# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 1 Introduction  
A tremor [1] is defined as the involuntary rhythmic oscillation of a part of the human body, which is produced by the competing acting contractions of the muscles in the joints. The need to find the tremor's origin and its diagnosis is imperative to achieve immediate reduction and treatment of the related syndrome. Tremor is distinguished in the following basic categories: (a) resting tremor, where the affected body part is placed on a surface so that it remains unaffected by the force of gravity, (b) postural tremor, which is the most commonly occurring type, where the subject attempts to keep his trembling body part steady during a pose affected by gravity and (c) motion tremor, which occurs during movement and increases with its duration [2]. There are several tremor types, each with different causes and characteristics: Parkinsonian (Parkinson's disease and parkinsonism), essential, cerebellum, post-traumatic, pharmaceutical, dystonia, multiple sclerosis, neuropathy, [PERSON], [PERSON] disease, psychogenic tremor, etc. [3]. The clinical picture and the proper reception of medical history, aswell as the experience of the individual doctor contribute to a more accurate determination of tremor type. From the different types of tremor, essential and Parkinson's are described by the highest occurrence, reaching up to 5% and 1% of a country's population [4], respectively, and with increasing frequency. Despite the existence of various diagnostic methods for Parkinson's and tremor, their rapid and effective discrimination, especially in their early stages, proves particularly difficult, mainly due to their wide range of causes and similarity of symptoms [5]. The challenge of finding an accurate and effective method for discriminating among tremor types, while being able to predict their stage and severity, has led to the use of sensor-enabled systems for extracting various elements of tremor, which cannot be extracted solely from the clinical picture of the patient. Recently, there has been an increasing interest of applying machine and deep learning modeling methodologies on the various acquired bio-signals for identifying and predicting the severity of [PERSON]'s disease [6; 7; 8]. Specifically, machine learning has been used for symptom quantification of Parkinson's as well as essential tremor patients via gyroscope and accelerometer sensors [9; 10; 11; 12; 13; 14; 15]. Furthermore, recognition of Parkinson's was recently tested on phonation and speech datasets for identifying dysphonia signs related to the syndrome [16; 17; 18; 19; 20]. Predictive diagnosis of Parkinson's has also been attempted via machine learning on demographic, movement and speech data [21], as well as using typing and mouse behavior for detecting and monitoring the severity of [PERSON]'s disease with supervised machine learning classifiers [22; 23]. Other bio-data including dopamine transporter data from tomographic images [24] and serum cytokines [25] have also been used for classification performance evaluation via extraction of shape features based on generated areas of interest from images and other indicators, while discrimination of patient motor status via machine learning has also been recently tested on a digital biomarker data set using Neural Network Construction methodology [26]. Deep learning modeling algorithms have also been recently utilized for tracing and identifying [PERSON]'s via fuzzy recurrence plots [27] and monitoring and predicting of [PERSON]'s disease via wearable sensors [28; 29]. Work has also been done on [PERSON]'s diagnosis via deep learning through medical imaging [30], handwritten dynamics [31] and voice data sets [32]. Moreover, in [33], an attempt to classify between [PERSON]'s and essential tremor has been performed. The aforementioned research shows the high potential in utilizing machine and deep learning for discriminating tremors.