# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Results  
Table 8 lists the name of each measurement subset, the three best prediction algorithms for each of the 16 measurements, their success rates, both aggregated and separated for each type of tremor, as well as the training times and the prediction speeds of these classifiers. Variable success rates when trying to make predictions with new examples (test data) are observed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Original (ET, PT, NT)** & **Augmented (ET, PT, NT)** & **Total (ET, PT, NT)** \\ \hline Training & 25 (2, 8, 15) & 115 (30, 40, 45) & 140 (32, 48, 60) \\ Test & 18 (1, 4, 13) & 40 (15, 12, 13) & 58 (16, 16, 26) \\ Total & 43 (3, 12, 28) & 155 (45, 52, 58) & 198 (48, 64, 86) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of data used for classification. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measurement** & \begin{tabular}{c} **ET (\%)** \\ **(\(k=1\))** \\ \end{tabular} & \begin{tabular}{c} **PT (\%)** \\ **(\(k=2\))** \\ \end{tabular} & \begin{tabular}{c} **NT (\%) (\% = 3)** \\ \end{tabular} & \begin{tabular}{c} **Total (\%)** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Algorithm** \\ **(sec)** \\ \end{tabular} &
\begin{tabular}{c} **Training Time** \\ **Speed (obj,/sec)** \\ \end{tabular} \\ \hline \multirow{3}{*}{\(l_{1}\)} & 93.7 & 68.7 & 50.0 & 67.2 & RUS Boosted trees & 0.45 & 4800 \\  & 87.5 & 37.5 & 73.1 & 67.2 & Medium tree & 4.71 & 3400 \\  & 87.5 & 43.7 & 43.6 & 51.72 & Bagged trees & 7.29 & 690 \\ \hline \multirow{3}{*}{\(l_{2}\)} & 100.0 & 75.0 & 92.3 & 89.6 & Bagged trees & 2.89 & 1200 \\  & 100.0 & 50 & 92.3 & 78.8 & Quadratic SVM & 2.65 & 2800 \\  & 100.0 & 41.7 & 92.1 & 78.8 & Fine KNN & 3.07 & 3300 \\ \hline \multirow{3}{*}{\(l_{3}\)} & 81.2 & 56.2 & 61.5 & 63.8 & Quadratic SVM & 1.81 & 3600 \\  & 62.5 & 45.8 & 73.1 & 61.4 & Linear SVM & 1.91 & 3800 \\ \cline{1-1}  & 62.5 & 37.5 & 73.1 & 57.6 & Bagged trees & 2.12 & 1300 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Maximum achieved prediction rates. From the presented results, there are algorithms that predicted correctly all 16 ET cases. Specifically, for the thumb and forearm measurements the majority of the chosen classifiers were able to accurately discriminate subjects with ET among the rest of the subjects, while for the index and metacarpal positions the prediction percentages of ET are high and reach up to 100%. On the contrary, it appears challenging to distinguish between PT and NT volunteers. This is reasonable since there were Parkinson's-diagnosed volunteers who showed no tremor during the measurements, which was verified in the subsequent processing of the acquired data. This was expected, as tremor may be one of the basic characteristics of the disease, but it is not a necessary and unique trait of the disease and it may not have a lasting nature. Therefore, in a resting position, where the hand is not affected from the gravitational pull, it is possible to produce false results in the distinction PT from NT cases. This phenomenon was not observed in ET subjects, most of which were properly classified, due to the fact that all such volunteers experienced tremor at a resting position. Thus, after the appropriate processing of the signals and the extraction of the relevant parameters for the 3 ET subjects and for the signals created on the basis of the data augmentation technique, they were correctly classified as ET observations, which applied to all measurement positions. However, PT was correctly classified at 100% for the SVM algorithms in both extended arm position measurements (\(M_{2}\), \(T_{2}\)) and in motion while holding an object (\(F_{4}\)).