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 language | English |
|---|---|
| Title of host publication | Proceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024 |
| Publisher | IEEE Computer Society |
| Pages | 347-352 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798331528041 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024 - Hybrid, Miyazaki, Japan Duration: 20 Sep 2024 → 23 Sep 2024 |
Publication series
| Name | Proceedings - International Conference on Machine Learning and Cybernetics |
|---|---|
| ISSN (Print) | 2160-133X |
| ISSN (Electronic) | 2160-1348 |
Conference
| Conference | 23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024 |
|---|---|
| Country/Territory | Japan |
| City | Hybrid, Miyazaki |
| Period | 20/09/24 → 23/09/24 |
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