# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 3 Results
### Feature Selection  
These twelve models were trained with different feature selection methods--mRMR, Chiz, ReliefF, ANOVA e Kruskal-Wallis--in order to assess the impact of each feature and determine which were most relevant to our goal of gesture recognition. Different percentiles of features were used with each model--30, 24, or 18 of the initial 36--in order to identify the ideal balance between model complexity and gesture prediction efficacy.  
The performances of the Cubic SVM and Quadratic SVM models stand out, which presented remarkable robustness, maintaining a high level of effectiveness, both in validation and in the test set, regardless of the number of characteristics or the selection method used. Specifically, the Cubic SVM model achieved 88.08% in the validation with 18 features selected by the ReliefF method and 90.28% in the test set, with the selection of 18 features using the Chi2 method. The Fine KNN and Weighted KNN models also proved to be very resilient, maintaining a remarkable effectiveness in both validation and testing, although lower than that of the SVM models.  
The results indicate a variable robustness of the models in the face of the selection of features. While most models showed resilience, maintaining efficacy even with the reduction in the number of features, some models, in particular Quadratic Discriminant, demonstrated greater sensitivity to this reduction. The effectiveness of this model fell drastically by limiting the number of features selected by the ANOVA and Kruskal-Wallis methods to 18. In contrast, the Cubic SVM, Fine KNN, and Weighted KNN models stood out for their robustness, maintaining or even improving their effectiveness even with the reduction in the number of features.  
The ranking of the characteristics for the five methods of selection of characteristics shows that there is a predominance of characteristics taken from the signals measured in the extensor muscle, with two methods in which in the top-10 there is no characteristic related to the flexor muscle. On the other hand, it is also observed that in the top 5, there are no characteristics taken from FMG signals in three of the five methods. It should be noted that, on the contrary, the ReliefF method has only characteristics of the FMG with the exception of #4. Table 4 presents these top 10 characteristics.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & \begin{tabular}{l} **Accuracy \%** \\ **(Validation)** \\ \end{tabular} & **Accuracy \% (Test)** & \begin{tabular}{l} **F-Score** \\ **(Validation)** \\ \end{tabular} & **F-Score (Test)** & \
\begin{tabular}{l} **ROC-AUC** \\ **(Validation)** \\ \end{tabular} &
\begin{tabular}{l} **ROC-AUC** \\ **(Test)** \\ \end{tabular} \\ \hline Quadratic Discriminant & 83.68 & 83.80 & 0.8377 & 0.8324 & 0.9370 & 0.9405 \\ Quadratic SVM & 89.35 & 87.96 & 0.8827 & 0.8758 & 0.9823 & 0.9733 \\ Cubic SVM & 89.24 & 87.96 & 0.8928 & 0.8788 & 0.9811 & 0.9669 \\ Medium Gaussian SVM & 85.65 & 87.96 & 0.8577 & 0.8772 & 0.9750 & 0.9676 \\ Fine KNN & 86.69 & 86.57 & 0.8687 & 0.8625 & 0.9180 & 0.9155 \\ Medium KNN & 83.80 & 85.65 & 0.8382 & 0.8514 & 0.9703 & 0.9620 \\ Cosine KNN & 80.56 & 84.26 & 0.8058 & 0.8371 & 0.9633 & 0.9704 \\ Cubic KNN & 81.02 & 83.80 & 0.8119 & 0.8338 & 0.9641 & 0.9533 \\ Weighted KNN & **86.92** & **88.43** & 0.8684 & 0.8810 & 0.9785 & 0.9709 \\ Bagged Trees & 85.88 & 83.33 & 0.8586 & 0.8276 & 0.9700 & 0.9643 \\ Medium Neural Network & 85.07 & 81.02 & 0.8514 & 0.8039 & 0.9527 & 0.9182 \\ Wide Neural Network & **85.43** & **82.41** & 0.8831 & 0.8179 & 0.9684 & 0.9410 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the metrics used for the selection of the better classification models (green, darker indicates better results).