Empirical Analysis of Artificial limb for Trans Radial Amputation People with the support of advanced machine learning strategies

Abstract

For the improvement of performance of trans radial amputees, there will be identification of hand gestures using electromyogram. Whatever the data retrieved from the electrode will be used for controlling the prosthetic hands. In this paper, we are proposing the systematic hand gestures to improve the performance of the trans radial amputation people using the 2-channels amplifier. Different hand gestures like hands up, hands open, hands down, hands close are represented by the different variants and all these gestures will be preprocessed by the discrete approach. Extraction of the features will be done using the combination of ML Strategies. For identifying the pattern, LVQ mechanism is used. By this 2-Channel amplifier the identification of outcome can be evaluated effectively.