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, 19 (3)

Hand Movement Classification Using Burg Reflection Coefficients

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Hand Movement Classification Using Burg Reflection Coefficients

Daniel Ramírez-Martínez et al. Sensors (Basel).

Abstract

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.

Keywords: classification algorithms; electromyography; feature selection; hand movement; health monitoring; machine learning; maximum entropy reflection coefficients.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
On top, the original signal; on the bottom, the clipped signal with a zero mean value.
Figure 2
Figure 2
Dataset building block diagram.
Figure 3
Figure 3
Lattice filter prediction cascade diagram.
Figure 4
Figure 4
Accuracy rate achieved by the different classifiers.
Figure 5
Figure 5
WAUC achieved by the different classifiers.
Figure 6
Figure 6
Sensitivity achieved by the different classifiers.
Figure 7
Figure 7
Specificity achieved by the different classifiers.

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