The statistics of an EMG depend on several factors such as the location of the sensor, coronal temperature, and body fatness [24]. Consequently, defining a mathematical model to characterize an EMG signal is a difficult task. In Figure 1, we show an example of EMGs measured by the Myo armband for each of the five gestures recognized in this work: _wave in_, _wave out_, \(fist\), \(fingers\)_spread_, and _double tap_. These five gestures are the ones recognized by the proprietary system of the Myo armband, a sensor that returns EMGs composed of eight channels measured at 200 Hz. It is worth mentioning that, in other works, some authors used sensors with EMGs sampling rates from 200 to 1000 Hz obtaining sundry results in their recognition systems [7]. However, the Myo armband sensor sampling rate--200 Hz--has been demonstrated to provide high-performance results in hand gesture recognition applications, which make it one of the most popular EMGs sensor in the market [7; 25]. There are two types of hand gesture recognition models: user-specific and general. On one hand, a general model is designed to recognize gestures from any user. For the training phase, a general model uses the data gathered from a set of users, and then the model can be tested with other users. Therefore, a general model must be trained only once. On the other hand, a user-specific model must be trained and tested with the data of each user. Although user-specific models must be retrained for a new user, it is well-known that these types of models provide higher accuracy than general models. Consequently, for this research, we have chosen to design a user-specific model. It is important to note that even a slight variation of the orientation of the EMG sensor between training and testing can cause the reduction of the performance in terms of accuracy for user-specific and general models [26]. This variation happens when the sensor's pods are placed in a different position every time a user wears the armband. Therefore, a correction of the sensor orientation is needed to compensate for any change in the orientation between training and testing. There are few works in the literature that address the orientation correction problem of the EMG sensor. For example, a set of experiments was carried out in [26] that demonstrate that shifting the sensor 2 cm can cause the classification accuracy to drop considerably with accuracy between 50% and 60%. In another work, it is demonstrated that, if the sensor is rotated, a remapping can be performed to reorganize the distribution of the sensors and thus try to avoid drops in the classification accuracy due to the incorrect position of the sensor [27]. Finally, another method can be applied by analyzing the angle shift of the EMG sensor [28]. However, such approaches were computationally expensive due to the high complexity of the calculations required to perform the orientation correction procedure. Some researchers [11; 27; 28] use the Myo armband or the Delsys sensor [29] to measure EMGs. Even though they claim to propose hand gesture recognition systems; in practice, they only perform classification. As it was mentioned before, classification is different from recognition, so most of the literature provides classification systems instead of recognition systems. In [26], a high-density electrode array is used. This sensor array is composed of 96 channels that measure EMGs used to distinguish 11 hand and wrist movements. However, this proposal is focused on the classification task only. Regarding the correction of sensor orientation, there are a few works that tackle this problem using the Myo armband [27; 28]. Despite these two works solving the problem of the correction of sensor rotation, they do not perform recognition. Therefore, it is important to design systems that perform recognition with the correction of sensor orientation. Figure 1: Examples of EMGs of the five hand gestures used in this work. In this work, a user-specific model with correction of sensor orientation is proposed.