2019 22nd International Conference on Computer and Information Technology (ICCIT) 2019
DOI: 10.1109/iccit48885.2019.9038173
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Heterogeneous Hand Guise Classification Based on Surface Electromyographic Signals Using Multichannel Convolutional Neural Network

Abstract: Electromyography (EMG) is a way of measuring the bioelectric activities that take place inside the muscles. EMG is usually performed to detect abnormalities within the nerves or muscles of a target area. The recent developments in the field of Machine Learning allow us to use EMG signals to teach machines the complex properties of human movements. Modern machines are capable of detecting numerous human activities and distinguishing among them solely based on the EMG signals produced by those activities. Howeve… Show more

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Cited by 3 publications
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“…An interesting method of utilizing cross recurrence plots was implemented in EMG hand movement recognition ( Aceves-Fernandez et al, 2019 ). A multichannel Convolutional Neural Networks (CNN) was also used for the heterogenous hand guise classification depending on the Surface Electromyogram (sEMG) signals ( Sikder et al, 2019 ). Two-channel surface Electromyogram (EMG) signals were utilized to classify the hand and finger movements with ELM classifiers reporting an accuracy of 98.95% ( Sezgin, 2019 ).…”
Section: Introductionmentioning
confidence: 99%
“…An interesting method of utilizing cross recurrence plots was implemented in EMG hand movement recognition ( Aceves-Fernandez et al, 2019 ). A multichannel Convolutional Neural Networks (CNN) was also used for the heterogenous hand guise classification depending on the Surface Electromyogram (sEMG) signals ( Sikder et al, 2019 ). Two-channel surface Electromyogram (EMG) signals were utilized to classify the hand and finger movements with ELM classifiers reporting an accuracy of 98.95% ( Sezgin, 2019 ).…”
Section: Introductionmentioning
confidence: 99%