A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects

• Published in: Frontiers in neuroscience Vol. 18; p. 1306050
• Main Authors: Lin, Chuang, He, Zheng
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 20.03.2024
• Subjects: cross-subject model; finger joint angles estimation; Neuroscience; rotary transformer (RoFormer); sEMG; transfer learning; transfer learning; sEMG; finger joint angles estimation; cross-subject model; rotary transformer (RoFormer)

Surface Electromyographic (sEMG) signals are widely utilized for estimating finger kinematics continuously in human-machine interfaces (HMI), and deep learning approaches are crucial in constructing the models. At present, most models are extracted on specific subjects and do not have cross-subject...