Karakoc, M.Calgici, E.Kandaz, D.Ucar, M.K.2024-08-232024-08-232023979-835030659-0https://doi.org/10.1109/ASYU58738.2023.10296732https://hdl.handle.net/20.500.12684/147312023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153Behavior 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.en10.1109/ASYU58738.2023.10296732info:eu-repo/semantics/closedAccessAggressive BehaviorClassificationElectromyogramMachine LearningNormal BehaviorStatistical FeaturesBiomedical signal processingLearning systemsSignal detectionSupport vector machinesAggressive behaviorBehavior analysisBehaviour classificationClassification modelsElectromyo gramsElectromyogram signalsMachine-learningNeuromuscular systemsNormal behaviorStatistical featuresClassification (of information)Conference Object2-s2.0-85178260461N/A