Exploring the contribution of joint angles and sEMG signals on joint torque prediction accuracy using LSTM-based deep learning techniques

Tarih

Yazarlar

Argunsah, Hande

DOI No

10.1080/10255842.2024.2400318

Diğer Tanımlayıcı No

WOS:001306158500001

Yayınevi

TAYLOR \& FRANCIS LTD

Anahtar kelimeler

Machine learning, computational gait analysis, surface electromyogram, torque prediction, long short\\-term memory neural network

URI

https://openaccess.acibadem.edu.tr/handle/11443/5613

Koleksiyonlar

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