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

Functional and histological assessment of 3D-printed helmet and hearing protection devices in preventing blast-induced auditory injury in Chinchillas.

Hearing research·2026
Same author

Evaluating the concurrent validity and test-retest reliability of markerless motion capture for static postural control assessment.

Journal of biomechanics·2026
Same author

What not to wear: Examining the usability of markerless motion capture for pediatric populations.

Journal of biomechanics·2026
Same author

Reducing robotic upper-limb assessment time while maintaining precision: a time series foundation model approach.

Journal of neuroengineering and rehabilitation·2026
Same author

Brain connectivity signatures of cognitive impairment in temporal lobe epilepsy identified by robotic assessment.

Neuroimage. Reports·2026
Same author

Characterizing visual compensation for proprioceptive impairments during the subacute phase of stroke.

Journal of neuroengineering and rehabilitation·2026

Related Experiment Video

Updated: Nov 7, 2025

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

14.4K

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system.

Robert M Kanko1, Elise K Laende1, Gerda Strutzenberger2

  • 1Mechanical and Materials Engineering, Queen's University, Kingston, Canada.

Journal of Biomechanics
|April 29, 2021
PubMed
Summary

Markerless motion capture accurately measures spatiotemporal gait parameters, showing good agreement with traditional methods. This technology offers a user-friendly approach for assessing gait health in various settings.

Keywords:
Deep learningGait analysisGait matMarkerless motion captureSpatiotemporal parameters

More Related Videos

3D Kinematic Gait Analysis for Preclinical Studies in Rodents
10:19

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

Published on: August 3, 2019

11.0K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K

Related Experiment Videos

Last Updated: Nov 7, 2025

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

14.4K
3D Kinematic Gait Analysis for Preclinical Studies in Rodents
10:19

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

Published on: August 3, 2019

11.0K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K

Area of Science:

  • Biomechanics
  • Human Movement Analysis
  • Wearable Technology

Background:

  • Spatiotemporal gait parameters are crucial for assessing health status and detecting gait changes.
  • Markerless motion capture offers a user-friendly, cost-effective alternative to traditional gait analysis systems.
  • Reducing barriers to gait measurement can expand its clinical and research applications.

Purpose of the Study:

  • To validate markerless motion capture for measuring spatiotemporal gait parameters.
  • To compare markerless system measurements against marker-based motion capture and a gait mat.
  • To assess the concurrent validity of markerless gait analysis.

Main Methods:

  • Two studies were conducted with healthy young adults performing treadmill and over-ground gait.
  • Markerless video data were synchronized with marker-based motion capture and a pressure-sensitive gait mat.
  • Gait cycles were identified using kinematic heel-strike and toe-off events; nine spatiotemporal parameters were compared.

Main Results:

  • Markerless motion capture demonstrated good to excellent agreement with marker-based systems and gait mats for most parameters.
  • Stance time and double limb support time showed less agreement with both comparison systems.
  • Stride width agreement was lower when compared to the gait mat system.

Conclusions:

  • Markerless motion capture is a valid tool for measuring spatiotemporal gait parameters in healthy young adults.
  • The technology shows promise for clinical and diverse settings, despite minor discrepancies in specific parameters.
  • This approach can enhance accessibility to gait analysis for health assessment.