Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.
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Stroke-Related Changes in the Complexity of Muscle Activation during Obstacle Crossing Using Fuzzy Approximate Entropy Analysis.Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control.Forearm Motion Recognition With Noncontact Capacitive SensingCauses of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb ProsthesesFeasibility Study of Advanced Neural Networks Applied to sEMG-Based Force EstimationMultiday EMG-Based Classification of Hand Motions with Deep Learning Techniques
P2860
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.
description
2017 nî lūn-bûn
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2017年の論文
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
@zh-my
2017年学术文章
@zh-sg
2017年學術文章
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2017年學術文章
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name
Self-Recalibrating Surface EMG ...... Convolutional Neural Network.
@en
type
label
Self-Recalibrating Surface EMG ...... Convolutional Neural Network.
@en
prefLabel
Self-Recalibrating Surface EMG ...... Convolutional Neural Network.
@en
P2093
P2860
P50
P356
P1476
Self-Recalibrating Surface EMG ...... Convolutional Neural Network.
@en
P2093
Beth Jelfs
Rosa H M Chan
Xiaolong Zhai
P2860
P356
10.3389/FNINS.2017.00379
P577
2017-07-11T00:00:00Z