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Classification of Biceps Brachii Muscle Fatigue Condition Using Phase Space Network Features
Navaneethakrishna Makaram1, Ramakrishnan Swaminathan1
1Non-Invasive Imaging and Diagnostics laboratory, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India - 600036.
Studies in Health Technology and Informatics
|June 24, 2020
Summary
This study differentiates muscle fatigue from non-fatigue states using signal complexity and phase space network analysis. The method achieved 91% accuracy, offering potential for diagnosing neuromuscular disorders.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Signal Processing
Background:
- Muscle fatigue is a complex physiological state affecting performance.
- Current methods for assessing muscle fatigue can be invasive or subjective.
- Objective, non-invasive methods are needed for accurate fatigue assessment.
Purpose of the Study:
- To differentiate between muscle non-fatigue and fatigue conditions.
- To explore the utility of signal complexity metrics from phase space networks for fatigue detection.
- To establish a quantitative method for assessing muscle fatigue.
Main Methods:
- Healthy volunteers (n=55) performed dynamic biceps brachii contractions.
- Non-fatigue (first curl) and fatigue (last curl) states were analyzed.
- Weighted phase space networks were constructed and reduced to binary networks.
- Mean and median degree centrality features were extracted for classification.
Main Results:
- Signal complexity metrics derived from phase space networks effectively differentiated non-fatigue and fatigue states.
- Classification accuracy reached 91% using mean and median degree centrality.
- The developed method demonstrates high efficacy in detecting muscle fatigue.
Conclusions:
- Phase space network analysis provides a robust method for distinguishing muscle fatigue.
- This approach shows promise for objective, non-invasive fatigue monitoring.
- The technique can be extended to diagnose neuromuscular disorders characterized by fatigue.
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