Literature DB >> 11018441

Surface EMG models: properties and applications.

D F Stegeman1, J H Blok, H J Hermens, K Roeleveld.   

Abstract

After a general introduction on the kind of models and the use of models in the natural sciences, the main body of this paper reviews potential properties of structure based surface EMG (sEMG) models. The specific peculiarities of the categories (i) source description, (ii) motor unit structure, (iii) volume conduction, (iv) recording configurations and (v) recruitment and firing behaviour are discussed. For a specific goal, not all aspects conceivable have to be part of a model description. Therefore, finally an attempt is made to integrate the 'question level' and the 'model property level' in a matrix providing direction to the development and application of sEMG models with different characteristics and varying complexity. From this overview it appears that the least complex are models describing how the morphological muscle features are reflected in multi-channel EMG measurements. The most challenging questions in terms of model complexity are related to supporting the diagnosis of neuromuscular disorders.

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Year:  2000        PMID: 11018441     DOI: 10.1016/s1050-6411(00)00023-7

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


  20 in total

1.  Influence of the subcutaneous fat layer, as measured by ultrasound, skinfold calipers and BMI, on the EMG amplitude.

Authors:  C Nordander; J Willner; G-A Hansson; B Larsson; J Unge; L Granquist; S Skerfving
Journal:  Eur J Appl Physiol       Date:  2003-04-24       Impact factor: 3.078

Review 2.  Surface electromyogram signal modelling.

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

3.  Advances in surface electromyographic signal simulation with analytical and numerical descriptions of the volume conductor.

Authors:  D Farina; L Mesin; S Martina
Journal:  Med Biol Eng Comput       Date:  2004-07       Impact factor: 2.602

4.  Comparison of spatial filter selectivity in surface myoelectric signal detection: influence of the volume conductor model.

Authors:  D Farina; L Mesin; S Martina; R Merletti
Journal:  Med Biol Eng Comput       Date:  2004-01       Impact factor: 2.602

5.  On-line signal quality estimation of multichannel surface electromyograms.

Authors:  C Grönlund; K Roeleveld; A Holtermann; J S Karlsson
Journal:  Med Biol Eng Comput       Date:  2005-05       Impact factor: 2.602

6.  A simulation study for a surface EMG sensor that detects distinguishable motor unit action potentials.

Authors:  Jin Lee; Alexander Adam; Carlo J De Luca
Journal:  J Neurosci Methods       Date:  2007-09-18       Impact factor: 2.390

7.  Epoch length to accurately estimate the amplitude of interference EMG is likely the result of unavoidable amplitude cancellation.

Authors:  Kevin G Keenan; Francisco J Valero-Cuevas
Journal:  Biomed Signal Process Control       Date:  2008-04       Impact factor: 3.880

8.  Detecting the unique representation of motor-unit action potentials in the surface electromyogram.

Authors:  Dario Farina; Francesco Negro; Marco Gazzoni; Roger M Enoka
Journal:  J Neurophysiol       Date:  2008-05-21       Impact factor: 2.714

9.  Computational model to investigate the relative contributions of different neuromuscular properties of tibialis anterior on force generated during ankle dorsiflexion.

Authors:  Ariba Siddiqi; Sridhar Poosapadi Arjunan; Dinesh Kant Kumar
Journal:  Med Biol Eng Comput       Date:  2018-01-16       Impact factor: 2.602

10.  Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model.

Authors:  Vincent Carriou; Sofiane Boudaoud; Jeremy Laforet
Journal:  Med Biol Eng Comput       Date:  2018-01-23       Impact factor: 2.602

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