Upper-limb movement classification through logistic regression sEMG signal processing

2016

BJIC

Self Cite

This paper aims to present the development of a computational intelligence method based on Regularized Logistic Regression able to classify 17 distinguish upper-limb movements through the sEMG signal processing. The choose of the tuning parameters of the regularization and the generation of the different classification methods are presented. For the different models were used variations involving 12 sEMG channels and the RMS, Variance and Medium Frequency features with which we proposed to achieve a most proper combination of parameter to perform the movements classification. The tests involved 50 subjects, including 10 amputees, using the NinaPro database and also a database currently on development by the authors. The global mean accuracy rate considering all the subjects and the channel and features variations was 70,2% prior the definition of the best case scenario. Once we defined the most proper channel and features combination, we were able to improve the accuracy rate to 87,1%, raising the rates of all movements performed for all databases.

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Section: Discussionmentioning

confidence: 94%

Section: E Regularized Logistic Regression Model Fittingmentioning

confidence: 99%

“…With the ( ) as a strictly convex function, we prevent the optimization from stuck in local minimum values. As detailed in [17], the regularized model of LR is presented on Equation 4.…”

Section: E Regularized Logistic Regression Model Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing

2016

BJIC

Self Cite

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“…Typically, a sEMG signal movement classification consists on a pattern recognition / classification algorithm, which includes several popular methods such as LDA [2,3], Artificial Neural Networks (ANN) [4,5], Fuzzy Logic [6,7], Neuro Fuzzy [8], Genetic Algorithms, Support Vector Machines [9], Bayesian Networks [10][11][12] and Logistic Regression [13]. There are also some approaches using Independent Component Analysis (ICA) [14] and Principal Component Analysis (PCA) [15,16] focusing on dimensionality reduction and efficient computation, techniques focused on provide more efficiency to classification stage.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Method for sEMG Movement Classification Based on Continuous Optimal Electrode Assortment

Favieiro

2016

BJIC

Self Cite

Abstract-Surface electromyography (sEMG) analysis is becoming increasingly popular in a broad variety of applications. Despite satisfactory classification rates are frequently obtained through supervised machine learning (ML) algorithms, there are some issues mostly related to the data acquisition which are not properly addressed in current studies. In this paper we present a method capable of mitigate the noise in the sEMG acquisition caused mainly by loose or misplaced non-invasive electrodes. To address this issue we propose a stage of pre-processing capable of being adapted on a variety of classifiers. The proposed method is capable of identify this two anomalies in the signal and provide the data to retrain the classifier, discarding the problematic channels. Once the method is retrained using only the most relevant channels it is possible to increase the accuracy rate of the ML method. The method was tested on a database containing five ablebodied subjects and four amputee subjects of both sexes. The average classification accuracy for the adaptive input selection method was 83,96  6,5% for the able-bodied subjects and 61,15  7,7% for the amputees subjects against 72,06  8,0% in ablebodied subjects and 39,77  10,6% for the amputees subjects considering the non-adaptive approach. Both systems make use of the proposed method to classify 9 distinguish upper-limb movements with different degrees of freedom.Index Terms-electrode assortment, upper-limb signal, neural network, auto-adaptive methods, surface electromyography

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Enhancing the classification of hand movements through sEMG signal and non-iterative methods

2019

Health Technol.