Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Seconds Matter: Rapid Non-Contact Monitoring of Heart and Respiratory Rate from Face Videos.

Sensors (Basel, Switzerland)·2026
Same author

Effects on Serum Protein Levels From One Bout of High Intensity Interval Training in Individuals With Axial Spondyloarthritis and Controls.

Immunity, inflammation and disease·2025
Same author

Facilitators influencing participation in digitally-based high-intensity interval training among individuals with axial spondyloarthritis - a qualitative study.

BMC rheumatology·2025
Same author

The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review.

Journal of medical Internet research·2025
Same author

Changes in Physical Fitness in Youth Padel Players during One Season: A Cohort Study.

Sports (Basel, Switzerland)·2024
Same author

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.

JMIR research protocols·2024

Related Experiment Video

Updated: Jan 4, 2026

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running
08:26

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running

Published on: July 17, 2020

6.4K

A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation.

Taha Khan1, Lina E Lundgren2,3, Eric Järpe4

  • 1Centre of Artificial Intelligence, School of information technology, Halmstad University, SE-301 18 Halmstad, Sweden. taha.khan@hh.se.

Sensors (Basel, Switzerland)
|November 6, 2019
PubMed
Summary

Surface-electromyography (sEMG) can predict running fatigue by analyzing muscle signals. A novel method using sEMG effectively classifies aerobic, anaerobic, and recovery states, aiding in fatigue monitoring during exercise.

Keywords:
blood lactate concentrationfatiguerandom forestrunningsurface-electromyography

More Related Videos

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
06:35

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

9.5K
Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
06:00

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

Published on: July 27, 2015

13.0K

Related Experiment Videos

Last Updated: Jan 4, 2026

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running
08:26

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running

Published on: July 17, 2020

6.4K
Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
06:35

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

9.5K
Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
06:00

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

Published on: July 27, 2015

13.0K

Area of Science:

  • Exercise Physiology
  • Biomedical Engineering
  • Sports Science

Background:

  • Blood lactate accumulation is a key indicator of fatigue in athletes.
  • Surface-electromyography (sEMG) has been used to non-invasively estimate blood lactate and predict fatigue in cycling.
  • A method for automatic classification of running fatigue using sEMG is needed.

Purpose of the Study:

  • To predict muscle fatigue during running using sEMG.
  • To develop and validate a novel method for automatic classification of running fatigue levels.
  • To identify specific sEMG features and muscles most effective for fatigue classification.

Main Methods:

  • Collected sEMG data from 12 runners during an incremental treadmill test, focusing on leg muscles (vastus lateralis, vastus medialis, biceps femoris, semitendinosus, gastrocnemius).
  • Collected blood lactate samples every two minutes to label fatigue levels (aerobic, anaerobic, recovery) using a change-point segmentation algorithm.
  • Trained three random forest models using frequency, time-domain, and time-event sEMG features, optimizing with sequential feature elimination.

Main Results:

  • A random forest model utilizing distributive power frequency features from the vastus lateralis muscle alone achieved high accuracy in classifying running fatigue.
  • The identified sEMG feature demonstrated statistically significant differences (p < 0.01) between fatigue classes.
  • The model successfully classified fatigue into aerobic, anaerobic, and recovery states.

Conclusions:

  • sEMG signals, particularly from the vastus lateralis muscle, can accurately predict and classify running fatigue levels.
  • The proposed method offers a non-invasive approach for monitoring exercise intensity and fatigue during running.
  • This technique has potential applications in optimizing training and preventing overexertion in runners.