## 5 Conclusions and Future Work  
In this work, we have presented a model for hand gesture recognition. Together with this model, we have also presented an algorithm for correcting, by software, the orientation of the EMG bracelet that is used to measure EMGs on the forearm for recognizing, in real time, five gestures of the hand: _wave in_, _wave out_, _fist_, _fingers spread_, and _double tap_. The proposed gesture recognition model is user-specific, which means that it is trained and tested with EMGs of the same user, but its architecture is the same for all users. For the architecture of the recognition model, we use a shallow feed-forward artificial neural network, which takes as input a feature vector composed of the covariances between the channels of an EMG and a bag of five functions applied to each channel of an EMG. The algorithm for the correction of orientation is based on finding the index of the channel of maximum energy for a set of synchronization EMGs, which for this work corresponds to the gesture _wave out_.  
The average classification and recognition accuracies of the proposed model are relatively high and have low standard deviations when the bracelet is placed in exactly the same orientation for training and testing. However, by simulating rotations of the bracelet for testing (which is very likely to occur in reality), we have demonstrated that the accuracies of the proposed model deteriorate so much that they behave almost as bad as a random recognition algorithm. The inclusion, as part of the recognition model, of the algorithm for correcting the orientation of the bracelet significantly improves the classification and recognition accuracies. This improvement of the accuracy increases on average by including more synchronization EMGs. In this work, using four synchronization EMGs allowed us to obtain average accuracies that are almost as good as when the bracelet is placed on exactly the same orientation for training and testing. However, the main drawback of the proposed model for gesture recognition is its high standard deviation when we include the algorithm for correcting the orientation of the EMG bracelet.  
Even though the experiments and the results presented in this paper have been executed and obtained using EMGs measured with the Myo armband, the proposed model has the potential to be used with EMGs acquired using other sensors (i.e., bracelets) whose designs are similar to the Myo armband--for example, with the new EMG sensor gForce-pro.  
Future work includes testing the proposed algorithm for correcting the orientation of the EMG bracelet for general gesture recognition models. Another future work includes studying statistically the difference in the recognition accuracy for the classes sex and handedness.  
Conceptualization, L.I.B.L., A.L.V.C. and M.E.B.; investigation, L.I.B.L., A.L.V.C. and M.E.B.; project administration, L.I.B.L., A.L.V.C. and M.E.B.; resources, L.I.B.L., A.L.V.C. and M.E.B.; supervision, L.I.B.L., A.L.V.C. and M.E.B.; validation, L.I.B.L., A.L.V.C. and M.E.B.; data-curation, L.I.B.L., A.L.V.C.; software, M.E.B.; visualization, L.I.B.L., A.L.V.C. and M.E.B.; methodology, M.E.B.; formal analysis, M.E.B.; funding acquisition, M.E.B.; writing original draft, L.I.B.L., A.L.V.C. and M.E.B.; writing-review & editing, L.I.B.L., A.L.V.C. and M.E.B. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge the financial support provided by the Escuela Politecnica Nacional (EPN) for the development of the research project \"PIGR-19-07 Reconocimiento de gestos de la mano usando sestales electromiograficas e inteligencia artificial y su aplicacion para la implementacion de interfaces humano--matiuna y humano--humano\".  
The authors declare no conflict of interest.