# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 3 Results
### Model Optimization  
In this phase, we moved on to the final stage of the modeling process: the adjustment of the hyperparameters of the models that were identified as the most promising in the previous sections. Hyperparameters are settings external to the model, which can be adjusted to control how the model will \"learn\". Unlike model parameters, hyperparameters are not learned from the data during training but are decided from the start. Examples of hyperparameters include the number of \"neurons\" or layers in a neural network, the type of kernel in an SVM model (linear, polynomial, etc. ), or the number of neighbors in a KNN model. The five most promising models were then used: Quadratic SVM, Cubic SVM, Fine KNN, Weighted KNN, and Wide Neural Network. This last model performed slightly lower than the others, but its inclusion in this is due to the fact that neural networks have the ability to capture complex and nonlinear interactions between features that other models may not be able to identify. Each model will be optimized with the goal of finding the best combination of hyperparameters and feature selection strategies for each model. The models were evaluated with 100% of the features and subsets obtained by the selection methods, using different percentiles. The combinations with the best performance will be the ones that will now be explored. Preliminary results in Table 5 show that for some classification models, it is possible to achieve an improvement in accuracy with a subset of features, even using only 50% in one case. In the choice of these combinations, the test results were privileged, however, the validation results were also considered. This procedure was fundamental to ensure that \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**\#** & **Features** & **Chi2** & **\#** & **Features** & **mRMR** & **\#** & **Features** & **ReliefF** \\ \hline
**1** & mmf\_Extensor & 288 & **1** & mmf\_Extensor & 0.354 & **1** & median\_fmg\_Extensor & 0.094 \\
**2** & mmpsd\_Extensor & 220 & **2** & median\_fmg\_Extensor & 0.322 & **2** & mmf\_Extensor & 0.093 \\
**3** & rms\_Extensor & 213 & **3** & ssc\_fmg\_Extensor & 0.281 & **3** & std\_fmg\_Extensor & 0.068 \\
**4** & sd\_Extensor & 213 & **4** & wl\_Flexor & 0.057 & **4** & rms\_fmg\_Extensor & 0.068 \\
**5** & mav\_Extensor & 196 & **5** & kurt\_Extensor & 0.042 & **5** & mean\_fmg\_Extensor & 0.067 \\
**6** & iqr\_Extensor & 173 & **6** & std\_fmg\_Flexor & 0.034 & **6** & sd\_Extensor & 0.067 \\
**7** & log\_Extensor & 153 & **7** & mmf\_Flexor & 0.026 & **7** & rms\_Extensor & 0.067 \\
**8** & median\_fmg\_Extensor & 150 & **8** & rms\_Extensor & 0.021 & **8** & mmf\_Flexor & 0.062 \\
**9** & rms\_fmg\_Extensor & 122 & **9** & wl\_fmg\_Extensor & 0.021 & **9** & mav\_Extensor & 0.058 \\
**10** & mean\_fmg\_Extensor & 108 & **10** & std\_fmg\_Extensor & 0.019 & **10** & median\_fmg\_Flexor & 0.055 \\ \hline
**\#** & **Features** & **ANOVA** & **\#** & **Features** & **Kruskal** & & \\ \hline
**1** & sd\_Extensor & 230 & **1** & mmpsd\_Extensor & 139 & & & \\
**2** & rms\_Extensor & 230 & **2** & rms\_Extensor & 137 & & & \\
**3** & mmpsd\_Extensor & 224 & **3** & sd\_Extensor & 137 & & & \\
**4** & mav\_Extensor & 199 & **4** & mav\_Extensor & 128 & & & \\
**5** & iqr\_Extensor & 150 & **5** & log\_Extensor & 109 & & & \\
**6** & log\_Extensor & 145 & **6** & iqr\_Extensor & 107 & & & \\
**7** & mmf\_Extensor & 137 & **7** & mmf\_Extensor & 93 & & & \\
**8** & rms\_fmg\_Extensor & 95 & **8** & kurt\_Extensor & 86 & & & \\
**9** & pk\_Extensor & 94 & **9** & mmf\_Flexor & 85 & & & \\
**10** & median\_fmg\_Extensor & 94 & **10** & rms\_fmg\_Extensor & 82 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top 10 of the ranking of characteristics for the five feature selection methods used.