Jove
Visualize
Contact Us

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

Early detection of Alzheimer's disease using deep learning methods.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Review of models for estimating 3D human pose using deep learning.

PeerJ. Computer science·2025
Same author

Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI.

Diagnostics (Basel, Switzerland)·2025
Same author

SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning.

Sensors (Basel, Switzerland)·2024
Same author

An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.

Bioengineering (Basel, Switzerland)·2024
Same author

AI-Enhanced Dyscalculia Screening: A Survey of Methods and Applications for Children.

Diagnostics (Basel, Switzerland)·2024
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 Video

Updated: Jul 19, 2025

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality
08:45

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Published on: April 5, 2018

7.7K

Motion Capture Technologies for Ergonomics: A Systematic Literature Review.

Sani Salisu1,2, Nur Intan Raihana Ruhaiyem1, Taiseer Abdalla Elfadil Eisa3

  • 1School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia.

Diagnostics (Basel, Switzerland)
|August 12, 2023
PubMed
Summary

Motion capture (MoCap) systems offer solutions for musculoskeletal disorders but face usability challenges. This review identifies the most used MoCap technologies for clinical, rehabilitation, and ergonomic analysis, guiding future research and application.

Keywords:
EMGIMS systemsMBased systemsMLess systemshandsshoulder

More Related Videos

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

664
Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.5K

Related Experiment Videos

Last Updated: Jul 19, 2025

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality
08:45

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Published on: April 5, 2018

7.7K
Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

664
Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.5K

Area of Science:

  • Biomedical Engineering
  • Human Movement Analysis
  • Rehabilitation Technology

Background:

  • Musculoskeletal disorders pose significant challenges for the working population.
  • Motion capture (MoCap) technology is crucial for clinical, ergonomic, and rehabilitation solutions.
  • Knowledge barriers hinder the widespread adoption and effective use of MoCap systems.

Purpose of the Study:

  • To conduct a state-of-the-art literature review on MoCap systems for human clinical, rehabilitation, and ergonomic analysis.
  • To identify the most utilized MoCap systems in these domains.
  • To categorize MoCap applications and target populations.

Main Methods:

  • Systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
  • Searches conducted on Google Scholar, PubMed, Scopus, and Web of Science (2013-2023).
  • Screening of titles and abstracts, followed by categorization of 40 eligible articles.

Main Results:

  • Identification of prevalent MoCap systems used in clinical, rehabilitation, and ergonomic contexts.
  • Analysis of the diverse applications of MoCap technology across different human movement studies.
  • Characterization of the target populations benefiting from MoCap analysis.

Conclusions:

  • This review provides a comprehensive overview of MoCap systems for human movement analysis.
  • It serves as a valuable guide for researchers and organizational management in selecting and implementing MoCap solutions.
  • Highlights the need for improved usability and accessibility of MoCap technologies.