# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Results

Table 12: Average sensitivity, specificity and accuracy percentages of classification methods for motion with object measurements. as positive. Moreover, the correct classification of the positive PT cases proved difficult for the classifiers with the highest percentages being below 50% which was the best PT sensitivity rate achieved from the Coarse KNN. On the other hand, the algorithms were able to categorize as negative most of negative PT cases with the RUSBoosted trees classifier achieving 100% success rate. Lastly, both the NT sensitivity and specificity rates are high for most of the classifiers with Weighted KNN and RUSBoosted trees having 100% sensitivity percentage and Cubic KNN having the same perfect specificity percentage. Regarding the average accuracy for each of the 21 classifiers for the motion with object measurement, subset Coarse Gaussian SVM classifier stands out as the most successful in terms of accuracy with 82.62% while simultaneously having high sensitivity and specificity rates, except NT sensitivity which is relatively low. Finally, it is notable that 5 out of 6 SVM classifiers predicted correctly the examples with approximately 80% success. Overall, as we observe from Tables 9-12, ET is well classified for all four measurement subsets with high sensitivity and specificity percentages. On the contrary, positive PT cases were more difficult to classify. In detail, only in the postural measurement subset the positive cases of PT were correctly categorized as positive at a satisfactory level with the high success sensitivity rate of 81.25% from the Quadratic Discriminant classifier. However, the majority of the classifiers managed to efficiently categorize correctly the negative cases of PT at all four measurement positions and at percentages reaching up to 100% in each of the sets. In general, the NT cases were classified correctly at high success rates especially for free motion and motion with object measurement subsets. Regarding the various classifiers, Boosted trees recognized all the positive NT cases as positive at rest, postural and free motion measurement positions. Furthermore, the NT specificity rate was relatively high in all sets, but the motion with object one proved to be the most accurate with 100% specificity rate for Cubic KNN. To summarize, the measurement subset which provided the best accuracy percentage is the motion while holding an object with 82.62% average accuracy for the accelerometer signals from index, thumb, metacarpal and forearm. This percentage is approximately 8% better than the second best accuracy achieved at rest position, 10% better than at postural position and 15% better than at free motion position. The highest percentage belongs to the Coarse Gaussian SVM classifier while the second best accuracy percentage belongs to Cubic SVM classifier at the rest measurement subset, with Fine KNN at postural position and Quadratic SVM at free motion position following third and forth, respectively. Therefore, our investigation shows that it is possible to efficiently classify among essential, Parkinson's and no diagnosed tremor, especially at motion with object position. This observation agrees with our findings from Table 8 that during the object-holding arm position, the classification of new examples is more successful than of the other three positions, for the reason that as the hand moves, the strength of the muscles gradually weakens causing an increase in tremor amplitude thus creating a clear margin of separation between the three classes.