Literature DB >> 23481867

Surface versus untargeted intramuscular EMG based classification of simultaneous and dynamically changing movements.

Ernest Nlandu Kamavuako, Jakob Celander Rosenvang, Ronnie Horup, Winnie Jensen, Dario Farina, Kevin B Englehart.   

Abstract

The pattern recognition-based myoelectric control scheme is in the process of being implemented in clinical settings, but it has been mainly tested on sequential and steady state data. This paper investigates the ability of pattern recognition to resolve movements that are simultaneous and dynamically changing and compares the use of surface and untargeted intramuscular EMG signals for this purpose. Ten able-bodied subjects participated in the study. Both EMG types were recorded concurrently from the right forearm. The subjects were instructed to track dynamic contraction profiles using single and combined degrees of freedom in three trials. During trials one and two, the amplitude and the frequency of the profile were kept constant (nonmodulated data), and during trial three, the two parameters were modulated (modulated data). The results showed that the performance was up to 93% for nonmodulated tasks, but highly depended on the nature of the data used. Surface and untargeted intramuscular EMG had equal performance for data of similar nature (nonmodulated), but the performance of intramuscular EMG decreased, compared to surface, when tested on modulated data. However, the results of intramuscular recordings obtained in this study are promising for future use of implantable electrodes, because, besides the value added in terms of potential chronic implantation, the performance is theoretically the same as for surface EMG provided that enough information is captured in the recordings. Nevertheless, care should be taken when training the system since data obtained from selective recordings probably need more training data to generalize to new signals.

Mesh:

Year:  2013        PMID: 23481867     DOI: 10.1109/TNSRE.2013.2248750

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  8 in total

1.  Real-time simultaneous and proportional myoelectric control using intramuscular EMG.

Authors:  Lauren H Smith; Todd A Kuiken; Levi J Hargrove
Journal:  J Neural Eng       Date:  2014-11-14       Impact factor: 5.379

2.  Myoelectric Control System and Task-Specific Characteristics Affect Voluntary Use of Simultaneous Control.

Authors:  Lauren H Smith; Todd A Kuiken; Levi J Hargrove
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2015-03-06       Impact factor: 3.802

3.  Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

Authors:  Jose Gonzalez-Vargas; Strahinja Dosen; Sebastian Amsuess; Wenwei Yu; Dario Farina
Journal:  PLoS One       Date:  2015-06-12       Impact factor: 3.240

4.  Tibialis anterior analysis from functional and architectural perspective during isometric foot dorsiflexion: a cross-sectional study of repeated measures.

Authors:  Maria Ruiz Muñoz; Manuel González-Sánchez; Antonio I Cuesta-Vargas
Journal:  J Foot Ankle Res       Date:  2015-12-18       Impact factor: 2.303

Review 5.  EMG Processing Based Measures of Fatigue Assessment during Manual Lifting.

Authors:  E F Shair; S A Ahmad; M H Marhaban; S B Mohd Tamrin; A R Abdullah
Journal:  Biomed Res Int       Date:  2017-02-19       Impact factor: 3.411

6.  Online mapping of EMG signals into kinematics by autoencoding.

Authors:  Ivan Vujaklija; Vahid Shalchyan; Ernest N Kamavuako; Ning Jiang; Hamid R Marateb; Dario Farina
Journal:  J Neuroeng Rehabil       Date:  2018-03-13       Impact factor: 4.262

7.  Muscle Activation in Middle-Distance Athletes with Compression Stockings.

Authors:  Diego Moreno-Pérez; Pedro J Marín; Álvaro López-Samanes; Roberto Cejuela; Jonathan Esteve-Lanao
Journal:  Sensors (Basel)       Date:  2020-02-26       Impact factor: 3.576

8.  A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal.

Authors:  Mehmet Baygin; Prabal Datta Barua; Sengul Dogan; Turker Tuncer; Sefa Key; U Rajendra Acharya; Kang Hao Cheong
Journal:  Sensors (Basel)       Date:  2022-03-04       Impact factor: 3.576

  8 in total

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