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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
JournalCluster Computing
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Bibliographical note

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

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

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Biomedical signals
  • EMG signals
  • Occupational medicine

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