Abstract
This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification.
Highlights
This paper proposed an artificial EMG generation model based on signal-dependent noise
The proposed model estimates the variance distribution of EMG signals using the inverse gamma distribution, and generates artificial EMG signals with signal-dependent noise superimposed according to muscle activation levels
The evaluations conducted on the generated artificial EMG signals and the comparison in terms of amplitude, frequent component, and kurtosis of EMG distribution revealed that the proposed variance distribution-based generation method can reproduce the features of the measured EMG signals during isometric muscle contraction
Summary
De Luca [8] derived EMG signal features such as the mean rectified value, the root mean square value, and the variance of the rectified signals from the stochastic properties of the inter-pulse interval and the motor unit action potential train (MUAPT). This was done based on a mathematical model derived with the underlying assumption that EMG signals are expressed as the sum of MUAPTs. Hogan and Mann [6, 7] modeled the relationship between muscle force and EMG signals based on a Gaussian white noise process with a zero mean and variance nonlinearly depending on muscle force. Inspired by this underlying concept, Hayashi et al [13] expanded Hogan and Mann’s model by assuming that signal-dependent noise [14, 15] is superimposed according to muscle activation levels onto the EMG signal variance, resulting in an expression of the variance as a random variable following an inverse gamma distribution
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