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

Dual-task interference during a functional mobility task in Parkinson's disease persists across medication states.

Gait & posture·2026
Same author

A Comprehensive Evaluation of Mobility: Validation of the Functional Ambulation and Stair Test in Older Adults.

Journal of clinical medicine·2026
Same author

An initial machine learning model applied to local field potential data from the subthalamic nucleus to detect freezing of gait in Parkinson's disease.

Frontiers in neurology·2026
Same author

A machine learning approach to quantifying fall conversion risk in fall-naïve Parkinson's patients.

Parkinsonism & related disorders·2026
Same author

A Structured Aerobic Exercise Program Increases Physical Activity in People With Parkinson's Disease: A Secondary Analysis of the CYCLE-II Trial.

Journal of geriatric physical therapy (2001)·2026
Same author

Closing the Gate Before the Horse is out of the Barn: A Model to Effectively Predict the First Fall in Patients with Parkinson's Disease.

Movement disorders clinical practice·2026

Related Experiment Video

Updated: Feb 28, 2026

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living
08:36

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

Published on: July 28, 2022

4.5K

Utilizing an Augmented Reality Headset to Accurately Quantify Lower Extremity Function in Parkinson's Disease.

Andrew Bazyk1, Colin Waltz2, Ryan D Kaya1

  • 1Center for Neurological Restoration, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH 44195, USA.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

Markerless motion capture (MMC) offers objective gait analysis for Parkinson's disease (PD). A new algorithm (CART-MMC) accurately measures lower extremity function, aiding in PD diagnosis and treatment monitoring.

Keywords:
Parkinson’s diseaseaugmented realitygait biomechanicsmarkerless motion capturestepping in place

More Related Videos

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.8K
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

13.1K

Related Experiment Videos

Last Updated: Feb 28, 2026

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living
08:36

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

Published on: July 28, 2022

4.5K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.8K
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

13.1K

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Subjective gait assessments limit Parkinson's disease (PD) treatment.
  • Markerless motion capture (MMC) can provide objective biomechanical data.
  • Validation is crucial for clinical adoption of MMC in PD.

Purpose of the Study:

  • Evaluate the criterion validity of a custom MMC algorithm (CART-MMC) against 3D motion capture.
  • Assess CART-MMC's ability to differentiate PD patients from healthy controls (HC).
  • Determine the clinical utility of CART-MMC for lower extremity function in PD.

Main Methods:

  • Sixty-two individuals with PD and 29 HCs performed a stepping in place task.
  • Data collected using an augmented reality headset with RGB and depth cameras.
  • CART-MMC algorithm computed 3D pose and biomechanical measures, compared to traditional 3D motion capture.

Main Results:

  • CART-MMC outcomes were statistically equivalent (within 5%) to traditional 3D motion capture for step count, cadence, duration, height, asymmetry, and path length.
  • CART-MMC identified significant differences between PD and HC groups in step height, variability, asymmetry, duration variability, and normalized path length.

Conclusions:

  • The CART-MMC algorithm provides valid biomechanical measures of lower extremity function in PD.
  • Validated MMC tools can track disease progression and monitor treatment efficacy.
  • Objective biomechanical evaluation supports personalized PD therapy and clinical decision-making.