Gesture recognition is a trending topic in modern technology, being used to control mobile apps, robotics and also videogames. Many approaches are in use to detect gestures and make them suitable for digital processing and machine learning classifier. ?? For this specific application, data are collected starting from the surface ElectroMyoGraphic (sEMG) signals, obtained applying non-invasive electrodes on a selected skin area. The acquisition setup makes gesture recognition also suitable for Human-Machine Interface (HMI), like prosthesis and robotic limb control.?? This thesis work offers a wide overview of the machine learning algorithms used for hand gesture recognition.?? Considering low-energy consumption as key feature, the system is based on an event-driven approach focused on the Average Threshold Crossing (ATC) information.