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A feature-based processing framework for real-time implementation of muscle fatigue measurement

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Abstract

Electromyographic signals (EMGs) are becoming important as a tool for muscle fatigue monitoring. EMGs measure the electric currents produced in muscle contractions providing information that can be analyzed and processed to evaluate the conditions of muscles. In this work, we proposed a real-time system that measures muscle fatigue levels based on Electromyographic signals. We used the Mean Frequency and the Power Spectral Density as features for muscle fatigue determination. A linear regression model determines the levels of muscle fatigue. Moreover, the system is composed of EMG wireless sensors allowing it to be used in common activities in the manufacturing industry as manual handling loads.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Authors thanks to The Mexican Council of Science and Technology, CONACYT, Mexico, for its support (Grant 236207, CB-2014-01).

Funding

This work was supported by the Consejo Nacional de Ciencia y Tecnología de México (Conacyt-México).

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Authors and Affiliations

  1. Department of Industrial Engineering, Universidad de Sonora, Luis Encinas y Rosales S/N, Col. Centro, Hermosillo, C.P. 83000, Sonora, México

    P. González-Zamora, Victor H. Benitez & Jesus Pacheco

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  1. P. González-Zamora
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  2. Victor H. Benitez
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  3. Jesus Pacheco
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Contributions

All the authors in this paper contribute to the design and development of the system presented here. The full manuscript was written by P. González-Zamora, and all authors contributed with comments on the previous versions.

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Correspondence to Jesus Pacheco.

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González-Zamora, P., Benitez, V.H. & Pacheco, J. A feature-based processing framework for real-time implementation of muscle fatigue measurement. Cluster Comput 26, 385–394 (2023). https://doi.org/10.1007/s10586-021-03437-7

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