Relationship Analysis of Pre-Task Measurements and Muscle Fatigue Transition Curves for Estimating Muscle Fatigue in Vulnerable Populations

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Conventional methods for measuring muscle fatigue present limitations that do not allow reliable continuous monitoring. The most inconvenient characteristic is employing the Maximum Voluntary Contraction test since it exposes individuals to uncontrollable fatigue, restricts continuous measurement, and provides inaccurate values. This article proposes a continuous muscle fatigue measurement protocol that eliminates the need to administer the MVC test, making fatigue quantification more suitable for vulnerable populations such as the elderly. Relationships between pre-task individual measurements and muscle fatigue temporal curves were evaluated for this. For Biceps Brachii, relevant indicators for defining the curve values include body fat, resting metabolism, arms subcutaneous fat, body age, and Visual Analog Scale (VAS) score for the arm. In the case of the Brachialis muscle, the strongest indicators were body fat, arms skeletal muscle, sub-cutaneous fat, arms subcutaneous fat, height, sleep hours, Fatigue Severity Scale (FSS) score, and VAS score for the arm. For the Brachioradialis muscle, relevant indicators include skeletal muscle, arms skeletal muscle, subcutaneous fat, arms subcutaneous fat, sex, height, and VAS score for the arm.

Original languageEnglish
Title of host publicationProceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024
PublisherIEEE Computer Society
Pages347-352
Number of pages6
ISBN (Electronic)9798331528041
DOIs
StatePublished - 2024
Externally publishedYes
Event23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024 - Hybrid, Miyazaki, Japan
Duration: 20 Sep 202423 Sep 2024

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024
Country/TerritoryJapan
CityHybrid, Miyazaki
Period20/09/2423/09/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Continuous muscle fatigue monitoring
  • Isometric elbow flexion
  • MVC
  • Muscle fatigue
  • Time-frequency analysis
  • sEMG

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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