## 1 Introduction  
Parkinson's disease (PD) patients experience difficulties in managing the symptoms of their illness. Each PD patient experiences symptoms differently and with varying severity. Doctors and medical practitioners customize the treatment of PD for every patient depending on their experienced symptoms. In order to do that, patients have to monitor their symptoms actively and report to their doctor. Then, doctors provide different doses of the anti-PD drug depending on the reported symptoms. The \"wearing-off\" phenomenon [1, 2, 3, 4] occurs when the anti-PD medicine loses its effect. Patients start to experience the symptoms again until their next intake. In the long term, the duration of wearing-off begins to shorten. Thus, patients and doctors have to work together to manage PD symptoms effectively. Patients have to track their symptoms and the wearing-off period. Meanwhile, doctors need to have this information to give a customized approach to the patients' treatment. Previous works have used different wearable data to detect PD symptoms. They mainly worked with accelerometer, electromyography (EMG), and gyroscope data to detect tremors, freezing of gait (FoG), and \"wearing-off\" periods [5, 6, 7]. Some of these previous works collected data in a closed and controlled environment. In contrast, other studies showed the feasibility of medium to large-scale data collection using different wearable sensors [8, 9]. The Parkinson's KinetiGraph (PKG) has been used in some of these studies [9, 10]. Statistical analysis [11] and machine learning-based analysis have been conducted on gait features; in one of these studies, machine learning algorithms were applied to wearable accelerometer-based sensor data to detect \"on\" and \"off\" states for PDpatients [12]. In another study, machine learning classifiers were applied to PKG data to estimate the levodopa response from the Unified Parkinson's Disease Rating Scale Part III (UPDRS III) [10]. Smartwatches have also been employed to monitor motor fluctuations based on accelerometer data in a real-world environment [8,13]. Studies have usually employed either PKG or other medical-grade wearable sensors. The medium to large-scale data collection approach has yet to apply automated analysis using the machine learning approach to analyze and predict the wearing-off phenomenon. Data collection in a real-life environment has shown promise for further analysis and thus could help PD patients. There is still untapped potential in the use of commercially available fitness trackers and smartwatches for PD management and for predicting the wearing-off phenomenon. Fitness trackers and smartwatches can now report a user's heart rate, sleep quality, stress, and even blood pressure, among many other data types. Although the reliability of fitness trackers and smartwatches is contestable [14], these data can be used to verify earlier studies. For example, the blood pressure and heart rate of PD patients have been investigated for the wearing-off phenomenon, and vlood pressure change was statistically significant among PD patients experiencing wearing-off [15]. PD patients have also reported effects on their sleep patterns. The associated risk factors of rapid eye movement (REM) sleep behavior disorder (RBD) in PD patients were examined [16,17]. Aside from RBD as a form of sleep disturbance, light, fragmented sleep due to increased muscle activity, disruption of biological rhythms during sleep, breathing difficulties, insomnia, and excessive daytime sleepiness were sleep disturbances that manifested in PD patients [18,19]. A study with 3075 PD patients showed an increase of PD symptoms during sleep disturbances for 32% of the patients, while depressive moods were found for 20% of the patients. This study reported that each PD patient had varying degrees of symptoms and a differing psychological stress structure [20]. Moreover, in a recent study, PD patients were shown with clinical evidence to be highly sensitive to the effects of stress.