Literature DB >> 11018444

EMG signals detection and processing for on-line control of functional electrical stimulation.

C Frigo1, M Ferrarin, W Frasson, E Pavan, R Thorsen.   

Abstract

The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional neuromuscular electrical stimulation. A proper positioning of the recording electrodes in relation to the stimulation electrodes, and a proper processing of the recorded signals is required to reduce the stimulus artefact and the non-voluntary contribution (M-wave). Six orientations and six locations of the recording electrodes were investigated in the present work. A comb filter (with and without a blanking windowing) was applied to remove the signal components synchronously correlated to the stimulus. An operative definition of the signal to noise ratio and an efficiency index were implemented. It resulted that when the recording electrodes were located within the two stimulation electrodes the best orientation was perpendicular to the longitudinal line. However the best absolute indexes were obtained when the recording electrodes were located externally of the stimulation electrodes, and in that case the best orientation was longitudinal. Concerning the filtering procedure, the use of a blanking window before the application of the comb filter, gave the best performance.

Mesh:

Year:  2000        PMID: 11018444     DOI: 10.1016/s1050-6411(00)00026-2

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


  19 in total

1.  A phenomenological model that predicts forces generated when electrical stimulation is superimposed on submaximal volitional contractions.

Authors:  Ramu Perumal; Anthony S Wexler; Trisha M Kesar; Angela Jancosko; Yocheved Laufer; Stuart A Binder-Macleod
Journal:  J Appl Physiol (1985)       Date:  2010-03-18

2.  Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.

Authors:  Gang Wang; Zhizhong Wang; Weiting Chen; Jun Zhuang
Journal:  Med Biol Eng Comput       Date:  2006-09-02       Impact factor: 2.602

3.  Classification of surface electromyographic signals by means of multifractal singularity spectrum.

Authors:  Gang Wang; Doutian Ren
Journal:  Med Biol Eng Comput       Date:  2012-11-07       Impact factor: 2.602

4.  An Epidermal Stimulation and Sensing Platform for Sensorimotor Prosthetic Control, Management of Lower Back Exertion, and Electrical Muscle Activation.

Authors:  Baoxing Xu; Aadeel Akhtar; Yuhao Liu; Hang Chen; Woon-Hong Yeo; Sung Ii Park; Brandon Boyce; Hyunjin Kim; Jiwoo Yu; Hsin-Yen Lai; Sungyoung Jung; Yuhao Zhou; Jeonghyun Kim; Seongkyu Cho; Yonggang Huang; Timothy Bretl; John A Rogers
Journal:  Adv Mater       Date:  2015-10-15       Impact factor: 30.849

5.  Unsupervised Stochastic Strategies for Robust Detection of Muscle Activation Onsets in Surface Electromyogram.

Authors:  S Easter Selvan; Didier Allexandre; Umberto Amato; Guang H Yue
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-06       Impact factor: 3.802

6.  Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control.

Authors:  Hojun Yeom; Young-Hui Chang
Journal:  J Neurosci Methods       Date:  2010-08-14       Impact factor: 2.390

7.  Interaction of poststroke voluntary effort and functional neuromuscular electrical stimulation.

Authors:  Nathaniel Makowski; Jayme Knutson; John Chae; Patrick Crago
Journal:  J Rehabil Res Dev       Date:  2013

8.  Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation.

Authors:  Eran Langzam; Eli Isakov; Joseph Mizrahi
Journal:  J Neuroeng Rehabil       Date:  2006-11-23       Impact factor: 4.262

9.  The analysis of surface EMG signals with the wavelet-based correlation dimension method.

Authors:  Gang Wang; Yanyan Zhang; Jue Wang
Journal:  Comput Math Methods Med       Date:  2014-04-27       Impact factor: 2.238

Review 10.  Hybrid soft computing systems for electromyographic signals analysis: a review.

Authors:  Hong-Bo Xie; Tianruo Guo; Siwei Bai; Socrates Dokos
Journal:  Biomed Eng Online       Date:  2014-02-03       Impact factor: 2.819

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