## 1 Introduction  
As a second most common neurodegenerative disorder, Parkinson's disease (PD) is expected to impose an increasing social and economic burden on societies as populations age [1]. Its prevalence rate is estimated to approximately 1.5% for people aged over 65 years [2]. The risk of being affected by PD strongly increases with age, and, in the next 15 years, the incidence of PD is expected to be doubled [3; 4]. The rapid degeneration of dopaminergic cells in the substantia nigra pars compacta [5] arose as the most significant biological finding associated with the disease, but the exact pathophysiological cause of PD has not yet been discovered. PD cardinal motor symptoms involve bradykinesia (slowness of movement), tremor at rest, rigidity, gait impairment, and postural instability [6; 7; 8]. A variety of non-motor symptoms may emerge as well--for instance, cognitive impairment, dementia, depression, sleep disorders, or anxiety [6; 9; 10]. Handwriting requires cognitive, perceptual, and fine motor abilities. In conjunction with motor dysfunctions in people suffering from PD, it has been proven that disrupted handwriting may be used as a significant biomarker for PD diagnosis [11; 12]. Micrographia, which is associated with the progressive decrease in letters' amplitude, is the most commonly observed handwriting abnormality in patients with PD [13; 14]. Moreover, according to [PERSON] et al. [14], in approximately 5% of PD patients, micrographia may be observed even before the onset of the cardinal motor symptoms. The recent advantage of new technologies coming hand-in-hand with Health 4.0 systems enables the acquisition of online handwriting signals, where temporal information is added to the \(x\) and \(y\) position. Therefore, by using a digitizing tablet, the analysis is not limited to spatial features which mainly quantify PD micrographia. In addition, we are able to quantify temporal, kinematic, and dynamic manifestations of PD dysgraphia, such as hesitations, pauses, and slow movement [7], which cannot be studied objectively using a classical paper-and-pen method. Due to this complexity, [PERSON] et al. [15] started to refer to these manifestations using the generalized term PD dysgraphia. Several research teams have explored the impact of quantitative PD dysgraphia analysis utilizing simple handwriting/drawing tasks (e.g., separate characters, a combination of two or three characters, repetitive loops, circles), as well as more complex ones (e.g., words, sentences, figures, 3D objects, and the Archimedean spiral) [8; 16; 17; 18; 19; 20]. An overview of recent related works (2015-present) can be seen in Table 1. Most of them confirm the irreplaceability of kinematic features in PD dysgraphia analysis. Additionally, the researchers usually employ temporal, spatial, and dynamic features. Some more advanced parameters are reported too. For instance, [PERSON] et al. [8; 16; 17] demonstrated a combination of kinematic, pressure, energy, or empirical mode decomposition (EMD)-based features that resulted in a classification accuracy of up to 89% using several handwriting tasks. [PERSON] et al. [21] achieved an average prediction accuracy of 91% using simple horizontal lines and features describing the variability in the pen tip's velocity, a deviation from the horizontal plane, and the trajectory's entropy. Other works report even higher classification accuracies (approximately 97%), e.g., [PERSON] et al. [18], who used computer vision and electromyography signal processing techniques, or [PERSON] et al. [22], who used a combination of features related to the correlation between kinematic and pressure characteristics (but, in this case, applied to a very small dataset). Another promising approach was published by [PERSON] et al. [23], who reached an 83% classification accuracy by employing convolutional neural networks (CNN) that were used to extract discriminating visual features from handwriting data transformed into the offline mode. In 2018, [PERSON] et al.