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Electromyogram (EMG) signals have become more prevalent in recent years for the purpose of hand and finger motion identification. On the other hand, the majority of research have concentrated their attention on the arm and the entire hand, rather than on individual finger (IF) motions, which were thought to be more challenging. EMG-based classifications for hand and finger gestures are being developed using data mining algorithms in this study. Constant circuit arrangement is the basis for these algorithms. Ten individuals in good health were asked to make ten different hand/finger gestures, seven of which were IF movements. Three channels' worth of Electromyogram (EMG) signals was measured, and then each channel's worth of data was broken down into six time-domain (TD) characteristics. Artificial neural networks (ANN), a support vector machine, a random forest (RF) as well as a logistic regression …