scholarly journals Feature Extraction from sEMG of Forearm Muscles, Performance Analysis of Neural Networks and Support Vector Machines for Movement Classification

2017 ◽  
Author(s):  
Luis Morales ◽  
Jaime Cepeda
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Performance Analysis ◽  
Support Vector ◽  
Forearm Muscles ◽  
Vector Machines ◽  
Movement Classification

scholarly journals Multifunctional Prosthesis Control with Simulation of Myoelectric Signals

2020 ◽  
Author(s):  
João Fermeiro ◽  
Filipa Moreira ◽  
José Pombo ◽  
Rosário Calado ◽  
Sílvio Mariano
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Signal Conditioning ◽  
Myoelectric Signals ◽  
Emg Signal ◽  
Vector Machines

The skeletal muscle activation generates electric signals called myoelectric signals. In recent years a strong scientific activity has been developed in the recognition of limb movements from electromyography (EMG) signals recorded from non-invasive (surface) electrodes, in order to design systems for prosthetic control. Surface EMG acquire the activation of surrounding muscles and for that reason the obtained signal needs to be conditioned and processed, with pattern recognition techniques for extraction and classification. In this work EMG signals were acquired for two hand movements, “hand close” and “hand open”.  The EMG electrodes were placed on the forearm  and positioned over the extensor digitorum muscle, for the “hand open” and flexor digitorum muscle, for the “hand close”. Using MATLAB software the signal conditioning, feature extraction and classification were performed. The feature extraction process was carried with the Wavelet Packet Transform (WPT) technique and the classification process was done with two different techniques for comparison purposes, Neural Networks (NN) and Support Vector Machines (SVM). The results show that the SVM classifier used presented better classification performance compared to NN classifier used. Keywords: EMG, Signal conditioning, Wavelet Packet Transform (WPT), Neural Networks (NN), Support Vector Machines (SVM)

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

2018 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Rui Liu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Tool Wear ◽  
Support Vector ◽  
Workpiece Material ◽  
Artificial Intelligent ◽  
Tool Condition ◽  
Hardness Variation ◽  
Vector Machines ◽  
Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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