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Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks

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dc.contributor.author Oleinikov, Artemiy
dc.contributor.author Abibullaev, Berdakh
dc.contributor.author Shintemirov, Almas
dc.contributor.author Folgheraiter, Michele
dc.date.accessioned 2019-09-10T10:26:04Z
dc.date.available 2019-09-10T10:26:04Z
dc.date.issued 2018-01-05
dc.identifier.citation Oleinikov, A., Abibullaev, B., Shintemirov, A., & Folgheraiter, M. (2018). Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (Vol. 2018-January, pp. 1-5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311527 en_US
dc.identifier.uri https://ieeexplore.ieee.org/document/8311527
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/4231
dc.description.abstract Electromyography (EMG) signal analysis is one of the key determinants of the effectiveness of prosthetic devices. Modern researchers provide various methods of detection of different hand movements and postures. In this work, we examined the possibility to produce efficient detection of hand movement to a specific posture with the minimum possible number of electrodes. The data acquisition is produced with 1 channel BiTalino EMG sensor based on bipolar differential measurement. Using feature extraction and artificial neural network we achieved 82% of offline classification accuracy for 8 hand motions and 91% accuracy for 6 hand motions based on 200 ms of EMG signal. Also, the motion detection algorithm was developed and successfully tested that allowed to implement the algorithm for real-time classification and that showed sufficient accuracy for 2 and 4 motion classes cases. en_US
dc.language.iso en en_US
dc.publisher Nazarbayev University, School of Science and Technology en_US
dc.title Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks en_US
dc.type Conference Paper en_US
workflow.import.source science


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