xun911/CIMS_sEMG_dualstreamCNN
Surface electromyography based gesture recognition based on dual-stream CNN
This project helps researchers and engineers analyze surface electromyography (sEMG) signals to identify human hand gestures. It takes pre-processed sEMG data as input and outputs classifications of specific hand movements, assisting in studies related to human-computer interaction, prosthetics, or rehabilitation. The primary users are researchers or practitioners working with bio-signal processing for gesture recognition.
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Use this if you are a researcher or engineer working with pre-processed sEMG data and need to classify distinct hand gestures using a dual-stream Convolutional Neural Network.
Not ideal if you need tools for the initial feature extraction from raw sEMG signals, as that functionality is not included.
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Language
Python
License
AGPL-3.0
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Last pushed
Jun 07, 2023
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