An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors.

Cuperman, Rafael; Jansen, Kaspar M B; Ciszewski, Michal G

Cuperman, Rafael; Jansen, Kaspar M B; Ciszewski, Michal G.

Afiliação

Cuperman R; Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology (TU Delft), Mekelweg 4, 2628 CD Delft, The Netherlands.
Jansen KMB; Faculty of Industrial Design Engineering, Delft University of Technology (TU Delft), Landbergstraat 15, 2628 CE Delft, The Netherlands.
Ciszewski MG; Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology (TU Delft), Mekelweg 4, 2628 CD Delft, The Netherlands.

Sensors (Basel) ; 22(4)2022 Feb 10.

Article em En | MEDLINE | ID: mdl-35214245

Assuntos

Aprendizado Profundo; Futebol; Dispositivos Eletrônicos Vestíveis; Humanos; Aceleração; Redes Neurais de Computação

Palavras-chave

HAR; IMU; LSTM; artificial neural networks; convolutional neural networks; deep learning; football; human activity recognition; machine learning; soccer

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Futebol / Dispositivos Eletrônicos Vestíveis / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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