Literature DB >> 19747943

Relationship between grasping force and features of single-channel intramuscular EMG signals.

Ernest Nlandu Kamavuako1, Dario Farina, Ken Yoshida, Winnie Jensen.   

Abstract

The surface electromyographic (sEMG) signal can be used for force prediction and control in prosthetic devices. Because of technological advances on implantable sensors, the use of intramuscular EMG (iEMG) is becoming a potential alternative to sEMG for the control of multiple degrees-of-freedom (DOF). An invasive system is not affected by crosstalk, typical of sEMG, and provides more stable and independent control sites. However, intramuscular recordings provide more local information because of their high selectivity, and may thus be less representative of the global muscle activity with respect to sEMG. This study investigates the capacity of selective single-channel iEMG recordings to represent the grasping force with respect to the use of sEMG with the aim of assessing if iEMG can be an effective method for proportional myoelectric control. sEMG and iEMG were recorded concurrently from 10 subjects who exerted six grasping force profiles from 0 to 25/50N. The linear correlation coefficient between features extracted from iEMG and force was approximately 0.9 and was not significantly different from the degree of correlation between sEMG and force. This result indicates that a selective iEMG recording is representative of the applied grasping force and can be used for proportional control.

Mesh:

Year:  2009        PMID: 19747943     DOI: 10.1016/j.jneumeth.2009.09.006

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  5 in total

1.  Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model.

Authors:  Mylena Mordhorst; Thomas Heidlauf; Oliver Röhrle
Journal:  Interface Focus       Date:  2015-04-06       Impact factor: 3.906

2.  Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders.

Authors:  Ercan Gokgoz; Abdulhamit Subasi
Journal:  J Med Syst       Date:  2014-04-03       Impact factor: 4.460

3.  Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography.

Authors:  Nebojsa Malesevic; Anders Björkman; Gert S Andersson; Christian Cipriani; Christian Antfolk
Journal:  Sensors (Basel)       Date:  2022-07-05       Impact factor: 3.847

4.  A biomechanical study of spherical grip.

Authors:  Jaime Martin-Martin; Antonio I Cuesta-Vargas
Journal:  Springerplus       Date:  2013-11-04

5.  A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles.

Authors:  Yina Wang; Liwei Zheng; Junyou Yang; Shuoyu Wang
Journal:  Sensors (Basel)       Date:  2022-03-04       Impact factor: 3.576

  5 in total

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