Detection of forearm movements using wavelets and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Guvenc, Seyit Ahmet; Demir, Mengu; Ulutaş, Mustafa

doi:10.1109/inista.2014.6873617

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…The overall percentage on improvement is around 13%, considering all movements. Additionally, the movements with higher accuracy rate were identified (1,3,6,7,10,(13)(14)(15)(16)(17), which may give an expected good result of the method applied on a prosthetic limb.…”

Section: Discussionmentioning

confidence: 91%

“…Among the most popular ML techniques used to perform the signal classification are methods such as Linear Discriminant Analysis (LDA) [10,11], Artificial Neural Networks (ANN) [12,13], Fuzzy Logic, Neuro Fuzzy [14,15], Genetic Algorithms, Support Vector Machines (SVM) [10,17] and Logistic Regression [18]. More recently, methods as Independent Component Analysis (ICA) are been used to identify different muscle contribution to the formation of sEMG signal [19].…”

Section: Introductionmentioning

confidence: 99%

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

Cene

Balbinot

2016

BJIC

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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: 91%

Section: Introductionmentioning

confidence: 99%

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

Cene

Balbinot

2016

BJIC

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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

Cene

Balbinot

2016

BJIC

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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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“…However, another classification technique such as k nearest neighbor and SVM are also popular for EMG signal recognition as they too provide good results [7,8]. Some of the researchers also utilized the combination of different neural and fuzzy classification techniques for classification [9,10,11,12] like a combination of wavelet neural network and fuzzy classifier. Fuzzy based classifiers also have been used by many researchers [13,14].…”

Section: Introductionmentioning

confidence: 99%

EMG signal based finger movement recognition for prosthetic hand control

Haris

Chakraborty

Rao

2015

2015 Communication, Control and Intelligent Systems (CCIS)

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Electromyography (EMG) signal can be defined as a measure of electrical activity produced by skeletal muscles. It can be used in handling electronic devices or prosthesis. If we are able recognize the hand gesture captured using EMG signal with greater reliability and classification rate, it could serve a good purpose for handling the prosthesis and to provide the good quality of life to amputees and disabled people. In this paper, we have worked on recognizing the 9 classes of individual and combined finger movement captured using 2 channel EMG sensor. We have used two different classification techniques such as Artificial Neural Network (ANN), and k-nearest neighbors (KNN), to classify the test samples. Seven time domain features a) Mean absolute value, b) root mean square, c) variance, d) waveform length, e) number of zero crossing, f) complexity, g) mobility have been used to uniquely represent the EMG channel data. Tuning parameters like number of hidden layers, learning constant and number of neighbors have been determined from the experimental results to achieve the better classification results. Classification accuracy has been selected as a metric to evaluate the performance of each classifier.

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Detection of forearm movements using wavelets and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Cited by 5 publications

References 19 publications

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

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

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

EMG signal based finger movement recognition for prosthetic hand control

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