Gesture recognition is a computer discipline based on mathematical algorithms for the interpretation of human gestures. In recent years, recognizing and identifying hand movements have gained a lot of interest due to the increasing of Human-Computer Interface (HCI) applications like the control of mobile apps, robotics, video games, and, in the clinical field, prosthesis and robotic limb control. Several techniques are currently available for data collection: this thesis focuses on the acquisition of the surface ElectroMyoGraphic (sEMG) signal, by applying noninvasive electrodes on the skin above the muscle of interest. This thesis work studies the implementation of dry electrodes for sEMG acquisition, evaluating signal quality by means of the Signal-to-Noise Ratio (SNRdB).