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Alexandre Balbinot
Universidade Federal do Rio Grande do Sul
74Publications
8H-index
200Citations
Publications 74
Newest
#1Vincius Horn Cene (UFRGS: Universidade Federal do Rio Grande do Sul)
#2Alexandre Balbinot (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 8
In movement classification through surface electromyography signal processing, the classification method must identify the user’s intention with satisfactory accuracy to promote an adequate biosignal interface. Traditionally, classical methods such as Support Vector Machines, Artificial Neural Networks, and Logistic Regression have been used to this end. Recently, Non-Iterative Methods based on Artificial Neural Networks have been revisited in the form of Random Vector Functional-Link Networks (...
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Jul 1, 2019 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Juliano MachadoH-Index: 2
#2Vinicius Horn Cene (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 3
Last.Alexandre Balbinot (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 8
view all 3 authors...
This study aims at estimating a virtual surface Electromyography (sEMG) channel through a Recurrent Neural Network (RNN) by using Long Short-Term Memory (LSTM) nodes. The virtual channel is used to classify hand postures from the publicly NinaPro database with a multi-class, one-against-all Support Vector Machine (SVM) using the Root Mean Square RMS of the sEMG signal as feature. The classification of the signals through the virtual channel was compared with uncontaminated data and data contamin...
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Jul 1, 2019 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Vinicius Horn Cene (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 3
#2Juliano Machado (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 2
Last.Alexandre Balbinot (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 8
view all 3 authors...
Despite all the recent developments of using the surface electromyography (sEMG) as a control signal, reliable classifications still remain an arduous task due to overlapping classes and classification ripples. In this paper, we present a straightforward approach to avoid classification ripple based on smoothing the arg max value of an Extreme Learning Machine (ELM) classifier. We compare the baseline accuracy of the classifier with an arg max filtered by a traditional Exponential Smoothing Filt...
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#1Vinicius Horn CeneH-Index: 3
Last.Alexandre BalbinotH-Index: 8
view all 4 authors...
Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reli...
1 CitationsSource
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#1Mariano Majolo (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 3
#2Alexandre Balbinot (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 8
There are many studies in the machine learning field for classifying movements using electromyography (EMG) signals and some of them achieve high classification rates. The cost for good performance although, is the long time necessary to train the classifiers. This work proposes a multi-class Support Vector Machine (SVM) running in hardware. It is part of a bigger project which aims to train and classify movements maintaining good classification rates in reduced time. For testing the hardware so...
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#1Maurício Hüsken (UFRGS: Universidade Federal do Rio Grande do Sul)
#2Alexandre Balbinot (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 8
Cycling has grown as a leisure activity, means of transport and mainly as a professional sport. Therefore, deeper studies and research aiming at maximizing the performance of high-level athletes have been made necessary. Additionally to such studies, the area of instrumentation schemes has developed seeking to make possible the measuring and characterization of several parameters for researchers interested in cycling. Regarding the vibrant surging of systems that allow the measuring of magnitude...
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#1Vinicius Horn Cene (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 3
#2Alexandre Balbinot (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 8
Abstract Several Machine Learning techniques have been employed to process sEMG signals in order to provide a reliable control biosignal. Although some papers report accuracy rates superior to 90%, there is a lack of more detailed reasoning for reliable systems capable of providing control signals to users that may, for instance, control a prosthetic device. In this paper, we combined two strategies in order to increase the representativity of the sEMG signals: (a) the use of a stochastic filter...
2 CitationsSource
Jul 1, 2018 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Andre Vieira Pigatto (University of Rio Grande)
#2Raphael Ruschel dos Santos (University of Rio Grande)
Last.Alexandre Balbinot (University of Rio Grande)H-Index: 8
view all 3 authors...
This paper describes the development of an automatic cycling performance measurement system with a Fuzzy Logic Controller (FLC), using Mamdani Inference method, to classify the performance of the cyclist. From data of the average power, its standard deviation and the effective force bilateral asymmetry index, a score that represents the cyclist performance is determined. Data are acquired using an experimental crank arm load cell force platform developed with built-in strain gages and conditioni...
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Jul 1, 2018 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Karina O. A. Moura (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 3
#2Raphael Ruschel (UFRGS: Universidade Federal do Rio Grande do Sul)
Last.Alexandre Balbinot (UFRGS: Universidade Federal do Rio Grande do Sul)H-Index: 8
view all 3 authors...
The capacity to identify the contamination in surface electromyography (sEMG) signals is necessary for applying the sEMG controlled prosthesis over time. In this paper, the method for the automatic identification of commonly occurring contaminant types in sEMG signals is evaluated. The presented approach uses two-class support vector machine (SVM) trained with clean sEMG and artificially contaminated sEMG. The contaminants considered include electrocardiogram interference, motion artefact, power...
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