Between- and within-day repeatability of markerless 2D motion analysis using deep neural networks

Between- and within-day repeatability of markerless 2D motion analysis using deep neural networks

The purpose of this study was to evaluate kinematic analysis repeatability by deep learning approach in countermovement jump. Seventy athletes (39 women, 31 men) performed two maximal countermovement jumps in either one session or two separate sessions (jumps separated by two-weeks). The jumps were...

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Bibliographic Details
Main Author: Romppanen, Vesa
Other Authors: Liikuntatieteellinen tiedekunta, Faculty of Sport and Health Sciences, Liikunta- ja terveystieteet, Sport and Health Sciences, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:eng
Published: 2021
Subjects:
markerless
countermovement jump
deeplabcut
Biomekaniikka
Biomechanics
5012
toistettavuus
syväoppiminen
koneoppiminen
biomekaniikka
nivelet
liikeanalyysi
hyppääminen
liikeoppi
algoritmit
repeatability
deep learning
machine learning
biomechanics
joints (musculoskeletal system)
motion analysis
jumping
kinematics
algorithms
Online Access: https://jyx.jyu.fi/handle/123456789/77969

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