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

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dc.contributor.authorKaya, Engin
dc.contributor.authorArgunsah, Hande
dc.date.accessioned2025-10-16T15:12:48Z
dc.date.issued2024
dc.identifier.doi10.1080/10255842.2024.2400318
dc.identifier.otherWOS:001306158500001
dc.identifier.urihttps://openaccess.acibadem.edu.tr/handle/11443/5613
dc.publisherTAYLOR \& FRANCIS LTD
dc.sourceCOMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
dc.subjectMachine learning
dc.subjectcomputational gait analysis
dc.subjectsurface electromyogram
dc.subjecttorque prediction
dc.subjectlong short\\-term memory neural network
dc.titleExploring the contribution of joint angles and sEMG signals on joint torque prediction accuracy using LSTM-based deep learning techniques
dc.typeArticle; Early Access

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