Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition

2021 ◽  
Vol 68 ◽  
pp. 102577
Author(s):  
Yang Zhou ◽  
Chaoyang Chen ◽  
Mark Cheng ◽  
Yousef Alshahrani ◽  
Sreten Franovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Methods ◽  
Semg Signal ◽  
Motion Recognition ◽  
Machine Learning Methods ◽  
Shoulder Motion

Heart Rate Classification Using ECG Signal Processing and Machine Learning Methods

2021 ◽  
Author(s):  
Maria Papadogiorgaki ◽  
Maria Venianaki ◽  
Paulos Charonyktakis ◽  
Marios Antonakakis ◽  
Ioannis Tsamardinos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Signal Processing ◽  
Ecg Signal ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ecg Signal Processing

scholarly journals Machine Learning Methods for Social Signal Processing

2017 ◽  
pp. 234-254 ◽  
Author(s):  
Ognjen Rudovic ◽  
Mihalis A. Nicolaou ◽  
Vladimir Pavlovic
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Social Signal Processing ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Social Signal

Guest Editorial: Special Issue on Machine Learning Methods in Signal Processing

2004 ◽  
Vol 52 (8) ◽  
pp. 2152-2152
Author(s):  
M. Feder ◽  
M.A.T. Figueiredo ◽  
A.O. Hero ◽  
C.-H. Lee ◽  
H.-A. Loeliger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Guest Editorial ◽  
Special Issue ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Editorial Special Issue

Hand Gesture Recognition Based on sEMG Signal and Convolutional Neural Network

2021 ◽  
pp. 2151012
Author(s):  
Ziyi Su ◽  
Handong Liu ◽  
Jinwu Qian ◽  
Zhen Zhang ◽  
Lunwei Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Hand Gestures ◽  
Learning Methods ◽  
Semg Signal ◽  
Machine Learning Methods ◽  
Semg Signals

Recently, deep learning has become a promising technique for constructing gesture recognition classifiers from surface electromyography (sEMG) signals in human–computer interaction. In this paper, we propose a gesture recognition method with sEMG signals based on a deep multi-parallel convolutional neural network (CNN), which solves the problem that traditional machine learning methods may lose too much useful information during feature extraction. CNNs provide an efficient way to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. Sophisticated feature extraction is to be avoided and hand gestures are to be classified directly. A multi-parallel and multi-convolution layer convolution structure is proposed to classify hand gestures. Experiment results show that in comparison with five traditional machine learning methods, the proposed method could achieve higher accuracy.

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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