Results 91 to 100 of about 3,364,312 (190)

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Sensors, 2020
This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer ...
Evan Campbell, Angkoon Phinyomark, Erik Scheme   +2 more
doaj   +1 more source

Towards the unification of structural and statistical pattern recognition

, 2012
Institute of Computer Science, Pattern Recognition Group (PRG)Emeriti, Faculty of ScienceInstitute of Computer ...
Bunke, Horst, Riesen, Kaspar
core   +1 more source

Sparse distributed localized gradient fused features of objects [PDF]

, 2015
The sparse, hierarchical, and modular processing of natural signals is related to the ability of humans to recognize objects with high accuracy. In this study, we report a sparse feature processing and encoding method, which improved the recognition ...
Pappachen James, Alex, Swathikiran, Sudhakaran, Sudhakaran, Swathikiran   +2 more
core   +1 more source

Raising the standard: an open source benchmarking platform and data repository to accelerate myoelectric control research

Machine Learning: Health
After decades of use in the control of powered upper-limb prostheses, myoelectric control is now emerging as an input modality for general human–machine interaction.
Ethan Eddy, Evan Campbell, Christian Morrell, Heather Williams, Scott Bateman, Erik Scheme   +5 more
doaj   +1 more source

Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle-Computer Interface. [PDF]

Sensors (Basel), 2023
Fan J, Wen J, Lai Z.
europepmc   +1 more source

Human motion recognition using gaussian processes classification

, 2008
This paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences.
Liang Wang, Suter, D., Hang Zhou, Wang, L., Zhou, H., David Suter   +5 more
core   +1 more source

Design of an Effective Prosthetic Hand System for Adaptive Grasping with the Control of Myoelectric Pattern Recognition Approach. [PDF]

Micromachines (Basel), 2022
Wang Y, Tian Y, She H, Jiang Y, Yokoi H, Liu Y.   +5 more
europepmc   +1 more source

Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements. [PDF]

Sensors (Basel), 2021
Abbaspour S, Naber A, Ortiz-Catalan M, GholamHosseini H, Lindén M.   +4 more
europepmc   +1 more source

An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition. [PDF]

Sensors (Basel), 2021
Spieker V, Ganguly A, Haddadin S, Piazza C.   +3 more
europepmc   +1 more source

Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes

, 2017
Institute of Computer ScienceInstitute of Computer Science, Pattern Recognition Group ...
Uchida, Seiichi, Iwana, Brian Kenji, Riesen, Kaspar, Frinken, Volkmar   +3 more
core   +1 more source