Assessment of kinematic cmj data using a deep learning algorithm-based markerless motion capture system

(Bewertung kinematischer cmj-Daten mit einem auf einem Deep-Learning-Algorithmus basierenden markerlosen Bewegungserfassungssystem)

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.
© Copyright 2020 ISBS Proceedings Archive (Michigan). Northern Michigan University. Veröffentlicht von International Society of Biomechanics in Sports. Alle Rechte vorbehalten.

Bibliographische Detailangaben
Schlagworte:
Notationen:Trainingswissenschaft Biowissenschaften und Sportmedizin Naturwissenschaften und Technik
Tagging:deep learning Algorithmus reaktiver Sprung markerless
Veröffentlicht in:ISBS Proceedings Archive (Michigan)
Sprache:Englisch
Veröffentlicht: Liverpool International Society of Biomechanics in Sports 2020
Online-Zugang:https://commons.nmu.edu/isbs/vol38/iss1/218
Jahrgang:38
Heft:1
Seiten:Article 218
Dokumentenarten:Kongressband, Tagungsbericht
Level:hoch