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.
| 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
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| Online-Zugang: | https://commons.nmu.edu/isbs/vol38/iss1/218 |
| Jahrgang: | 38 |
| Heft: | 1 |
| Seiten: | Article 218 |
| Dokumentenarten: | Kongressband, Tagungsbericht |
| Level: | hoch |