Wavelet Based Classification of Finger Movements with Machine Learning Classifier

Feature extraction and machine learning methods are essential in electromyogram (EMG) based movement discrimination for controlling prosthesis. The purpose of the research is to establish finger movements for an upper-limb prosthesis. In this paper, research looks at how to build an intelligent classification system for finger gestures. Electromyogram (EMG) impulses from a person's muscles can be used to movements control of numerous modern prosthesis. This paper studies the accuracy of finger movement detection using surface EMG data. Across a wide range of subjects, the proposed technique produced results with an average classification accuracy of 86.18 percent.