Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals.

Guo, Weiyu; Ma, Chenfei; Wang, Zheng; Zhang, Hang; Farina, Dario; Jiang, Ning; Lin, Chuang

Guo, Weiyu; Ma, Chenfei; Wang, Zheng; Zhang, Hang; Farina, Dario; Jiang, Ning; Lin, Chuang.

Afiliação

Guo W; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
Ma C; University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Wang Z; Equally contributed to this work.
Zhang H; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, People's Republic of China.
Farina D; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
Jiang N; Equally contributed to this work.
Lin C; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.

J Neural Eng ; 18(2)2021 03 03.

Article em En | MEDLINE | ID: mdl-33326941

Assuntos

Dedos; Mãos; Fenômenos Biomecânicos; Eletromiografia/métodos; Dedos/fisiologia; Gestos; Humanos

Palavras-chave

convolutional long short-term memory network; estimation; finger joint angle; proportional; simultaneous; surface electromyography

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dedos / Mãos Limite: Humans Idioma: En Revista: J Neural Eng Ano de publicação: 2021 Tipo de documento: Article

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