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Local binary patterns for noise-tolerant sEMG classification

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Abstract

One-dimensional local binary pattern (1DLBP) has been recently specialized for feature extraction from different types of 1D biological signals. One of the major drawbacks of using 1DLBP, which unavoidably results in classification accuracy reduction, is its noise sensitivity due to the thresholding mechanism. To overcome this deficiency, we have proposed a new one-dimensional noise-tolerant binary pattern (1DNTBP) in this paper. In contrast to 1DLBP, our proposed operator has been defined to use information of a sampling interval as a threshold instead of using central sample value. In order to evaluate 1DNTBP, we applied our proposed feature extraction method on sEMG for basic hand movement dataset. Additionally, a feature selection stage has been considered to perform further noise removal and insignificant patterns reduction process. Hereafter, a variety of classifiers have been tested with the aim of categorizing the selected features. Experimental results indicate that not only does the proposed operator provide noise tolerance, but also it works adaptably well with various classifiers causing it to be a universal operator, sufficiently appropriate to be applied to different applications.

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Authors and Affiliations

  1. Computer Engineering Department, RAZI University, Kermanshah, Iran

    Sayed Mohamad Tabatabaei & Abdolah Chalechale

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  1. Sayed Mohamad Tabatabaei
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  2. Abdolah Chalechale
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Correspondence to Abdolah Chalechale.

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Tabatabaei, S.M., Chalechale, A. Local binary patterns for noise-tolerant sEMG classification. SIViP 13, 491–498 (2019). https://doi.org/10.1007/s11760-018-1374-x

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  • DOI: https://doi.org/10.1007/s11760-018-1374-x

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