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 Experiment Videos

Driver's mental workload prediction model based on physiological indices.

Shengyuan Yan1, Cong Chi Tran1,2, Yingying Wei1

  • 1a College of Mechanical and Electrical Engineering , Harbin Engineering University , China.

International Journal of Occupational Safety and Ergonomics : JOSE
|August 19, 2017
PubMed
Summary
This summary is machine-generated.

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

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues.

bioRxiv : the preprint server for biology·2026
Same author

Effect of chitosan and ε-Polylysine composite coating on postharvest quality maintenance and disease resistance of fresh <i>Tremella fuciformis</i>.

Food chemistry: X·2026
Same author

Environmental factors drive rutin accumulation through a synergistic network promoting flavonoid biosynthesis and suppressing hydrolysis in tea plants (Camellia sinensis).

Food chemistry·2026
Same author

Role of 2-phenylethanol in regulating population density and biocontrol efficacy of Scheffersomyces spartinae W9 via quorum sensing.

International journal of food microbiology·2026
Same author

Transcriptomic and Metabolomic Analyses Reveal Color Variation and Taste Formation of Different Radish Sprouts.

Journal of agricultural and food chemistry·2026
Same author

Single-cell analyses of tissue regeneration in two true jellyfish.

Molecular biology and evolution·2026
Same journal

Optimization and prediction of overall moisture management capacity of cut-protective fabric.

International journal of occupational safety and ergonomics : JOSE·2026
Same journal

The influence of safety culture, fatalism, and free will beliefs on employees' safety performance.

International journal of occupational safety and ergonomics : JOSE·2026
Same journal

Occupational mental health among remote workers in Nigeria: the role of computer vision syndrome and the moderating effect of employee resilience.

International journal of occupational safety and ergonomics : JOSE·2026
Same journal

The effects of an 8-week resistance training program on shoulder strength and range of motion in firefighters with subacromial impingement syndrome: a randomized controlled trial.

International journal of occupational safety and ergonomics : JOSE·2026
Same journal

Impact of nursing risk management target cards on patient safety in intensive care unit patients: a mixed-methods study.

International journal of occupational safety and ergonomics : JOSE·2026
Same journal

Enhancing occupational safety among gas station workers in Egypt: a quasi-experimental evaluation of an educational intervention.

International journal of occupational safety and ergonomics : JOSE·2026
See all related articles

This study developed a predictive model for new drivers' mental workload (MWL) using physiological data and subjective ratings. The model accurately predicts driving errors, aiding in performance improvement.

Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Transportation Safety

Background:

  • Driver mental workload (MWL) is critical for performance, especially for novice drivers.
  • Predicting MWL can enhance driving safety and training effectiveness.
  • Current methods for assessing MWL may not be fully integrated or predictive.

Purpose of the Study:

  • To investigate the correlation between new drivers' MWL and their driving performance (number of errors).
  • To develop a predictive model for driver MWL using subjective and physiological data.
  • To establish a reference for new drivers' MWL and inform personalized driving lesson plans.

Main Methods:

  • Utilized the group method of data handling to construct the predictive model.
Keywords:
driving simulatormental workloadpredictive modelwork performance

Related Experiment Videos

  • Incorporated subjective workload ratings via the NASA task load index (NASA-TLX).
  • Integrated six physiological indices alongside NASA-TLX for MWL assessment.

    • Main Results:

    • A significant positive correlation was found between NASA-TLX scores and the number of driving errors.
    • The developed predictive model demonstrated strong validity with an R-squared value of 0.745.
    • The model successfully linked physiological indices to predicted driver MWL.

    Conclusions:

    • The proposed model provides a valid method for predicting new driver MWL and performance.
    • Physiological indices can serve as a reference for assessing new drivers' mental workload.
    • The model supports the development of targeted driving lesson plans to optimize MWL and performance.