VI-Net—View-Invariant Quality of Human Movement Assessment

We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 20; číslo 18; s. 5258
Hlavní autori:	Sardari, Faegheh, Paiement, Adeline, Hannuna, Sion, Mirmehdi, Majid
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI 15.09.2020 MDPI AG
Predmet:	Artificial Intelligence Computer Science Computer Vision and Pattern Recognition health monitoring Humans Image Processing Machine Learning Movement movement analysis Neural and Evolutionary Computing Neural Networks, Computer Signal and Image Processing view-invariant convolutional neural network (CNN) health monitoring view-invariant convolutional neural network (CNN) movement analysis
ISSN:	1424-8220, 1424-8220
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