Design and Implementation of Myoelectric Controlled Arm

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dc.contributor.authorYounes, Tariq M.
dc.contributor.authorAlKhedher, Mohammad
dc.contributor.authorSoliman, Abdel-Hamid
dc.contributor.authorETAL..
dc.date.accessioned2022-02-22T12:31:52Z
dc.date.accessioned2023-08-23T05:12:30Z
dc.date.available2022-02-22T12:31:52Z
dc.date.available2023-08-23T05:12:30Z
dc.date.issued2021-07
dc.description.abstractIn this paper, a discrimination system, using a neural network for electromyogram (EMG) externally controlled Arm is proposed. In this system, the Artificial Neural Network (ANN) is used to learn the relation between the power spectrum of EMG signal analysed by Fast Fourier Transform (FFT) and the performance desired by handicapped people. The Neural Network can discriminate 4 performances of the EMG signals simultaneously. The digital signal processing was realized using MATLAB and LabVIEW software.en_US
dc.identifier.citationYounes, T. M., AlKhedher, M. A., Soliman, A. H., & Al Alawin, A. (2021). Design and Implementation of Myoelectric Controlled Arm. International Journal of Control Systems and Robotics, 6.en_US
dc.identifier.urihttps://dspace-uat.adu.ac.ae/handle/1/2728
dc.language.isoenen_US
dc.publisherInternational Association of Research and Scienceen_US
dc.subjectElectromyogramen_US
dc.subjectNeural networken_US
dc.subjectBiosignalen_US
dc.subjectGripping and rotatingen_US
dc.titleDesign and Implementation of Myoelectric Controlled Armen_US
dc.title.alternativeJournal Articleen_US
dc.typeArticleen_US

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