Next Article in Journal
A Split G-Quadruplex and Graphene Oxide-Based Low-Background Platform for Fluorescence Authentication of Pseudostellaria heterophylla
Next Article in Special Issue
Monitoring Lipase/Esterase Activity by Stopped Flow in a Sequential Injection Analysis System Using p-Nitrophenyl Butyrate
Previous Article in Journal
A New Crank Arm-Based Load Cell for the 3D Analysis of the Force Applied by a Cyclist
Previous Article in Special Issue
Study of a Liquid Plug-Flow Thermal Cycling Technique Using a Temperature Gradient-Based Actuator
Article Menu

Export Article

Open AccessReview
Sensors 2014, 14(12), 22940-22970; doi:10.3390/s141222940

Mechanomyographic Parameter Extraction Methods: An Appraisal for Clinical Applications

1
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
2
Department of Biomedical Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, P.M.B. 1515, Nigeria
3
Department of Exercise Science, Creighton University, 2500 California Plaza, Kiewit Fitness Center 228, Omaha, NE 68178, USA
4
Department of Rehabilitation Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Received: 20 August 2014 / Revised: 28 October 2014 / Accepted: 4 November 2014 / Published: 3 December 2014
(This article belongs to the Special Issue Sensors for Bioprocess Monitoring and Control)
View Full-Text   |   Download PDF [1087 KB, uploaded 3 December 2014]   |  

Abstract

The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity. View Full-Text
Keywords: mechanomyographic signal; MMG parameters; muscle performance; isokinetic; isometric; electromyogram mechanomyographic signal; MMG parameters; muscle performance; isokinetic; isometric; electromyogram
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ibitoye, M.O.; Hamzaid, N.A.; Zuniga, J.M.; Hasnan, N.; Wahab, A.K.A. Mechanomyographic Parameter Extraction Methods: An Appraisal for Clinical Applications. Sensors 2014, 14, 22940-22970.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top