Gesture recognition is an important topic in modern IoT applications, being used to control mobile apps, robotics and also videogames. Many technologies are in use to detect gestures and make them suitable to digital processing and machine learning classifier. One widely used way to collect data, especially in biomedical researches, is based on surface ElectroMyoGraphic (sEMG) signals, obtained simply applying non-invasive electrodes on the skin of the area of interest. This approach makes gesture recognition suitable for Human-Machine Interface (HMI), like prosthesis and robotic limb control. This thesis work studies a possible implementation of hand gesture recognition, using a system based on the Average Threshold Crossing (ATC) event-driven feature of the forearm sEMG signals.