# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 4 Discussion and Conclusions  
This paper presents the preliminary results of the implementation of a bimodal system with EMG and FMG sensors in which two EMG + FMG pairs are placed in the flexor and extensor muscles. A total of thirty-six characteristics of these two acquired signals were used for three healthy individuals, and the dataset consisted of five different gestures. Even considering the sample bias that could be caused by this reduced number of participants, the main objective of this study is to evaluate the benefit, in terms of efficacy in the recognition of the gestures performed, that is obtained by the acquisition of the FMG signal simultaneously with the EMG signal, because this signal when used in isolation has some limitations that result, for example, from variations in the impedance of the skin interface. MATLAB's Classification Learner was used, thirty-three classifiers were applied, and a study was also made on the possibility of reducing the number of characteristics, which will be an important point to reduce the processing time and consequently the response time of the bionic hand in the execution of gestures. For this, three different methods of selection of the characteristics were used, with different percentages (75%, 50%, and 25%) of the total of thirty-six characteristics. The preliminary results presented focus on the most used metric, which is accuracy, but the results are also being analyzed with other metrics, namely, F-score and the area under the ROC curve. It is possible to verify how different classifiers have very different behaviors, with those that are more effective but more sensitive to the reduction of the number of characteristics and others that are more immune to this selection of characteristics. Although this evaluation of the bimodal system is still ongoing, the results presented here reinforce the idea, supported by previous research, that the combination of EMG and FMG allows to improve the efficiency of machine learning models in gesture recognition. In some classification models, the results obtained using only the characteristics of the EMG sensors, or even only those of the FMG sensors, are a little better than with all the characteristics of the bimodal system. However, in these cases, the accuracy results are low, showing that these models are not suitable for this application. Thus, based on the analysis of the accuracy values obtained in this previous study, six of the thirty-three classification models initially considered were selected. In this work, two studies were developed that we believe to be innovative. In one of them, the impact of each of the characteristics on success in the recognition of gestures was analyzed. From the five feature selection methods and with different numbers of features (36, 30, 24, or 18), it was possible to find a ranking showing the characteristics with the greatest impact for each of the five selection methods. From their analysis, it can be seen that these characteristics, being different between methods, allow us to draw the conclusion that those extracted from the extensor muscle have a greater presence in these rankings than those from the flexor muscle. It is not so evident that there is a predominance of the characteristics of one type of sensor, and it should also be remembered that a total of twenty-four EMG characteristics were used against only twelve FMG. Thus, although in three of the five selection models, the FMG characteristics appear only in the lowest places of the top 10, in the other two models, they appear in prominent positions in these rankings. On the other hand, strategies for the optimization of the classification models were also studied through the configuration of some parameters of these models, for example,the number of layers in a neural network. These tuning of the parameters were tested simultaneously with the different methods of feature selection in order to combine computational efficiency with effectiveness in gesture recognition.