## 2 Related Work
The data was collected by measuring output signals from patients while they were on their medication to avoid the tendency to fall, and it was proven that medication may reduce variations between the EMG signals of PD patients and healthy subjects. However, it does not remove them entirely. Also, they tested a number of patients that were off medication. [PERSON] et al. [9] proposed the use of sEMG in the form of a Myo armband attached to eight different points on the forearm. This measurement set is complex and requires an experienced specialist. For tele-rehabilitation purposes, it is recommended that the subjects be allowed to move freely, as they are supposed to take their measurements at home or the office. As stated from previous tests, accelerometers can only measure the acceleration and induce the changes in locomotion of the human body. However, using accelerometers alone has its drawbacks. In particular, it is almost impossible to detect tremors with an accelerometer, as it can only measure high frequency components of a tremor, whereas the magnitude of sensor output is completely dependent on the angular position. Another disadvantage is the fact that while all movements are based on rotations of joints, accelerometers measure linear acceleration. So, to increase the accuracy of output data, we may use one of the new approaches such as sEMG sensors, which can measure the muscle activity in combination with accelerometers to assess and predict PD remotely. Muscle activation is measured using surface electromyography (EMG), and can be used in PD for tremor analysis. Nowadays, there is an increased interest in the approaches based on surface EMG in addition to the commonly measured kinematics of PD patients.The reason for this is that they have the advantage of the absence of complex measurement setup and typically measurements are easy to be captured. Also, they are cost-effective and non-invasive. Recently, surface EMG signals proved that they can effectively be used to differentiate between healthy subjects and patients with PD. Since PD characteristics can considerably vary over the period of the day, thus using EMG recordings is beneficial for continuous monitoring of patients along the day. Previous studies showed that long-term EMG monitoring is valid and reliable for measuring and quantifying pathological tremor, which, as was previously stated, can greatly help in detecting PD patients. More recently, the deployment of machine learning (ML) techniques in addition to wearable sensors has been evolving [19]. The use of ML algorithms enables for both accurate and automatic assessment of patient symptoms, which in turn deemed to help in both decision making and long-term monitoring of patients. Some of the aforementioned studies, among others, have employed ML algorithms in their studies. A summary of the important related work from the literature that employed ML techniques is depicted in Table 1. The best classification accuracy was reported in [9] using a Myo armband on eight points of the forearm with a classification accuracy of 92.98% using support vector machine (SVM) classifier. However, as was stated earlier, for tele-rehabilitation purposes where the patient is allowed to take measurements at home, having a complex measurement approach would not be feasible.