A novel approach for SEMG signal classification with adaptive local binary patterns.

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Design and Validation of a Breathing Detection System for Scuba Divers Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure.

P2860

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P2860

A novel approach for SEMG signal classification with adaptive local binary patterns.

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2015 nî lūn-bûn

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A novel approach for SEMG signal classification with adaptive local binary patterns.

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label

A novel approach for SEMG signal classification with adaptive local binary patterns.

@en

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A novel approach for SEMG signal classification with adaptive local binary patterns.

@en

P1476

A novel approach for SEMG signal classification with adaptive local binary patterns.

@en

P2093

Ramazan Tekin

http://www.w3.org/2001/XMLSchema#string

Yılmaz Kaya

http://www.w3.org/2001/XMLSchema#string

Ömer Faruk Ertuğrul

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P2860

Hybrid soft computing systems for electromyographic signals analysis: a review

EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury.

Characterizing EMG data using machine-learning tools.

Effect of arm position on the prediction of kinematics from EMG in amputees.

Surface EMG and acceleration signals in Parkinson's disease: feature extraction and cluster analysis.

Simulation of surface EMG for the analysis of muscle activity during whole body vibratory stimulation.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine.

Artificial neural network model of the mapping between electromyographic activation and trajectory patterns in free-arm movements.

Surface EMG Signal Classification Using a Selective Mix of Higher Order Statistics.

P304

1137-1146

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P31

scholarly article

P356

10.1007/S11517-015-1443-Z

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P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P577

2015-12-31T00:00:00Z

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P698

26718556

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A novel approach for SEMG signal classification with adaptive local binary patterns.

about

P2860

P2860

A novel approach for SEMG signal classification with adaptive local binary patterns.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P577

P698

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P577

P698