Adversarial autoencoder for visualization and classification of human activity: Application to a low-cost commercial force plate

The ability to visualize and interpret high dimensional time-series data will be critical as wearable and other sensors are adopted in rehabilitation protocols. This study proposes a latent space representation of high dimensional time-series data for data visualization. For that purpose, a deep lea...

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Vydané v:	Journal of Biomechanics Ročník 103; s. 109684
Hlavní autori:	Hernandez, Vincent, Kulić, Dana, Venture, Gentiane
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 16.04.2020 Elsevier BV Elsevier Limited
Predmet:	Adversarial autoencoder Classification Data visualization Deep learning Experiments Force plates Ground reaction force Human Activities Human activity recognition Humans Learning algorithms Machine Learning Patients Principal components analysis Rehabilitation Representations Scientific visualization Self evaluation Sensors Visualization Deep learning Human activity recognition Ground reaction force Adversarial autoencoder Machine learning
ISSN:	0021-9290, 1873-2380, 1873-2380
On-line prístup:	Získať plný text
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