006       Impact factor: 11.136

Review 5.  Quantification of quadriceps and hamstring antagonist activity.

Authors:  E Kellis
Journal:  Sports Med       Date:  1998-01       Impact factor: 11.136

6.  Manifestations of muscle fatigue in baseball pitchers: a systematic review.

Authors:  Richard Birfer; Michael Wl Sonne; Michael Wr Holmes
Journal:  PeerJ       Date:  2019-07-29       Impact factor: 2.984

Review 7.  Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.

Authors:  Reed D Gurchiek; Nick Cheney; Ryan S McGinnis
Journal:  Sensors (Basel)       Date:  2019-11-28       Impact factor: 3.576

Review 8.  Critical Appraisal of Surface Electromyography (sEMG) as a Taught Subject and Clinical Tool in Medicine and Kinesiology.

Authors:  Vladimir Medved; Sara Medved; Ida Kovač
Journal:  Front Neurol       Date:  2020-10-26       Impact factor: 4.003

  8 in total

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