EMG pattern recognition via Bayesian inference with scale mixture-based stochastic generative models

Electromyogram (EMG) has been utilized to interface signals for prosthetic hands and information devices owing to its ability to reflect human motion intentions. Although various EMG classification methods have been introduced into EMG-based control systems, they do not fully consider the stochastic...

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Bibliographic Details
Published in:Expert systems with applications Vol. 185; p. 115644
Main Authors: Furui, Akira, Igaue, Takuya, Tsuji, Toshio
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 15.12.2021
Elsevier BV
Subjects:
Bayesian analysis
Bayesian inference
Classification
Classifiers
Control systems
Electromyogram (EMG)
Human motion
Human performance
Machine learning
Motion classification
Pattern classification
Pattern recognition
Probabilistic models
Prostheses
Random variables
Scale mixture model
Statistical inference
Variance
Electromyogram (EMG)
Scale mixture model
Motion classification
Pattern recognition
Bayesian inference
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:Electromyogram (EMG) has been utilized to interface signals for prosthetic hands and information devices owing to its ability to reflect human motion intentions. Although various EMG classification methods have been introduced into EMG-based control systems, they do not fully consider the stochastic characteristics of EMG signals. This paper proposes an EMG pattern classification method incorporating a scale mixture-based generative model. A scale mixture model is a stochastic EMG model in which the EMG variance is considered as a random variable, enabling the representation of uncertainty in the variance. This model is extended in this study and utilized for EMG pattern classification. The proposed method is trained by variational Bayesian learning, thereby allowing the automatic determination of the model complexity. Furthermore, to optimize the hyperparameters of the proposed method with a partial discriminative approach, a mutual information-based determination method is introduced. Simulation and EMG analysis experiments demonstrated the relationship between the hyperparameters and classification accuracy of the proposed method as well as the validity of the proposed method. The comparison using public EMG datasets revealed that the proposed method outperformed the various conventional classifiers. These results indicated the validity of the proposed method and its applicability to EMG-based control systems. In EMG pattern recognition, a classifier based on a generative model that reflects the stochastic characteristics of EMG signals can outperform the conventional general-purpose classifier. •We proposed an EMG pattern classification method based on a stochastic EMG model.•The proposed model is trained by variational Bayesian learning.•The hyperparameter can be determined by mutual information maximization.•Simulations, EMG analysis, and classification experiments were conducted.•The proposed method outperformed general generative/discriminative classifiers.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115644