Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network.

Sung, Joohwan; Han, Sungmin; Park, Heesu; Cho, Hyun-Myung; Hwang, Soree; Park, Jong Woong; Youn, Inchan

Sung, Joohwan; Han, Sungmin; Park, Heesu; Cho, Hyun-Myung; Hwang, Soree; Park, Jong Woong; Youn, Inchan.

Afiliação

Sung J; Center for Bionics, Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, Korea.
Han S; Department of Biomedical Science, College of Medicine, Korea University, Seoul 02841, Korea.
Park H; Center for Bionics, Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, Korea.
Cho HM; Center for Bionics, Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, Korea.
Hwang S; Department of Biomedical Science, College of Medicine, Korea University, Seoul 02841, Korea.
Park JW; Center for Bionics, Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, Korea.
Youn I; Department of Artificial Intelligence, Korea University, Seoul 02841, Korea.

Sensors (Basel) ; 22(1)2021 Dec 22.

Article em En | MEDLINE | ID: mdl-35009591

Assuntos

Marcha; Caminhada; Articulação do Tornozelo; Fenômenos Biomecânicos; Humanos; Extremidade Inferior; Redes Neurais de Computação

Palavras-chave

deep neural network; gait analysis; inertial measurement unit; long short-term memory; wearable sensor

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Caminhada / Marcha Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2021 Tipo de documento: Article

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