HAND GESTURE RECOGNITION FOR EMG SIGNAL BASED ON DIFFERENT MACHINE LEARNING METHODS
DOI:
https://doi.org/10.29121/digisecforensics.v3.i1.2026.99Keywords:
RF, SVM, KNN, GBM, FIRAbstract
Muscle electrical activity is measured by electromyography (EMG) signals, which are typically expressed as frequency, amplitude, and phase as function of time. Applications for biosignals include the diagnosis of neuromuscular disorders, the control of assistive devices, like orthotic or prosthetic devices, the control of machinery, computers, robots, etc. For the purpose of improving hand gesture recognition, the presented study employed EMG signals, band pass filtering, and finite impulse response for removing artifacts as well as low-frequency noise (like baseline drift or body movement) and high-frequency noise (like electrical interference) impairing the performance of the system. In order to more precisely identify hand gestures, efficient methods have been employed for classifying gestures utilizing a variety of machine learning (ML) classifiers (SVM, Random Forest (RF), KNN, and Gradient Boosting). The classification accuracy using Random Forest (99.9%) reached, accuracy using SVM (97.4%), accuracy using KNN (99.1%). while the classification accuracy using Gradient Boosting (99.7%)
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