# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees
## 3 Results
### Statistical Comparison  
To quantitatively compare methodologies we performed a five-factor ANOVA with repeated measures, with accuracy as the sole dependent variable and with Feature Vector (Classic or TD-PSD), Look-Back Filter Method (P, PPa), Transform (Linear or Log), Majority  
\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{2-10}  & **S** & **MV** & **S** & **MV** & **S** & **MV** & **S** & **MV** \\ \hline \multicolumn{10}{c}{Linear Models} \\ \hline Logistic Regression & 0.54 & 0.64 & 0.55 & 0.79 & 0.57 & 0.65 & 0.62 & 0.82 \\ \hline Linear Discriminant Analysis & 0.64 & 0.71 & 0.72 & 0.87 & 0.66 & 0.72 & 0.77 & 0.89 \\ \hline Stochastic Gradient Descent & & & & & & 0.52 & & \\ \hline Passive Aggressive & & & & & & & & \\ \hline \multicolumn{10}{c}{Non-Linear Models} \\ \hline Decision Tree & 0.50 & 0.67 & 0.54 & 0.79 & 0.50 & 0.66 & 0.56 & 0.79 \\ \hline Support Vector Machine(‘linear’) & 0.60 & 0.70 & 0.54 & 0.74 & 0.63 & 0.71 & 0.65 & 0.82 \\ \hline Support Vector Machine(‘poly’) & 0.58 & 0.67 & 0.49 & 0.65 & 0.60 & 0.66 & 0.53 & 0.64 \\ \hline Support Vector Machine(‘rbf’) & & & & & & & & \\ \hline Gaussian Naive Bayes & 0.53 & 0.62 & 0.61 & 0.79 & 0.54 & 0.61 & 0.66 & 0.79 \\ \hline \multicolumn{10}{c}{Ensemble Models} \\ \hline Ada Boost & & & & & & & & & \\ \hline Bagged Decision Tree & 0.60 & 0.72 & 0.66 & 0.83 & 0.63 & 0.72 & 0.73 & 0.85 \\ \hline Random Forest & 0.62 & 0.71 & 0.69 & 0.84 & 0.64 & 0.71 & 0.73 & 0.84 \\ \hline Extra Trees & 0.62 & 0.71 & 0.69 & 0.85 & 0.64 & 0.71 & 0.74 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean of 9 users, Algorithms vs Accuracy of TD-PSD features. 18 gestures without (S) and with majority voting (MV) to stabilize the results.  
Figure 3: Accuracy value distributions across users for the most promising combinations of Table 2.  
Voting (Simple or with MV) and Classifier (LDA, SVM, BAG and RF) as independent factors. We choose to concentrate on these 4 classifiers as they appear to give more promising results than the other options.  
Figure 4 shows the relative performance between methods. ANOVA shows a marginally significant main effect of the choice of feature vector (\(p<0.019\)) and significant main effects for the other four independent factors (\(p<0.001\)), suggesting that (a) the TD-PSD performs better than the Classic feature vector (NB see post-hoc analysis below), (b) LDA gives the best performance amongst the different classifiers, (c) logarithmic transformation of feature components has a significant positive effect on accuracy and (d) both look-back filtering and majority voting generally increase performance.  
Applying [PERSON]'s HSD test to identify homogeneous groups, LDA coupled with the log transformed combination of the present and past samples (PPaL) and majority voting stands out as the best combination, with no significant difference between the choice of feature vector (Classic or TD-PSD). Figure 5 showing the results for only the log-transformed feature elements illustrates this observation. Nevertheless, one can observe in Figure 6 that the accuracy rates appear to vary less across subjects (smaller range of values) for the Classic vs. TD-PSD features.  
Figure 4: Interaction of Feature Vector, Look-Back Filter Method and Classifier on prediction accuracy.  
Figure 5: Specific ANOVA analysis of LDA.