## 1 Introduction  
Parkinson's disease (PD) is a chronic neurological disease which leads to a great loss of motor function. There are multiple symptoms that indicate the severity of PD, including resting tremor,akinesia, decreased motor activity (hypokinesia), bradykinesia and dyskinesia. Among the most common chronic neuro-degenerative diseases, PD is ranked in the second place. Records show that PD affects more than four million people all over the world, affecting about 1-2% of the elderly people exceeding the age of 60 years. On the other hand, freezing of gait (FOG) is a phenomenon known as brief moments in which patients suffer from being unable to start appropriate forward movements. It is known as one of the most important things that might affect life in PD patients [1; 2]. In addition, it is considered as one of the most important reasons of falls [3]. Hence, it is important to develop wearable systems to predict and reduce FOG incidences [4]. The idea of getting quantitative data of human locomotion using wearable technology has received an increased interest from researchers in recent years. Typically, quantitative assessment using wearable sensors allows for continuous, economical, straightforward data collection. Also, it can improve doctor-patient interaction, which helps in making decisions, and eventually affects the overall global health status. Normally, patients' scheduled clinical visits do not provide all snapshots of the conditions that a patient has in that particular situation as how he/she performs at home. However, some neurological diseases that affect human gait, such as Parkinson's disease, are characterized by its fluctuations, which may or may not consistently occur within and across days. This necessitates the need for the use of new technologies that can quantitatively measure, assess, and remotely transmit data to the doctor. Practical quantitative recordings from wearable sensors may also help capture accurate and meaningful data that is more informative than clinical surveys. Also, monitoring patients' locomotion with neurological diseases such as Parkinson's disease in clinics does not always provide the doctors with a valid and accurate data due to the fact that patients always know that they are being monitored. So, the normal symptoms may not occur as they happen in normal days [5; 6]. The most important task of using different wearable sensors is to combine the data from these sensors, and generate useful knowledge and reasonable information that can help in predicting and decreasing the severity of locomotion abnormality remotely. The most commonly used types of wearable sensors that have been proposed in the literature for locomotion monitoring include accelerometers, gyroscopes, and magnetometers. Still, these sensors have their limitations [7]. In addition to these sensors, few studies recommended to combine surface electromyography (EMG) with some of the above mentioned sensors. However, the studies in that field are still evolving with few preliminary trials reported in the literature. [PERSON] et al. [8] proposed using surface EMG with accelerometers, as surface EMG (sEMG) in PD patients is characterized by alternating patterns. In this study, the EMG was collected from the biceps brachii (BB) muscle, and accelerometers were placed on the Palmer wrists. sEMG was also used by [PERSON] et al. [9] for tremor detection, where Myo armbands were attached to the subjects' forearms. In addition to the use of wearable sensors, the application of machine learning (ML) techniques on the measured data has received increased attention by researchers in recent years. More specifically, the application of ML techniques enables an automatic and accurate assessment of the measured data, which in turn facilitates the employment of tele-rehabilitation. Moreover, ML, when combined with signal processing techniques, helps to overcome some of the limitations and inaccuracies encountered during the measurement process using wearable sensors [10].