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Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis
Published in American Society of Mechanical Engineers (ASME)
2016
Volume: 138
   
Issue: 11
Abstract
Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series. © 2016 by ASME.
About the journal
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
PublisherAmerican Society of Mechanical Engineers (ASME)
ISSN00220434
Open AccessNo
Concepts (21)
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    Classification (of information)
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    Electromyography
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    Fractals
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    Frequency domain analysis
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    Genetic algorithms
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    Muscle
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    Nearest neighbor search
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    Physiological models
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    Signal theory
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    Spectrum analysis
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    SPORTS MEDICINE
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    Time series analysis
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    Classification algorithm
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    DETRENDED MOVING AVERAGE
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    Feature selection methods
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    MUSCLE FATIGUES
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    SINGULARITY SPECTRUM
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    SURFACE ELECTROMYOGRAPHY
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    SURFACE ELECTROMYOGRAPHY SIGNALS
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    Time and frequency domains
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    Biomedical signal processing