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Related Concept Videos

Fatigue01:21

Fatigue

763
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
763
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

478
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
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Muscle Recovery and Fatigue01:24

Muscle Recovery and Fatigue

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Muscle fatigue refers to the decline in a muscle's ability to maintain the force of contraction after prolonged activity. It primarily stems from changes within muscle fibers. Even before experiencing muscle fatigue, one may feel tired and have the urge to stop the activity. This response, known as central fatigue, occurs due to changes in the central nervous system, namely the brain and spinal cord. While there is no single mechanism that induces fatigue, it may serve as a protective...
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Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer
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Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor.

G Ramos1, J R Vaz2,3,4, G V Mendonça4

  • 1PLUX Wireless Biosignals S.A, Avenida 5 Outubro 70, 1050-59 Lisbon, Portugal.

Journal of Healthcare Engineering
|January 31, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning system to assess athlete fatigue using electromyography (EMG) and heart rate variability (HRV). The system provides a global fatigue descriptor (GFD) for injury prevention.

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Area of Science:

  • Physiology and sports science
  • Biomedical engineering
  • Machine learning applications in sports

Background:

  • Fatigue, a complex psychophysiological phenomenon, significantly impacts athletic performance and injury risk.
  • Both subjective (mental) and objective (physical) fatigue are crucial for optimal athletic functioning.
  • Current training programs often undervalue subjective fatigue, necessitating integrated assessment methods.

Purpose of the Study:

  • To develop and validate a machine learning system for assessing global athlete fatigue.
  • To integrate both mental and physical fatigue indicators for a comprehensive evaluation.
  • To enable preventive assessment of dangerous fatigue levels and minimize injury risk.

Main Methods:

  • Utilized electromyography (EMG) and heart rate variability (HRV) biosignals.
  • Developed a novel approach combining a global fatigue descriptor (GFD) and a support vector machine (SVM) classifier.
  • Extracted individual fatigue descriptors (IFDs) based on 9 combined features from constant work rate test data.

Main Results:

  • Achieved a fatigue regime classification performance of 0.82 ± 0.24.
  • The system successfully identifies dangerous fatigue levels for preventive assessment.
  • Demonstrated the efficacy of combining EMG and HRV for a global fatigue assessment.

Conclusions:

  • The developed machine learning system offers a robust method for assessing both physical and mental fatigue in athletes.
  • Integrating multiple biosignals provides a more holistic view of fatigue onset.
  • This approach supports the development of training programs that prioritize injury prevention through comprehensive fatigue monitoring.