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Related Experiment Video

Updated: Jun 30, 2025

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data

Brian Horsak¹, Kerstin Prock², Philipp Krondorfer²

¹Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria; Institute of Health Sciences, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria.

Journal of Biomechanics

|March 17, 2024

Summary

This summary is machine-generated.

Markerless motion capture shows higher inter-trial variability than marker-based systems in gait analysis. This increased variability, ranging from 6.6% to 22.0%, can affect human movement science findings.

Keywords:

Deep learning Gait analysis Intra-session variability OpenCap Pose estimation markerless motion capture

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Last Updated: Jun 30, 2025

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Published on: August 3, 2019

Area of Science:

Biomechanics
Human Movement Science
Clinical Gait Analysis

Background:

Markerless motion capture offers ease of use for large-scale, out-of-laboratory human movement studies.
Previous research indicates acceptable accuracy but notes higher inter-trial variability in markerless gait data.

Purpose of the Study:

To compare the inter-trial variability between markerless (OpenCap) and marker-based motion capture systems.
To assess if gait pattern influences the variability difference between systems.

Main Methods:

Simultaneous recording of 18 healthy volunteers using OpenCap and a marker-based system.
Volunteers simulated four distinct gait patterns: physiological, crouch, circumduction, and equinus.
Comparison of inter-trial variability metrics between the two motion capture approaches.

Main Results:

Markerless systems exhibited increased inter-trial variability compared to marker-based systems, with a range of 6.6% to 22.0% across gait patterns.
Pose estimation pipelines in markerless systems contribute to variability in kinematic data.
Variability increases were observed across different gait patterns and natural variability levels.

Conclusions:

Markerless pose estimation introduces additional variability into kinematic gait data.
Averaged waveforms are recommended over single waveforms to mitigate increased inter-trial variability.
Caution is advised when employing variability-based metrics with markerless gait data due to potential misleading results.