Repeatability of Knee Kinematic Gait Outcomes Using a Novel Hospital Hallway Setup of a Markerless Motion Capture System

Instrumented gait analysis has traditionally been isolated to laboratory, marker-based optoelectronic motion capture systems, which limits clinical uptake. Markerless motion capture (MMC) systems driven by trained machine learning algorithms offer high-throughput solutions for translational clinical opportunities. The aim of this study was to examine the day-to-day repeatability of discrete knee kinematic gait metrics in a healthy population using an MMC system uniquely installed in a hospital hallway. Twenty healthy adults (13 females, 7 males) participated in 3 overground hallway gait sessions, on average 11 days apart, using a novel MMC system setup. Intraclass correlation coefficients, standard errors of measurement, and minimal detectable changes were examined for each gait outcome. Results indicated good-to-excellent repeatability, with most (7/8) outcomes having intraclass correlation coefficient values over .86. Standard error of measurement values for all kinematic outcomes were less than 2.0°, and minimal detectable change values were less than 4.7°. Our novel setup of a hospital hallway MMC system produced highly repeatable gait kinematic metrics in a population of healthy adults. Repeatability errors from this study can be used as a healthy reference for future applications of this system.