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On the detection of activity patterns in electromyographic signals via decision trees

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Abstract

In the present work, decision trees are employed to determine the activation patterns in electromyographic signals in multiple muscles of interest. Due to the interaction of several muscles when performing a movement, it is common for several muscles to activate simultaneously. The problem is to determine if any muscle of interest is active and, in the case of multiple active muscles, which of them is the predominant one. In turn, we are interested in determining if a muscle is active enough to associate it with a voluntary movement. The proposed scheme is based on decision trees (which are supervised learning strategies for classification), and the envelope of a signal as a classification feature. The detection of an activation is performed in two stages: the first stage determines the candidate muscle to be active and a second stage determines its activation level. The algorithm was tested in control of a robotic arm in real time. The activation of a muscle is associated with a control command of a DC motor of the robotic arm. The proposed algorithm made it possible to manipulate the movements of that robotic arm to perform a simple task using electromyographic signals.

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Data availability

Data available upon reasonable request from the corresponding author (J.E.M.-D.)

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Acknowledgements

The authors wish to thank the associate editor in charge of handling this paper as well as the anonymous reviewers, for the time and efforts reviewing this manuscript. Thanks to their constructive criticisms and suggestions, this work was improved substantially.

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Author notes

  1. V. Ramírez-Pérez, J. A. Guerrero-Díaz-de-León and J. E. Macías-Díaz: These authors contributed equally to this work.

Authors and Affiliations

  1. Centro de Ciencias Básicas, Universidad Autónoma de Aguascalientes, Avenida Universidad 940, Ciudad Universitaria, Aguascalientes, 20100, Ags., Mexico

    Vanessa Ramírez-Pérez

  2. Departamento de Estadística, Universidad Autónoma de Aguascalientes, Avenida Universidad 940, Ciudad Universitaria, Aguascalientes, 20100, Ags., Mexico

    José A. Guerrero-Díaz-de-León

  3. Department of Mathematics and Didactics of Mathematics, Tallinn University, Narva mnt 25, Tallinn, 10120, Estonia

    Jorge E. Macías-Díaz

  4. Departamento de Matemáticas y Física, Universidad Autónoma de Aguascalientes, Avenida Universidad 940, Ciudad Universitaria, Aguascalientes, 20100, Ags., Mexico

    Jorge E. Macías-Díaz

Authors
  1. Vanessa Ramírez-Pérez
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  2. José A. Guerrero-Díaz-de-León
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  3. Jorge E. Macías-Díaz
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The authors contributed equally to the work.

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Correspondence to Jorge E. Macías-Díaz.

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The authors declare that they have no conflict of interest.

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The corresponding author (J.E.M.-D.) acknowledges the financial support from the National Council of Science and Technology of Mexico (CONACYT) through grant A1-S-45928.

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Code available on request from the corresponding author.

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Ramírez-Pérez, V., Guerrero-Díaz-de-León, J.A. & Macías-Díaz, J.E. On the detection of activity patterns in electromyographic signals via decision trees. Evol. Intel. 17, 577–588 (2024). https://doi.org/10.1007/s12065-023-00844-0

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