Assessment of kinematic cmj data using a deep learning algorithm-based markerless motion capture system
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. Published by International Society of Biomechanics in Sports. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | training science biological and medical sciences technical and natural sciences |
| Tagging: | deep learning Algorithmus reaktiver Sprung markerless |
| Published in: | ISBS Proceedings Archive (Michigan) |
| Language: | English |
| Published: |
Liverpool
International Society of Biomechanics in Sports
2020
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| Online Access: | https://commons.nmu.edu/isbs/vol38/iss1/218 |
| Volume: | 38 |
| Issue: | 1 |
| Pages: | Article 218 |
| Document types: | congress proceedings |
| Level: | advanced |