O levantamento da exposicao dos ruidos e vibracoes que sofrem o corpo humano, em pessoas queopera... more O levantamento da exposicao dos ruidos e vibracoes que sofrem o corpo humano, em pessoas queoperam maquinas escavadoras, tratores e demais veiculos e incompleto. Porem existem evidencias de quea exposicao regular aos ruidos e vibracoes pode contribuir na geracao de problemas no aparelho auditivoe de dores nas costas em motoristas profissionais, tais como motoristas de onibus, de tratores, e helicopteros.Utilizando o prototipo de um mini-baja foram avaliados os niveis de pressao sonora e exposicoes asvibracoes emitidos pelo prototipo fora de estrada de um mini-baja. Os dados adquiridos foram analisadosestatisticamente na tentativa de extrair informacoes uteis como identificar os niveis e frequencias que maisafetam os seres humanos.Palavras chaves: ruido, vibracao, veiculo automotor.
This study aims at estimating a virtual surface Electromyography (sEMG) channel through a Recurre... more 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 contaminated with noise saturation. The hit rate from the clean data has averaged 73.96% ± 3.02%. The classification from the contaminated data of one of the channels has improved from 9.29% ± 4.42% to 66.48% ± 6.11% with the virtual channel.
Despite all the recent developments of using the surface electromyography (sEMG) as a control sig... more 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 Filter (ESF) and our adaptation of Antonyan Vardan Transform (AVT). The classifiers were evaluated using sEMG data acquired through 12 channels from four subjects performing 17 different movements of forearm and fingers with three repetitions each. In the best scenario, our methods reached results higher than 96% and 82% of overall and weighted accuracy, respectively. Those results match or outperform similar papers of the literature using a simpler model, which may help the application of the techniques on embedded platforms and make the practical use of such devices more feasible.
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
The capacity to identify the contamination in surface electromyography (sEMG) signals is necessar... more 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 line interference, amplifier saturation, and electrode displacement. The results demonstrated that the sEMG signal with the contaminants could readily be distinguished, even with increase channels degraded. The SFTD detection depends on the noise type, whether the amputee or non-amputee subjects and which channel is being analysed. This method presented a suitable solution for the detection of contaminants in the sEMG signal, being able to provide the acquired signal validation before the movement intended recognition to operate in an intelligent recognition with greater reliability.
2020 IEEE PES Transmission & Distribution Conference and Exhibition - Latin America (T&D LA), 2020
The load forecasting is important for the distribution system operation and expansion planning. T... more The load forecasting is important for the distribution system operation and expansion planning. The main methodologies for load forecasting using deep learning are Long Short-Term Memory (LSTM) and Convolution Neural Networks (CNN). LSTM is specialized in sequential data; on the other hand CNN is specialized in image data. The residential consumption can be treated as a time series (sequential data) and a two-dimensional (image data) dataset. Therefore, LSTM and CNN can be used to extract data characteristics from the residential consumption dataset. Thus, this paper reviews and compares the main methodologies for residential load forecasting such as CNN, LSTM, and CNN-LSTM. The mean square error (MSE) and root mean square error (RMSE) are used as metrics. The dataset is from real residential consumers in Ireland. The result shows a similar performance in training and testing. The best results are found when CNN and LSTM are used together.
The paper describes a system for controlling a virtual mouse using the mioelectric signal from vo... more The paper describes a system for controlling a virtual mouse using the mioelectric signal from voluntary contractions of the masseter and temporal muscles. The average energy of each of the data packets is compared to a threshold, established by a process of personal calibration executed before start- ing the system. The threshold energy value was computed using two different adaptive techniques: Linear Energy Based Detector (LED) and Adaptive Linear Energy Based Detector (ALED). Two volunteers were submitted to 90 facial contractions in order to control the mouse and test the system with the proposed techniques. The Linear Energy Based detector presented 17% of failures, the adaptive Linear Energy Based Detector presented 15% of failures and the static threshold presented 26% of failures during the commands detection. Thus the preliminary results have shown that adaptive techniques are robust alternatives for threshold events detection.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Aug 1, 2016
Brain-computer interfaces (BCI) provide means of communications and control, in assistive technol... more Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments in this field is also conducted. Processing of the electroencephalography (EEG) signals is done applying Common Spatial Pattern filters. The EPOC electrodes cap is used for EEG acquisition, in two test subjects, for two distinct trial formats. The channels picked are FC5, FC6, P7 and P8 of the 10-20 system, and a discussion about the differences of using C3, C4, P3 and P4 positions is proposed. Dataset 3 of the BCI Competition II is also analyzed using the implemented algorithms. The maximum classification results for the proposed experiment and for the BCI Competition dataset were, ...
O levantamento da exposicao dos ruidos e vibracoes que sofrem o corpo humano, em pessoas queopera... more O levantamento da exposicao dos ruidos e vibracoes que sofrem o corpo humano, em pessoas queoperam maquinas escavadoras, tratores e demais veiculos e incompleto. Porem existem evidencias de quea exposicao regular aos ruidos e vibracoes pode contribuir na geracao de problemas no aparelho auditivoe de dores nas costas em motoristas profissionais, tais como motoristas de onibus, de tratores, e helicopteros.Utilizando o prototipo de um mini-baja foram avaliados os niveis de pressao sonora e exposicoes asvibracoes emitidos pelo prototipo fora de estrada de um mini-baja. Os dados adquiridos foram analisadosestatisticamente na tentativa de extrair informacoes uteis como identificar os niveis e frequencias que maisafetam os seres humanos.Palavras chaves: ruido, vibracao, veiculo automotor.
This study aims at estimating a virtual surface Electromyography (sEMG) channel through a Recurre... more 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 contaminated with noise saturation. The hit rate from the clean data has averaged 73.96% ± 3.02%. The classification from the contaminated data of one of the channels has improved from 9.29% ± 4.42% to 66.48% ± 6.11% with the virtual channel.
Despite all the recent developments of using the surface electromyography (sEMG) as a control sig... more 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 Filter (ESF) and our adaptation of Antonyan Vardan Transform (AVT). The classifiers were evaluated using sEMG data acquired through 12 channels from four subjects performing 17 different movements of forearm and fingers with three repetitions each. In the best scenario, our methods reached results higher than 96% and 82% of overall and weighted accuracy, respectively. Those results match or outperform similar papers of the literature using a simpler model, which may help the application of the techniques on embedded platforms and make the practical use of such devices more feasible.
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
The capacity to identify the contamination in surface electromyography (sEMG) signals is necessar... more 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 line interference, amplifier saturation, and electrode displacement. The results demonstrated that the sEMG signal with the contaminants could readily be distinguished, even with increase channels degraded. The SFTD detection depends on the noise type, whether the amputee or non-amputee subjects and which channel is being analysed. This method presented a suitable solution for the detection of contaminants in the sEMG signal, being able to provide the acquired signal validation before the movement intended recognition to operate in an intelligent recognition with greater reliability.
2020 IEEE PES Transmission & Distribution Conference and Exhibition - Latin America (T&D LA), 2020
The load forecasting is important for the distribution system operation and expansion planning. T... more The load forecasting is important for the distribution system operation and expansion planning. The main methodologies for load forecasting using deep learning are Long Short-Term Memory (LSTM) and Convolution Neural Networks (CNN). LSTM is specialized in sequential data; on the other hand CNN is specialized in image data. The residential consumption can be treated as a time series (sequential data) and a two-dimensional (image data) dataset. Therefore, LSTM and CNN can be used to extract data characteristics from the residential consumption dataset. Thus, this paper reviews and compares the main methodologies for residential load forecasting such as CNN, LSTM, and CNN-LSTM. The mean square error (MSE) and root mean square error (RMSE) are used as metrics. The dataset is from real residential consumers in Ireland. The result shows a similar performance in training and testing. The best results are found when CNN and LSTM are used together.
The paper describes a system for controlling a virtual mouse using the mioelectric signal from vo... more The paper describes a system for controlling a virtual mouse using the mioelectric signal from voluntary contractions of the masseter and temporal muscles. The average energy of each of the data packets is compared to a threshold, established by a process of personal calibration executed before start- ing the system. The threshold energy value was computed using two different adaptive techniques: Linear Energy Based Detector (LED) and Adaptive Linear Energy Based Detector (ALED). Two volunteers were submitted to 90 facial contractions in order to control the mouse and test the system with the proposed techniques. The Linear Energy Based detector presented 17% of failures, the adaptive Linear Energy Based Detector presented 15% of failures and the static threshold presented 26% of failures during the commands detection. Thus the preliminary results have shown that adaptive techniques are robust alternatives for threshold events detection.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Aug 1, 2016
Brain-computer interfaces (BCI) provide means of communications and control, in assistive technol... more Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments in this field is also conducted. Processing of the electroencephalography (EEG) signals is done applying Common Spatial Pattern filters. The EPOC electrodes cap is used for EEG acquisition, in two test subjects, for two distinct trial formats. The channels picked are FC5, FC6, P7 and P8 of the 10-20 system, and a discussion about the differences of using C3, C4, P3 and P4 positions is proposed. Dataset 3 of the BCI Competition II is also analyzed using the implemented algorithms. The maximum classification results for the proposed experiment and for the BCI Competition dataset were, ...
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Papers by Alexandre Balbinot