Literature DB >> 22163515

Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals.

Ramon de la Rosa1, Alonso Alonso, Albano Carrera, Ramon Durán, Patricia Fernández.   

Abstract

This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.

Entities:  

Keywords:  biological control systems; electromyography; mechanomyography; pattern classification; real-time systems; sensors; training

Mesh:

Year:  2010        PMID: 22163515      PMCID: PMC3231077          DOI: 10.3390/s101211100

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  18 in total

1.  A wavelet-based continuous classification scheme for multifunction myoelectric control.

Authors:  K Englehart; B Hudgins; P A Parker
Journal:  IEEE Trans Biomed Eng       Date:  2001-03       Impact factor: 4.538

2.  Adaptive whitening of the electromyogram to improve amplitude estimation.

Authors:  E A Clancy; K A Farry
Journal:  IEEE Trans Biomed Eng       Date:  2000-06       Impact factor: 4.538

3.  AC-coupled front-end for biopotential measurements.

Authors:  Enrique Mario Spinelli; Ramon Pallàs-Areny; Miguel Angel Mayosky
Journal:  IEEE Trans Biomed Eng       Date:  2003-03       Impact factor: 4.538

4.  A signal-to-noise investigation of nonlinear electromyographic processors.

Authors:  J G Kreifeldt; S Yao
Journal:  IEEE Trans Biomed Eng       Date:  1974-07       Impact factor: 4.538

5.  A new strategy for multifunction myoelectric control.

Authors:  B Hudgins; P Parker; R N Scott
Journal:  IEEE Trans Biomed Eng       Date:  1993-01       Impact factor: 4.538

6.  Multichannel PC-based data-acquisition system for high-resolution EEG.

Authors:  W J Dunseath; E F Kelly
Journal:  IEEE Trans Biomed Eng       Date:  1995-12       Impact factor: 4.538

7.  Amplifiers for bioelectric events: a design with a minimal number of parts.

Authors:  A C MettingVanRijn; A Peper; C A Grimbergen
Journal:  Med Biol Eng Comput       Date:  1994-05       Impact factor: 2.602

8.  Some theoretic results on a digital EMG signal processor.

Authors:  G C Filligoi; P Mandarini
Journal:  IEEE Trans Biomed Eng       Date:  1984-04       Impact factor: 4.538

9.  Myoelectric signal processing: optimal estimation applied to electromyography--Part II: experimental demonstration of optimal myoprocessor performance.

Authors:  N Hogan; R W Mann
Journal:  IEEE Trans Biomed Eng       Date:  1980-07       Impact factor: 4.538

10.  Myoelectric signal processing: optimal estimation applied to electromyography--Part I: derivation of the optimal myoprocessor.

Authors:  N Hogan; R W Mann
Journal:  IEEE Trans Biomed Eng       Date:  1980-07       Impact factor: 4.538

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  7 in total

Review 1.  Brain computer interfaces, a review.

Authors:  Luis Fernando Nicolas-Alonso; Jaime Gomez-Gil
Journal:  Sensors (Basel)       Date:  2012-01-31       Impact factor: 3.576

2.  Programmable gain amplifiers with DC suppression and low output offset for bioelectric sensors.

Authors:  Albano Carrera; Ramón de la Rosa; Alonso Alonso
Journal:  Sensors (Basel)       Date:  2013-09-27       Impact factor: 3.576

3.  Steering a tractor by means of an EMG-based human-machine interface.

Authors:  Jaime Gomez-Gil; Israel San-Jose-Gonzalez; Luis Fernando Nicolas-Alonso; Sergio Alonso-Garcia
Journal:  Sensors (Basel)       Date:  2011-07-11       Impact factor: 3.576

4.  Mechanomyographic parameter extraction methods: an appraisal for clinical applications.

Authors:  Morufu Olusola Ibitoye; Nur Azah Hamzaid; Jorge M Zuniga; Nazirah Hasnan; Ahmad Khairi Abdul Wahab
Journal:  Sensors (Basel)       Date:  2014-12-03       Impact factor: 3.576

Review 5.  Surface electromyography signal processing and classification techniques.

Authors:  Rubana H Chowdhury; Mamun B I Reaz; Mohd Alauddin Bin Mohd Ali; Ashrif A A Bakar; K Chellappan; T G Chang
Journal:  Sensors (Basel)       Date:  2013-09-17       Impact factor: 3.576

6.  Design, development and testing of a low-cost sEMG system and its use in recording muscle activity in human gait.

Authors:  Tamara Grujic Supuk; Ana Kuzmanic Skelin; Maja Cic
Journal:  Sensors (Basel)       Date:  2014-05-07       Impact factor: 3.576

7.  Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

Authors:  Karina de O A de Moura; Alexandre Balbinot
Journal:  Sensors (Basel)       Date:  2018-05-01       Impact factor: 3.576

  7 in total

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