Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Abstract

Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings.Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%.Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%.Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.

Accuracy of a markerless motion capture system for balance related quantities

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The Immediate Effect of Backward Walking on External Knee Adduction Moment in Healthy Individuals

“…An inverse dynamics algorithm was used to calculate the primary biomechanical outcome, EKAM, and KAAI, in the stance phase for the trials at both BW and FW conditions. Based on a previous study [25], the quality checks were performed in Visual3D in order to avoid extreme kinetics and kinematics values. Te EKAM and KAAI were normalized to the participant's body mass (Nm/kg and (Nm/kg)•s, respectively) and both the frst and second peaks of EKAM were presented.…”

“…Calculated at the time of peak EKAM [26]. Based on a previous study, the quality checks were performed in Visual3D in order to avoid extreme data [25].…”

Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing

“…The software implements deep convolutional neural network combining with standard biomechanical pose estimation approaches (inverse kinematics) to estimate 3D pose of human body segments. Using this tool, studies showed decent kinematic accuracies compared to marker-based method while maintaining good repeatability both in laboratory environment (Kanko et al, 2021a , b ) and in community settings (Mcguirk et al, 2022 ; Riazati et al, 2022 ). These studies, however, mainly provide the assessment of the lower extremity kinematics during either treadmill or over ground walking activity.…”