Results 101 to 110 of about 5,067 (128)

Context-informed incremental learning improves both the performance and resilience of myoelectric control. [PDF]

J Neuroeng Rehabil
Campbell E, Eddy E, Bateman S, Côté-Allard U, Scheme E.   +4 more
europepmc   +1 more source

Dimensionality analysis of forearm muscle activation for myoelectric control in transradial amputees. [PDF]

PLoS One, 2020
McClanahan A, Moench M, Fu Q.
europepmc   +1 more source

A Modular Adjustable Transhumeral Prosthetic Socket for Evaluating Myoelectric Control. [PDF]

IEEE J Transl Eng Health Med, 2020
Hallworth BW, Austin JA, Williams HE, Rehani M, Shehata AW, Hebert JS.   +5 more
europepmc   +1 more source

Machine Learning- and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals: A Systematic Review. [PDF]

Bioengineering (Basel)
Zaim T, Abdel-Hadi S, Mahmoud R, Khandakar A, Rakhtala SM, Chowdhury MEH.   +5 more
europepmc   +1 more source

Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts. [PDF]

Sensors (Basel)
Wang B, Li J, Hargrove L, Kamavuako EN.
europepmc   +1 more source

Evaluation of a Simultaneous Myoelectric Control Strategy for a Multi-DoF Transradial Prosthesis. [PDF]

IEEE Trans Neural Syst Rehabil Eng, 2020
Piazza C, Rossi M, Catalano MG, Bicchi A, Hargrove LJ.   +4 more
europepmc   +1 more source

Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition. [PDF]

Front Bioeng Biotechnol
Eddy E, Campbell E, Bateman S, Scheme E.
europepmc   +1 more source

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features. [PDF]

Front Bioeng Biotechnol, 2020
Côté-Allard U, Campbell E, Phinyomark A, Laviolette F, Gosselin B, Scheme E.   +5 more
europepmc   +1 more source