Research Papers

Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis

[+] Author and Article Information
Kiran Marri

Non Invasive Imaging and Diagnostics Lab,
Biomedical Engineering Group,
Department of Applied Mechanics,
Indian Institute of Technology Madras,
Chennai 600 036, India
e-mail: kirankmr@gmail.com

Ramakrishnan Swaminathan

Non Invasive Imaging and Diagnostics Lab,
Biomedical Engineering Group,
Department of Applied Mechanics,
Indian Institute of Technology Madras,
Chennai 600 036, India
e-mail: sramki@iitm.ac.in

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 5, 2015; final manuscript received February 23, 2016; published online July 15, 2016. Assoc. Editor: Hashem Ashrafiuon.

J. Dyn. Sys., Meas., Control 138(11), 111008 (Jul 15, 2016) (10 pages) Paper No: DS-15-1483; doi: 10.1115/1.4033832 History: Received October 05, 2015; Revised February 23, 2016

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.

Copyright © 2016 by ASME
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Fig. 1

Overall methodology for nonfatigue and fatigue studies

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Fig. 2

Representative sEMG signals recorded from biceps brachii muscles of (a) subject A and (b) subject B

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Fig. 3

Multifractal spectrum of sEMG signals in nonfatigue and fatigue conditions for (a) subject A and (b) subject B

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Fig. 4

Mean percentage differences of features in nonfatigue and fatigue conditions

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Fig. 5

Box plot of (a) SOM, (b) TSE-5, (c) WSE, and (d) DVS in nonfatigue and fatigue conditions. Central bar indicates median amplitude, central dot indicates mean, bottom and top of box indicate 25th and 75th percentiles, respectively, and extended lines indicate range (outliers are shown with cross).

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Fig. 6

Multifractal spectral features ranked using Wilcoxon rank test

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Fig. 7

Top 20 multifractal spectrum features using IG (light gray), GA (black), and Wilcoxon rank test (dark gray)

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Fig. 8

Performance of k-NN classifier with different distance methods

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Fig. 9

Three types of multifractal spectrum scenarios that the classifier did not predict accurately: (a) similar SOM, (b) similar maximum exponent, and (c) similar PEV and exponent




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