Open Access

Janina Helwig, Jos Vanrenterghem, Bastian Anedda, Daniel Koska, Markus Hipper, Yannick Denis, Luca Braun, Veerle Segers, Albert Gollhofer, Steffen Willwacher

• Marker-based (MB) motion capture systems face challenges like marker loss, soft tissue artifacts, and manual processing. Markerless (ML) motion capture systems might be particularly advantageous for capturing dynamic, non-linear movements like change of direction (COD) movements. The agreement between MB and ML systems for lower extremity joint kinematics during COD tasks was evaluated. NineteenMarker-based (MB) motion capture systems face challenges like marker loss, soft tissue artifacts, and manual processing. Markerless (ML) motion capture systems might be particularly advantageous for capturing dynamic, non-linear movements like change of direction (COD) movements. The agreement between MB and ML systems for lower extremity joint kinematics during COD tasks was evaluated. Nineteen athletes performed cutting movements in 5 directions at 3 intensities. Data was captured using infrared and video cameras. Joint angles were computed, and the agreement was assessed using prediction band and extended Bland-Altman (BA) methods. Knee joint angles showed the smallest random and systematic errors (bias = 4.34°, area = 3102.57 deg·stance%; BA: bias = 3.34°, limits of agreement (LoA) = ± 11.38°) compared to ankle and hip joint angles. Flexion/extension angles displayed a smaller random error (area = 2919.74 deg·stance%; LoA = ± 10.71°) compared to ab-/adduction (area = 3477.70 deg·stance%; LoA ± 12.16°) and internal/external rotation angles (area = 4630.68 deg·stance%; LoA = ± 15.75°). Slower and straight-line movements demonstrated stronger agreement than sharper, non-linear directions. These findings provide valuable insight for researchers and practitioners when placing ML data in the context of existing data, with particular care when considering highly dynamic, non-linear movements or internal/external rotation angles.…