Literature DB >> 19758823

Methodological aspects of SEMG recordings for force estimation--a tutorial and review.

Didier Staudenmann1, Karin Roeleveld, Dick F Stegeman, Jaap H van Dieën.   

Abstract

Insight into the magnitude of muscle forces is important in biomechanics research, for example because muscle forces are the main determinants of joint loading. Unfortunately muscle forces cannot be calculated directly and can only be measured using invasive procedures. Therefore, estimates of muscle force based on surface EMG measurements are frequently used. This review discusses the problems associated with surface EMG in muscle force estimation and the solutions that novel methodological developments provide to this problem. First, some basic aspects of muscle activity and EMG are reviewed and related to EMG amplitude estimation. The main methodological issues in EMG amplitude estimation are precision and representativeness. Lack of precision arises directly from the stochastic nature of the EMG signal as the summation of a series of randomly occurring polyphasic motor unit potentials and the resulting random constructive and destructive (phase cancellation) superimpositions. Representativeness is an issue due the structural and functional heterogeneity of muscles. Novel methods, i.e. multi-channel monopolar EMG and high-pass filtering or whitening of conventional bipolar EMG allow substantially less variable estimates of the EMG amplitude and yield better estimates of muscle force by (1) reducing effects of phase cancellation, and (2) adequate representation of the heterogeneous activity of motor units within a muscle. With such methods, highly accurate predictions of force, even of the minute force fluctuations that occur during an isometric and isotonic contraction have been achieved. For dynamic contractions, EMG-based force estimates are confounded by the effects of muscle length and contraction velocity on force producing capacity. These contractions require EMG amplitude estimates to be combined with modeling of muscle contraction dynamics to achieve valid force predictions. (c) 2009 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2009        PMID: 19758823     DOI: 10.1016/j.jelekin.2009.08.005

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


  57 in total

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2.  On the nature of the electromyographic signals recorded during vibration exercise.

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Journal:  Eur J Appl Physiol       Date:  2015-01-10       Impact factor: 3.078

3.  Modifying motor unit territory placement in the Fuglevand model.

Authors:  Jason W Robertson; Jamie A Johnston
Journal:  Med Biol Eng Comput       Date:  2017-04-08       Impact factor: 2.602

4.  Effect of 3 Different Applications of Kinesio Taping Denko® on Electromyographic Activity: Inhibition or Facilitation of the Quadriceps of Males During Squat Exercise.

Authors:  Júlio C Serrão; Bruno Mezêncio; João G Claudino; Rafael Soncin; Pedro L Sampaio Miyashiro; Eric P Sousa; Eduardo Borges; Vinícius Zanetti; Igor Phillip; Luiz Mochizuki; Alberto C Amadio
Journal:  J Sports Sci Med       Date:  2016-08-05       Impact factor: 2.988

5.  Weight bearing exercise can elicit similar peak muscle activation as medium-high intensity resistance exercise in elderly women.

Authors:  Remco J Baggen; Evelien Van Roie; Jaap H van Dieën; Sabine M Verschueren; Christophe Delecluse
Journal:  Eur J Appl Physiol       Date:  2017-12-30       Impact factor: 3.078

6.  Validity and Reliability of Surface Electromyography Measurements from a Wearable Athlete Performance System.

Authors:  Scott K Lynn; Casey M Watkins; Megan A Wong; Katherine Balfany; Daniel F Feeney
Journal:  J Sports Sci Med       Date:  2018-05-14       Impact factor: 2.988

7.  A comparison of a maximum exertion method and a model-based, sub-maximum exertion method for normalizing trunk EMG.

Authors:  Jacek Cholewicki; Jaap van Dieën; Angela S Lee; N Peter Reeves
Journal:  J Electromyogr Kinesiol       Date:  2011-06-12       Impact factor: 2.368

8.  Sex differences in muscular load among house painters performing identical work tasks.

Authors:  Jacob Meyland; Thomas Heilskov-Hansen; Tine Alkjær; Henrik Koblauch; Sigurd Mikkelsen; Susanne Wulff Svendsen; Jane Frølund Thomsen; Gert-Åke Hansson; Erik B Simonsen
Journal:  Eur J Appl Physiol       Date:  2014-06-07       Impact factor: 3.078

9.  Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors.

Authors:  Sridhar Poosapadi Arjunan; Dinesh Kant Kumar
Journal:  J Neuroeng Rehabil       Date:  2010-10-21       Impact factor: 4.262

10.  A model-based approach for estimation of changes in lumbar segmental kinematics associated with alterations in trunk muscle forces.

Authors:  Iman Shojaei; Navid Arjmand; Judith R Meakin; Babak Bazrgari
Journal:  J Biomech       Date:  2017-10-06       Impact factor: 2.712

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