# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 1 Introduction
However, the majority of the studies are mainly focused on [PERSON]'s identification, with only a few targeting at the differential diagnosis between different tremor types [29; 33; 34; 35]. Moreover, most of these efforts provide results based on a binary classification problem, while the majority utilize accelerometer sensors through wearable means from a single point of measurement, usually placed on the metacarpal region or the index finder. Such analyses are performed at postural arm poses, while the use of smartphones and their integrated accelerometers can limit the hand motion ranges. Therefore, this state-of-the-art overview shows an increasing need in further investigating this problem by expanding the classification target via inclusion of more measurement points on the user's arm, while assessed at different poses and via multiple different classifiers. The contribution of the presented work stems from the multi-class classification problem among essential, [PERSON]'s tremor- and non-tremor-diagnosed volunteers. For the presented research, a clinical trial was performed, where essential and [PERSON]'s tremor-diagnosed patients, along with healthy volunteers, underwent a series of pre-defined sequences involving multiple arm poses (resting, postural and motion patterns). During these sequences, an accelerometer-based wearable setup was used to measure their lower arm tremor characteristics from multiple selected points on the hand and forearm. Initially, the information extracted from the patients' medical history and questionnaire answers were used for assessing the efficiency of hierarchical grouping into tremor discrimination. Then, the extracted features from all acquired accelerometer signals were used as a basis for a comparative study between multiple different classification algorithms, includingdecision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms. The goal was to provide a detailed evaluation on utilizing machine learning algorithms for accurately discriminating between the two different tremor types, while providing information on the arm measurement points that provide better classification results. It has to be noted that this work is based on the initial findings presented in [36], providing the hierarchical grouping evaluation, as well as extended performance and accuracy results on the essential and Parkinson's tremor discrimination via the multiple utilized classification algorithms. The rest of the article is structured as follows. Section 2 provides an overview of essential and Parkinson's tremor, along with the existing diagnostic methods. Section 3 presents the measurement setup specifics utilized in the clinical trial. In Section 4, the methodology concerning the data processing algorithms for patient categorization, feature extraction, training and prediction are provided in detail. Section 5 provides the results of the clinical trial, along with commentary on the acquired data involving the volunteer categorization, hierarchical grouping and tremor classification. Finally, concluding remarks along with comments on future work are provided in Section 6.