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

Behavior 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.