happens, and its duration. Moreover, to develop a system with acceptable portability and usability characteristics, it needs to be able to work with limited computational resources [2; 7; 8]. Currently, several works are focused on hand gesture classification instead of recognition [8; 9; 10; 11]. Classification models only identify the class of a given hand movement from a predefined set of gesture classes. In other words, the classification only returns a label that identifies the gesture (which class), whereas the recognition returns the label as well as its timestamp (when it is produced and how long it lasts). As a result, the problem of gesture recognition is harder than classification. Moreover, a recognition model as part of its architecture, usually includes a classification model [7]. There are different types of sensors to develop hand gesture recognition models such as gloves equipped with force and flexion sensors [12; 13], optical and infrared cameras [14], electromyographic (EMG) sensors [15], and inertial measurement units (IMUs) [16; 17; 18; 19]. Each of these sensors has problems to solve to achieve promising results in hand gesture recognition applications. For example, in vision-based gesture recognition models, the detection and tracking of the hand movement may be affected by changes of illumination and occlusions [20]. Using a glove can be uncomfortable if the glove does not fit the user's hand properly or if it is used for a long period of time. Signals measured with IMUs (e.g., angular velocity and acceleration) are usually noisy, thus making it difficult to extract the part of the signal that conveys information of the gesture executed. For this work, EMG sensors are used because they do not suffer from some of the problems mentioned above. For example, EMG sensors do not have problems because of occlusion and changes in illumination; they are usually easy and comfortable to use, and EMG sensors return less noisy signals than IMUs. EMG sensors measure electric signals on skeletal muscles. These electric signals, called EMGs, contain information of a voluntary movement commanded by the brain. EMG sensors can be of two types: intramuscular and surface sensors. On one hand, intramuscular sensors use a set of invasive electrodes (i.e., needles), which make good and localized contact with the part of a skeletal muscle under examination, thus making it possible to record EMGs from deep skeletal muscles, with little interference. However, intramuscular sensors are not practical for implementing gesture recognition models because one or more needles must be inserted in the muscle causing pain to the user. On the other hand, a surface sensor consists of a set of non-invasive electrodes, such as wearable bracelets or adhesive pods, so wearing this type of sensor is easy and comfortable [21; 22]. For this reason, in this work, we use a surface EMG sensor called a Myo armband produced by Thalmic Labs (Kitchener, Ont.,Canada). As we mentioned before, an EMG sensor measures the electrical signals when a voluntary movement is produced due to the contraction of skeletal muscles. This contraction is produced by the activation of a set of muscle fibers, called a motor unit (MU), where each fiber of the MU is connected to the axon of a motor neuron. The force produced by a muscle depends on the number of MUs activated by the brain. MUs contract when a train of electrical pulses, generated by the brain, reaches each of its fibers. This train of pulses that reach a MU is called Motor Unit Action Potential Train (MUAPT). The superposition (i.e., linear summation) of the MUAPTs of several motor units is what we call an EMG. Additionally, an EMG contains not only MUAPTs but also Gaussian noise [23] due to signals generated by nearby organs, environment noise, or noise produced by the electrical equipment used to measure the EMG. It is important to highlight that an EMG signal behaves as a non-stationary process because the distribution of these signals change with time.