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

Muscle Recovery and Fatigue01:24

Muscle Recovery and Fatigue

2.0K
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...
2.0K
Fatigue01:21

Fatigue

174
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...
174
Survival Tree01:19

Survival Tree

61
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
61

You might also read

Related Articles

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

Sort by
Same author

PharmaGNN: a model for odor prediction based on graph neural networks.

Journal of the science of food and agriculture·2026
Same author

Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.

BMC cancer·2026
Same author

Slit2/Robo Signaling Restores Diabetic Erectile Function via Neurovascular Remodeling.

Andrology·2026
Same author

Construction of a sensory quality evaluation model for tobacco leaves from Henan Province and its application in tobacco quality assessment.

Frontiers in plant science·2026
Same author

Retraction: MicroRNA98 acts as a tumor suppressor in hepatocellular carcinoma via targeting SALL4.

Oncotarget·2026
Same author

General Expression for Vibronic Coupling in Proton-Coupled Energy Transfer.

Journal of chemical theory and computation·2026
Same journal

Long COVID and health-related quality of life: a systematic review of immune, inflammatory, and metabolic markers.

Frontiers in public health·2026
Same journal

Smart wearables for school-based physical activity promotion: K-12 physical education teachers' continuance intention and adoption pathways.

Frontiers in public health·2026
Same journal

Acceptance of an upper body exoskeleton for occupational tasks among soldiers.

Frontiers in public health·2026
Same journal

Effectiveness of a perforated spoon for reducing salt intake in a university cafeteria.

Frontiers in public health·2026
Same journal

Association of psychological resilience and social support with subjective coercion experience in hospitalized psychiatric inpatients: a cross-sectional study.

Frontiers in public health·2026
Same journal

The "5-4-3" model for weight management in psychiatric inpatients: a single-arm pre-post evaluation.

Frontiers in public health·2026
See all related articles

Related Experiment Video

Updated: Jun 9, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

11.9K

Identifying fatigue of climbing workers using physiological data based on the XGBoost algorithm.

Yonggang Xu1, Qingzhi Jian2, Kunshuang Zhu1

  • 1Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China.

Frontiers in Public Health
|October 24, 2024
PubMed
Summary
This summary is machine-generated.

High-voltage workers

Keywords:
XGBoostclimbing workersfatigue identificationmachine learningphysiological data

More Related Videos

Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer
07:22

Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer

Published on: February 20, 2020

5.8K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Related Experiment Videos

Last Updated: Jun 9, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

11.9K
Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer
07:22

Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer

Published on: February 20, 2020

5.8K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Area of Science:

  • Occupational Health
  • Physiology
  • Ergonomics

Background:

  • High-voltage work involves physically demanding climbing tasks.
  • Fatigue in these workers impairs motor and cognitive functions, posing safety risks.
  • Effective fatigue monitoring is crucial for worker safety.

Purpose of the Study:

  • To develop an experimental method for quantifying fatigue in high-voltage workers during climbing operations.
  • To identify key physiological indicators for assessing climbing-induced fatigue.
  • To create a predictive fatigue identification model using machine learning.

Main Methods:

  • Collected subjective (RPE scale) and objective (SBP, DBP, SpO2, VC, GS, RT, CFF, HR) fatigue data from 33 high-voltage workers.
  • Performed climbing tasks to induce fatigue.
  • Utilized the XGBoost algorithm to build a fatigue identification model.

Main Results:

  • Blood oxygen saturation (SpO2), vital capacity (VC), grip strength (GS), response time (RT), and critical fusion frequency (CFF) were identified as effective fatigue indicators.
  • The XGBoost model, incorporating subjective fatigue and these five physiological indicators, achieved 89.75% accuracy in fatigue identification.

Conclusions:

  • The study establishes a reliable method for assessing fatigue in high-voltage workers during climbing.
  • Findings support personalized fatigue management strategies to enhance worker safety.
  • Timely fatigue detection can mitigate accident risks in demanding occupational settings.