AI-Based Classification of Normal and Aggressive Behaviors using EMG Signals

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dc.authorscopusid58735644300en_US
dc.authorscopusid58733826500en_US
dc.authorscopusid57828226000en_US
dc.authorscopusid56779734300en_US
dc.contributor.authorKarakoc, M.
dc.contributor.authorCalgici, E.
dc.contributor.authorKandaz, D.
dc.contributor.authorUcar, M.K.
dc.date.accessioned2024-08-23T16:07:34Z
dc.date.available2024-08-23T16:07:34Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153en_US
dc.description.abstractBehavior analysis using Electromyogram (EMG) signals is an essential step in understanding aggressive behaviors, the physiological behavior of the neuromuscular system, and its applications in other disciplines. Therefore, this study aimed to develop a model for detecting behaviors using EMG signal analysis. This model utilized a dataset consisting of EMG signals from eight channels obtained during a series of activities on four subjects. Accordingly, a classification model was developed to detect specific features of behaviors and differentiate between normal and aggressive behaviors by analyzing the EMG signals. The model was developed by incorporating signal and statistical features and applying different machine-learning techniques. A total of 44 models were evaluated for their performance. The Support Vector Machine (SVM) classification model developed using all extracted features achieved an accuracy rate of approximately % 91 in behavior classification. The obtained results demonstrate the potential of EMG signals as a tool for behavior detection and classification. © 2023 IEEE.en_US
dc.identifier.doi10.1109/ASYU58738.2023.10296732
dc.identifier.isbn979-835030659-0en_US
dc.identifier.scopus2-s2.0-85178260461en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ASYU58738.2023.10296732
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14731
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAggressive Behavioren_US
dc.subjectClassificationen_US
dc.subjectElectromyogramen_US
dc.subjectMachine Learningen_US
dc.subjectNormal Behavioren_US
dc.subjectStatistical Featuresen_US
dc.subjectBiomedical signal processingen_US
dc.subjectLearning systemsen_US
dc.subjectSignal detectionen_US
dc.subjectSupport vector machinesen_US
dc.subjectAggressive behavioren_US
dc.subjectBehavior analysisen_US
dc.subjectBehaviour classificationen_US
dc.subjectClassification modelsen_US
dc.subjectElectromyo gramsen_US
dc.subjectElectromyogram signalsen_US
dc.subjectMachine-learningen_US
dc.subjectNeuromuscular systemsen_US
dc.subjectNormal behavioren_US
dc.subjectStatistical featuresen_US
dc.subjectClassification (of information)en_US
dc.titleAI-Based Classification of Normal and Aggressive Behaviors using EMG Signalsen_US
dc.typeConference Objecten_US

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