# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 6 Conclusions  
In this work, an attempt was made in differentiating Parkinson's and essential tremor via the use of machine learning modeling algorithms. To this goal, a clinical trial was conducted involving tremor-diagnosed patients and healthy volunteers, where a data acquisition setup was implemented and utilized for acquiring acceleration measurements from multiple arm points and for multiple motion scenarios. Initially, after collecting data from the medical history of tremor-diagnosed patients, grouping of patients was attempted via the use of dendrograms, for investigating the possibility of distinguishing between these kinds of tremor via diagrammatic representation. The overlapping in symptoms and characteristics of these two types of tremor further highlighted the challenge and risk of their misdiagnosis, which fueled the utilization of more advanced machine learning techniques for improving the identification possibilities.  
Via the acceleration measurements acquired from all volunteers under different arm poses and measurement points on the hand and forearm, the data was processed and augmented to account for the inequality between volunteers with essential and [PERSON]'s tremor, which was then followed by the extraction of statistical signal characteristics. Machine learning models were trained via the use of the augmented data, while multiple classification algorithms were tested for identifying the ones with the highest success rates and for making predictions.  
From the tremor identification results, it was noted that the prediction algorithms with the lowest prediction percentages appeared in the measurements with the arm in a resting position or during free motion with the lowest achieved success rate was the of \(T_{3}\) with 40.3%. On the contrary, the highest forecasting percentages were acquired from SVM-based algorithms, in both extended and moving while holding an object arm poses. Specifically, Cubic and Quadratic SVM managed to predict correctly with high speed, while requiring low training time, all the examples at \(M_{2}\). In parallel, Cubic SVM achieved 96.5% success rate at \(F_{4}\) while during the same measurement Linear SVM effectively combined accuracy with training time and prediction speed. Thus, the increased tremor in these two positions made the discrimination more distinct from each type of tremor parameter, thus resulting in more accurate predictions. In contrast, the free-motion arm pose, which includes the parallel action of different muscle groups, resulted in less muscle fatigue and less distinct characteristics for better discriminating among tremor types. In overall, Cubic and Quadratic SVM algorithms managed to provide good prediction rates under most measurement points and arm poses, reaching up to 100% prediction success rates for both (i) metacarpal measurements with the arm extended and (ii) forearm measurements with the arm moving while holding an object.  
Finally, from the results presenting the average sensitivity, specificity and accuracy of each measurement subset for the 21 selected classifiers, the SVM algorithms provided the best results, where for the motion with object position the accuracy percentage of Coarse Guassian SVM reached up to that of 82.62%. This high percentage along with superiority of the SVM classifiers denotes that there can be clear margin of separation between the three classes PT, ET and NT.