An interface based on electromyographic (EMG) signals is considered one of the central fields in human-machine interface (HCI) research with broad practical use. This paper presents the recognition of 13 individual finger movements based on the time-frequency representation of EMG signals via spectrograms. A deep learning algorithm, namely a convolutional neural network (CNN), is used to extract features and classify them. Two approaches to EMG data representations are investigated: different window segmentation lengths and reduction of the measured channels. The overall highest accuracy of the classification reaches 95.5% for a segment length of 300 ms. The average accuracy attains more than 90% by reducing channels from four to three
The problem of classifying electromyography signals in each gesture occurs due to the use of a const...
Thesis (Master's)--University of Washington, 2020One fundamental component of much modern human-mach...
With the rapid development of information technology, the quantity of information sharing by human i...
Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in...
Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) ...
This work introduces a method for high-accuracy EMG based gesture identification. A newly developed ...
EMG signal based research is ongoing for the development of simple, robust, user friendly, efficien...
Electromyography (EMG) shows excellent potential for human-machine interaction (HMI) tasks. It refle...
Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented a...
peer reviewedElectromyography (EMG) is a measure of electrical activity generated by the contraction...
Electromyography (EMG) is the study of electrical signals produced by the movement of muscles in the...
Electromyography (EMG) is the technique of collecting electrical signals from the human body for fur...
The use of a small number of Electromyography (EMG) channels for classifying the finger movement is ...
Electromyography (EMG) is the conventional noninvasive method for the estimation of muscle activitie...
This paper presents the classification of EMG signal for multiple hand gestures based on neural netw...
The problem of classifying electromyography signals in each gesture occurs due to the use of a const...
Thesis (Master's)--University of Washington, 2020One fundamental component of much modern human-mach...
With the rapid development of information technology, the quantity of information sharing by human i...
Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in...
Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) ...
This work introduces a method for high-accuracy EMG based gesture identification. A newly developed ...
EMG signal based research is ongoing for the development of simple, robust, user friendly, efficien...
Electromyography (EMG) shows excellent potential for human-machine interaction (HMI) tasks. It refle...
Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented a...
peer reviewedElectromyography (EMG) is a measure of electrical activity generated by the contraction...
Electromyography (EMG) is the study of electrical signals produced by the movement of muscles in the...
Electromyography (EMG) is the technique of collecting electrical signals from the human body for fur...
The use of a small number of Electromyography (EMG) channels for classifying the finger movement is ...
Electromyography (EMG) is the conventional noninvasive method for the estimation of muscle activitie...
This paper presents the classification of EMG signal for multiple hand gestures based on neural netw...
The problem of classifying electromyography signals in each gesture occurs due to the use of a const...
Thesis (Master's)--University of Washington, 2020One fundamental component of much modern human-mach...
With the rapid development of information technology, the quantity of information sharing by human i...