Assessment of kinematic cmj data using a deep learning algorithm-based markerless motion capture system - Strutzenberger, G.; Kanko, R.; Selbie, S.; Schwameder, H.; Deluzio, K. (ISBS Proceedings Archive (Michigan), 2021)
The purpose of this study was to compare the performance of a video-based markerless motion capture system to a conventional marker-based approach during a counter movement jump (CMJ). Twenty-three healthy participants performed CMJ while data was collected simultaneously via a marker-based (Oqus) and a 2D video-based motion capture system (Miqus, both: Qualisys). The video data was further processed to 3D-data using Theia3D (Theia Markerless Inc.). Excellent agreement between systems with ICCs >0.99 exists for jump height (mean average error of -0.27 cm) and sagittal ankle and knee plane angles (RMSD < 5°). The hip joint showed an average RMSD of 21° with a strong correlation of 0.80. As such the markerless system is capable of detecting jump height, sagittal ankle and knee joint angles and 3D joint positions of a CMJ to a high accuracy