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

Date

Authors

Argunsah, Hande

dc.identifier.doi

10.1080/10255842.2024.2400318

dc.identifier.other

WOS:001306158500001

Publisher

TAYLOR \& FRANCIS LTD

DOI

10.1080/10255842.2024.2400318

Keywords

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

URI

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

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