Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 9, 2026

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview

Seth Donahue, Irina Djuraskovic, Kunal Shah

IEEE Transactions on Bio-Medical Engineering

|May 7, 2026

Summary

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

A wireless subdural-contained brain-computer interface with 65,536 electrodes and 1,024 channels.

Nature electronics·2026

Same author

Functional, molecular, and digital measurements of biological age.

The Journal of clinical investigation·2026

Same author

Monocular Markerless Motion Capture Enables Quantitative Assessment of Upper Extremity Reachable Workspace.

Sensors (Basel, Switzerland)·2026

Same author

Portable biomechanics laboratory enables clinically accessible movement analysis from a handheld smartphone.

NPJ digital medicine·2026

Same author

Muscle-driven hand simulations emphasize the critical role of the extensor mechanism.

bioRxiv : the preprint server for biology·2026

Same author

Validation, characterization, and utility of markerless motion capture in a large cohort of pediatric patients with complex gait patterns.

Journal of biomechanics·2026

This study validates a probabilistic multi-view markerless motion capture (MMMC) method for human movement analysis. The model accurately quantifies uncertainty, enabling reliable clinical assessments without extra equipment.

Area of Science:

Biomechanics
Clinical Motion Analysis
Medical Technology

Background:

Video-based human movement analysis, specifically multi-view markerless motion capture (MMMC), shows promise for clinical practice and research.
Clinical implementation requires MMMC systems to be accurate and provide reliable confidence intervals for individual assessments.
Prior work utilized variational inference to estimate biomechanical variables against clinical gold-standards.

Purpose of the Study:

To evaluate the calibration and accuracy of a probabilistic MMMC method.
To determine if the system can reliably indicate its own accuracy for individual measurements.
To build upon previous research using variational inference for biomechanical variable estimation.

Main Methods:

Data from 68 participants across two institutions were analyzed.

Related Experiment Videos

Last Updated: May 9, 2026

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

The probabilistic MMMC model was validated against an instrumented walkway and marker-based motion capture.

Calibration of confidence intervals was measured using Expected Calibration Error (ECE).

Main Results:

The model demonstrated reliable calibration with ECE values generally below 0.1 for step/stride length and gait kinematics.
Median errors for step and stride length were approximately 16 mm and 12 mm, respectively.
Median bias-corrected kinematic errors ranged from 1.5° to 3.8° across lower extremity joints, with uncertainty correlating strongly with errors.

Conclusions:

The probabilistic model reconstruction effectively quantifies epistemic uncertainty, evidenced by low absolute error and calibrated uncertainty.
This probabilistic approach can identify unreliable outputs without requiring concurrent ground-truth instrumentation.
The findings support the clinical utility of this MMMC method for reliable movement assessment.