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Mechanomyographic parameter extraction methods: an appraisal for clinical applications.
Morufu Olusola Ibitoye1, Nur Azah Hamzaid2, Jorge M Zuniga3
1Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia. marufibitoye@yahoo.com.
Sensors (Basel, Switzerland)
|December 6, 2014
Summary
Mechanomyographic (MMG) signals offer a reliable alternative for assessing skeletal muscle performance, including force and endurance. This review details MMG parameter extraction methods, highlighting their potential for clinical and experimental applications.
Area of Science:
- Biomedical Engineering
- Sports Science
- Rehabilitation Medicine
Background:
- Skeletal muscle performance evaluation traditionally relies on various methods.
- Mechanomyographic (MMG) signals have emerged as a viable alternative for assessing muscle function.
- MMG signals reflect muscle activity during voluntary and evoked contractions.
Purpose of the Study:
- To review and appraise methods for extracting parameters from mechanomyographic (MMG) signals.
- To highlight the reliability and applications of MMG in muscle performance assessment.
- To identify limitations and suggest future directions for MMG signal analysis.
Main Methods:
- Literature review of studies utilizing mechanomyographic (MMG) signals.
- Analysis of signal features and their correlation with muscle performance indices.
- Evaluation of measurement theories and parameter extraction techniques.
Main Results:
- MMG signal parameters reliably assess muscle performance metrics like force, power, and endurance.
- MMG is suitable for both voluntary and stimulus-evoked muscle contractions.
- Established methods for MMG parameter extraction are presented, along with their limitations.
Conclusions:
- Mechanomyographic (MMG) signals provide a valuable, non-invasive tool for muscle performance assessment.
- Further development of MMG parameter extraction can enhance its clinical and experimental utility.
- MMG offers potential in musculoskeletal assessments and real-time muscle activity detection, especially where electromyography is limited.
