Assessment of kinematic cmj data using a deep learning algorithm-based markerless motion capture system - Strutzenberger, G.; Kanko, R.; Selbie, S.; Deluzio, K. (ISBS Proceedings Archive (Michigan), 2020)
The purpose of this study was to compare the performance of a 2D 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 were collected simultaneously via a marker-based (Oqus) and a 2D video-based motion capture system (Miqus, both: Qualisys AB, Gothenburg, Sweden). The 2D video data was further processed using Theia3D (Theia Markerless Inc.), both sets of data were analysed concurrently in Visual3D (C-motion, Inc). Excellent agreement between systems with ICCs >0.988 exists for Jump height (mean average error of 0.35 cm) and ankle and knee sagittal plane angles (RMS differences < 5°). The hip joint showed higher.