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Related Concept Videos

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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Muscle Stimulation Frequency01:22

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The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
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Skeletal muscle relaxants are a group of drugs that can reduce muscle stiffness and induce temporary paralysis to relieve pain. These agents can act centrally to reduce muscle tone or spasms in painful conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or spinal injuries; they are called antispasmodics or spasmolytics.
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Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
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When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
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Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
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A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold.

Junhong Wang1,2, Shaoming Sun1,2, Yining Sun1,2

  • 1Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study introduces a new method using surface electromyography (sEMG) and a long short-term memory (LSTM) network to automatically detect muscle fatigue. The proposed model demonstrates superior performance in classifying muscle fatigue compared to existing methods.

Keywords:
long short-term memorymuscle fatiguesurface electromyographywavelet packet

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Area of Science:

  • Biomedical Engineering
  • Sports Science
  • Rehabilitation Medicine

Background:

  • Muscle fatigue prediction is crucial for optimizing training and preventing injuries.
  • Previous methods often rely on indirect measures like the anaerobic threshold (AT).
  • Surface electromyography (sEMG) offers a direct measure of muscle electrical activity.

Purpose of the Study:

  • To develop and validate a novel, automatic muscle fatigue classification system.
  • To investigate the efficacy of surface electromyography (sEMG) signals for fatigue detection.
  • To compare the performance of a long short-term memory (LSTM) network against other machine learning models for muscle fatigue recognition.

Main Methods:

  • Acquired sEMG data from vastus rectus femoris, vastus lateralis, vastus medialis, and gastrocnemius muscles during incremental cycling tests.
  • Denoised sEMG signals using an improved wavelet packet threshold algorithm.
  • Extracted time-domain and frequency-domain features from sEMG signals.
  • Trained a long short-term memory (LSTM) network for muscle fatigue classification.

Main Results:

  • The improved wavelet packet threshold denoising method outperformed hard and soft thresholding.
  • The proposed LSTM-based muscle fatigue recognition model achieved superior classification performance compared to CNN, SVM, and other models.
  • Optimal LSTM network performance was observed with a 70% training, 10% validation, and 20% testing data split.

Conclusions:

  • The developed LSTM model provides an effective and automatic approach for muscle fatigue monitoring.
  • Surface electromyography (sEMG) combined with advanced machine learning offers a promising avenue for fatigue assessment.
  • This method has potential applications in sports science, physical therapy, and ergonomics.