Continuous Estimation of Hand Kinematics From Electromyographic Signals Based on Power-and Time-Efficient Transformer Deep Learning Network

Continuous Estimation of Hand Kinematics From Electromyographic Signals Based on Power-and Time-Efficient Transformer Deep Learning Network

Surface Electromyographic (sEMG) signals contain motor-related information and therefore can be used for human-machine interaction (HMI). Deep learning plays an important role in extracting motor-related information from sEMG signals. However, most studies prioritize model accuracy without sufficien...

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Bibliographic Details
Main Authors: Chuang Lin, Chunxiao Zhao, Jianhua Zhang, Chen Chen, Ning Jiang, Dario Farina, Weiyu Guo
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
sEMG
continuous estimation
finger kinematics
Transformer
model efficiency
Online Access:https://ieeexplore.ieee.org/document/10789212/
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