An EMG Controlled Bionic Arm with Machine Learning

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dc.authorscopusid58753792700
dc.authorscopusid58753453000
dc.authorscopusid58753380700
dc.authorscopusid23980961100
dc.contributor.authorUygun, M.I.
dc.contributor.authorRecber, B.
dc.contributor.authorCelikli, M.A.
dc.contributor.authorOniz, Y.
dc.date.accessioned2024-07-18T20:17:10Z
dc.date.available2024-07-18T20:17:10Z
dc.date.issued2023
dc.description7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 -- 26 October 2023 through 28 October 2023 -- -- 194332en_US
dc.description.abstractThis study uses electromyography (EMG) signals and machine learning techniques to create a bionic arm specifically made for amputees. The user's intended movements can be decoded and converted into orders for the bionic arm by observing and analyzing these signals. An EMG sensor was initially placed on the surface of particular muscles in the lower arm as part of the project's data-collection phase. As the muscles performed various movements, the electrodes captured the bioelectric signals they produced. These recorded bioelectrical signals are classified according to each movement by going through a series of processes, and it was aimed to increase the functionality of the bionic arm by gaining many movements control. The results showed promising advancements in the field and highlighted the potential for improving the quality of living for people with limb loss. © 2023 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT58785.2023.10304962
dc.identifier.isbn9798350342154
dc.identifier.scopus2-s2.0-85179134957en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT58785.2023.10304962
dc.identifier.urihttps://hdl.handle.net/11411/6424
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBioelectrical Signalsen_US
dc.subjectBionicen_US
dc.subjectElectromyography (Emg)en_US
dc.subjectLimb Lossen_US
dc.subjectMachine Learningen_US
dc.subjectMechanical Arm Designen_US
dc.subjectProstheticsen_US
dc.subjectBionicsen_US
dc.subjectMachine Learningen_US
dc.subjectMuscleen_US
dc.subjectBioelectrical Signalsen_US
dc.subjectElectromyographyen_US
dc.subjectElectromyography Signalsen_US
dc.subjectLimb Lossen_US
dc.subjectLower Armen_US
dc.subjectMachine Learning Techniquesen_US
dc.subjectMachine-Learningen_US
dc.subjectMechanical Armen_US
dc.subjectMechanical Arm Designen_US
dc.subjectProject Dataen_US
dc.subjectArtificial Limbsen_US
dc.titleAn EMG Controlled Bionic Arm with Machine Learning
dc.typeConference Object

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