@article{MAKHILLJEAS2019141117908,
    title = {CNN Architectures for Hand Gesture Recognition using
EMG Signals Throw Wavelet Feature Extraction},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {11},
    pages = {3528-3537},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.3528.3537},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.3528.3537},
    author = {Natalie Segura,Robinson Jimenez and},
    keywords = {Deep convolutional neural network,EMG signal,wavelet power spectrum,discrete wavelet transform,gesture recognition,validation},
    abstract = {This study presents the implementation of 3 convolutional neural network architectures for the
recognition of hand gestures by means of electromyographic signals. The acquisition of signals is done by
means of electrodes located in the forearm and the development platform specialized in biomedical signals
MySignals HW V2.0 which will be applied a pre-processing of the signal by means of the Wavelet Packet
Transform (WPT) for the feature extraction. The architectures that are proposed have as input base to the
network the map of features obtained by the wavelet power spectrum with which the database of training and
validation was constructed. Finally, in the tests perform in real time, the first architecture reached an accuracy
of 93.8325%, the second architecture, reaches a degree of accuracy of 95.8824% and finally, the third
architecture reaches an accuracy of 96.4706%. This means that the architecture with the highest accuracy
performs better when it comes to recognizing gestures, even with small databases.}
    }