Nicolas M. Philipp ^1* Andrew C. Fry ^1* Eric M. Mosier ² Dimitrije Cabarkapa ¹ Justin Nicoll ³ Stephanie A. Sontag ⁴

• ¹ University of Kansas, Lawrence, United States
• ² Washburn University, Topeka, Kansas, United States
• ³ California State University, Northridge, Los Angeles, California, United States
• ⁴ Oklahoma State University, Stillwater, Oklahoma, United States

Introduction: Advances in motion capture technology include markerless systems to facilitate valid data collection. Recently, the technological reliability of this technology has been reported for human movement assessments. To further understand sources of potential error, biological reliability must also be determined. The aim of this study was to determine the day-to-day reliability for a threedimensional markerless motion capture (MMC) system to quantify 4 movement analysis composite scores, and 81 kinematic variables. Methods: Twenty-two healthy men (n=11; X ± SD; age=23.0  2.6 years, height=180.4.8 cm, weight=80.4  7.3 kg) and women (n=11; age=20.8  1.1 years, height=172.2  7.4 cm, weight=68.0  7.3 kg) participated in this study. All subjects performed 4 standardized test batteries consisting of 14 different movements on four separate days. A threedimensional MMC system (DARI Motion, Lenexa, KS) using 8 cameras surrounding the testing area was used to quantify movement characteristics. 1x4 RMANOVAs determined significant differences across days for the composite movement analysis scores, and RM-MANOVAs were used to determine test day differences for the kinematic data (p<0.05). Intraclass correlation coefficients (ICCs) were reported for all variables to determine test reliability. To determine biological variability, mean absolute differences from previously reported technological variability data (Philipp et al., 2022) were subtracted from the total variability data from the present study. Results: No differences were observed for any composite score (i.e., athleticism, explosiveness, quality, readiness; or any of the 81 kinematic variables. Furthermore, 84 of 85 measured variables exhibited good to excellent ICCs (0.61-0.99). When compared to previously reported technological variability data, 62.3% of item variability was due to biological variability, with 66 of 85 variables exhibiting biological variability as the primary source of error (i.e., >50% total variability). Discussion: Combined, these findings effectively add to the body of literature suggesting sufficient reliability for MMC solutions in capturing kinematic features of human movement.