# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees
## 3 Results
### Classic Features  
Table 1 summarizes the results obtained for the algorithms tested with classic features and feature combinations and Figure 2 shows each user's result around the mean for the most promising algorithm-feature combination.  
* **Linear models:** One can observe in Table 1 the number of blank cells presented in the block of linear models of the table. It is remarkable that the Stochastic Gradient Descent and Passive Aggressive do not achieve more than 50% accuracy for any of the feature combinations as applied to this dataset. The Logistic Regression algorithm achieves greater than 50% accuracy only after majority voting has been applied. LinearDiscriminant Analysis appears to provide the best results out of all classifiers: 78% accuracy for single window analysis; 88% with majority voting.
* **Non-linear models:** Neither Decision Trees, polynomic or RBF versions of Support Vector Machines, nor the Naive Bayes classifier achieve 80% accuracy for the 18 classes. Only the linear version of support vector machine reaches 82% accuracy after majority voting with the PPaL feature combination. Accuracy is lower than with LDA in all conditions.
* **Ensemble models:** The Ada Boost classifier does not provide results better than 50% in any of its configuration. Although the other three proposed classifiers (Bagged Decision Trees, Random Forest, Extra Trees) provide better results, none surpassed 73% even with majority voting.  
Individual User Performance  
In Figure 2 one can observe that User 5 manifests the poorest results in all the algorithm-feature combinations (its general accuracy is lower than 60% even if the rest of the users cluster around 80%). Additionally, one can observe that user 9 outperforms the rest of the users in terms of accuracy almost achieving perfection with the LDA classifier both for PL and PPaL feature combination with majority voting.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**P**} & \multicolumn{2}{c}{**PL**} & \multicolumn{2}{c}{**PPa**} & \multicolumn{2}{c}{**PPaL**} \\ \cline{2-9}  & **S** & **MV** & **S** & **MV** & **S** & **MV** & **S** & **MV** \\ \hline \multicolumn{9}{c}{Linear Models} \\ \hline Logistic Regression & \multicolumn{2}{c}{0.58} & \multicolumn{2}{c}{0.59} & \multicolumn{2}{c}{0.61} \\ \hline Linear Discriminant Analysis & 0.62 & 0.73 & 0.74 & 0.86 & 0.65 & 0.73 & 0.78 & 0.88 \\ \hline Stochastic Gradient Descent & \multicolumn{2}{c}{} & & & & & & \\ \hline Passive Aggressive & \multicolumn{2}{c}{Non-Linear Models} \\ \hline Decision Tree & \multicolumn{2}{c}{0.63} & \multicolumn{2}{c}{0.68} & \multicolumn{2}{c}{0.63} & \multicolumn{2}{c}{0.68} \\ \hline Support Vector Machine(‘linear’) & 0.58 & 0.67 & 0.65 & 0.79 & 0.60 & 0.68 & 0.70 & 0.82 \\ \hline Support Vector Machine(‘poly’) & \multicolumn{2}{c}{0.62} & \multicolumn{2}{c}{0.50} & \multicolumn{2}{c}{0.65} \\ \hline Support Vector Machine(‘rbf’) & \multicolumn{2}{c}{0.62} & \multicolumn{2}{c}{0.61} \\ \hline Gaussian Naive Bayes & \multicolumn{2}{c}{0.57} & \multicolumn{2}{c}{0.53} & \multicolumn{2}{c}{0.62} & \multicolumn{2}{c}{0.52} & \multicolumn{2}{c}{0.57} & \multicolumn{2}{c}{0.56} \\ \hline \multicolumn{9}{c}{Ensemble Models} \\ \hline Ada Boost & \multicolumn{2}{c}{} & & & & & \\ \hline Bagged Decision Tree & 0.58 & 0.67 & 0.59 & 0.73 & 0.59 & 0.66 & 0.62 & 0.72 \\ \hline Random Forest & 0.59 & 0.68 & 0.61 & 0.73 & 0.61 & 0.67 & 0.63 & 0.72 \\ \hline Extra Trees & 0.60 & 0.67 & 0.61 & 0.73 & 0.61 & 0.67 & 0.64 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean of 9 users, Algorithms vs Accuracy of CLASSIC features. 18 gestures without (S) and with majority voting (MV) to stabilize the results.