ASSESSMENT OF KINEMATIC CMJ DATA USING A DEEP LEARNING ALGORITHM-BASED MARKERLESS MOTION CAPTURE SYSTEM

Authors

Gerda Strutzenberger, University of SalzburgFollow
Robert Kanko, Department of Mechanical & Materials Engineering, Queen's University, Kingston, CanadaFollow
Scott Selbie, Theia Markerless Inc, Kingston, CanadaFollow
Kevin Deluzio, Department of Mechanical & Materials Engineering, Queen's University, Kingston, CanadaFollow

Category

Methods / Statistics

Document Type

Paper

Abstract

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

Recommended Citation

Strutzenberger, Gerda; Kanko, Robert; Selbie, Scott; and Deluzio, Kevin (2020) "ASSESSMENT OF KINEMATIC CMJ DATA USING A DEEP LEARNING ALGORITHM-BASED MARKERLESS MOTION CAPTURE SYSTEM," ISBS Proceedings Archive: Vol. 38: Iss. 1, Article 218.
Available at: https://commons.nmu.edu/isbs/vol38/iss1/218