Literature DB >> 19719058

Use of MMG signals for the control of powered orthotic devices: development of a rectus femoris measurement protocol.

Michele Gabrio Antonelli1, P Beomonte Zobel, J Giacomin.   

Abstract

A test protocol is defined for the purpose of measuring rectus femoris mechanomyographic (MMG) signals. The protocol is specified in terms of the following: measurement equipment, signal processing requirements, human postural requirements, test rig, sensor placement, sensor dermal fixation, and test procedure. Preliminary tests of the statistical nature of rectus femoris MMG signals were performed, and Gaussianity was evaluated by means of a two-sided Kolmogorov-Smirnov test. For all 100 MMG data sets obtained from the testing of two volunteers, the null hypothesis of Gaussianity was rejected at the 1%, 5%, and 10% significance levels. Most skewness values were found to be greater than 0.0, while all kurtosis values were found to be greater than 3.0. A statistical convergence analysis also performed on the same 100 MMG data sets suggested that 25 MMG acquisitions should prove sufficient to statistically characterize rectus femoris MMG. This conclusion is supported by the qualitative characteristics of the mean rectus femoris MMG power spectral densities obtained using 25 averages.

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Year:  2009        PMID: 19719058     DOI: 10.1080/10400430902945678

Source DB:  PubMed          Journal:  Assist Technol        ISSN: 1040-0435


  5 in total

1.  The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements.

Authors:  Natasha Alves; Tom Chau
Journal:  J Neuroeng Rehabil       Date:  2010-05-21       Impact factor: 4.262

Review 2.  Non-invasive control interfaces for intention detection in active movement-assistive devices.

Authors:  Joan Lobo-Prat; Peter N Kooren; Arno H A Stienen; Just L Herder; Bart F J M Koopman; Peter H Veltink
Journal:  J Neuroeng Rehabil       Date:  2014-12-17       Impact factor: 4.262

3.  Assisted Grasping in Individuals with Tetraplegia: Improving Control through Residual Muscle Contraction and Movement.

Authors:  Lucas Fonseca; Wafa Tigra; Benjamin Navarro; David Guiraud; Charles Fattal; Antônio Bó; Emerson Fachin-Martins; Violaine Leynaert; Anthony Gélis; Christine Azevedo-Coste
Journal:  Sensors (Basel)       Date:  2019-10-18       Impact factor: 3.576

Review 4.  Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation.

Authors:  Jessica Gantenbein; Jan Dittli; Jan Thomas Meyer; Roger Gassert; Olivier Lambercy
Journal:  Front Neurorobot       Date:  2022-02-21       Impact factor: 2.650

5.  A Piezoresistive Sensor to Measure Muscle Contraction and Mechanomyography.

Authors:  Daniele Esposito; Emilio Andreozzi; Antonio Fratini; Gaetano D Gargiulo; Sergio Savino; Vincenzo Niola; Paolo Bifulco
Journal:  Sensors (Basel)       Date:  2018-08-04       Impact factor: 3.576

  5 in total

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