2008
DOI: 10.1109/tsp.2008.925246
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A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data

Abstract: As the primary noninvasive means to assess muscle activation, the surface electromyogram (sEMG) is of central importance for the study of motor behavior in both clinical and biomedical applications. However, multivariate sEMG analysis is complicated by the fact that data recorded during dynamic contractions are inherently nonstationary. To model this nonstationarity and to determine the dynamic muscle activity patterns during reaching movements, we propose combining hidden Markov models (HMMs) and multivariate… Show more

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Cited by 63 publications
(32 citation statements)
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“…In order to gain better control performance, the primary thing to do is to extract the main feature of the test sEMG signal. At the same time, a sEMG signal is assumed to be the output of a stochastic dynamic process driven by the physiological properties of the muscle and the form of the contraction [7,19]. Also, a hidden Markov model (HMM) is a representation of a type of random process, it can describe time-varying processes in signal processing.…”
Section: Introductionmentioning
confidence: 99%
“…In order to gain better control performance, the primary thing to do is to extract the main feature of the test sEMG signal. At the same time, a sEMG signal is assumed to be the output of a stochastic dynamic process driven by the physiological properties of the muscle and the form of the contraction [7,19]. Also, a hidden Markov model (HMM) is a representation of a type of random process, it can describe time-varying processes in signal processing.…”
Section: Introductionmentioning
confidence: 99%
“…Typically, a sEMG signal movement classification consists on a pattern recognition / classification algorithm, which includes several popular methods such as LDA [2,3], Artificial Neural Networks (ANN) [4,5], Fuzzy Logic [6,7], Neuro Fuzzy [8], Genetic Algorithms, Support Vector Machines [9], Bayesian Networks [10][11][12] and Logistic Regression [13]. There are also some approaches using Independent Component Analysis (ICA) [14] and Principal Component Analysis (PCA) [15,16] focusing on dimensionality reduction and efficient computation, techniques focused on provide more efficiency to classification stage.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, there have been some attempts to apply Hidden Markov Models (HMM) [11] and the Gaussian Mixture Model (GMM) [12] to upperlimb movement classification using myoelectric signals. Moreover, Bayesian approaches have as characteristics being good at assimilate prior data and construct an adaptive process without concrete information [10].…”
Section: Introductionmentioning
confidence: 99%
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“…Different scientists worked for feature extraction using various features like variance (VAR) [15], Willison amplitude (WA) [15], mean absolute value slope (MAVS) [4,16], mean absolute value (MAV) [7,16], number of zero crossings (ZC) [7,16], wave form length (WL) [7,16], number of slope sign changes (SSC) [7,16], root means square (RMS) [26,27], auto regression (AR) coefficients [5,15,31], cepstrum coefficients [18], fast Fourier transform (FFT) coefficients [36], short time Fourier transform coefficients (STFT) [12], wavelet transform coefficients (WT) [19], and wavelet packet transform (WPT) coefficients [6]. Naik et al [27,28] extracted RMS feature from the EMG signal preprocessed with independent component analysis (ICA) to improve the reliability of identification of small movements and gestures of hand.…”
Section: Introductionmentioning
confidence: 99%