# Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
## 3 Research Directions
### Parkinson's Disease
#### 3.1.3 Disease Diagnosis
Hence, the main contribution of the work was the application of a deep learning-oriented approach to aid PD diagnosis as well as the design of a signal-based dataset. The abovementioned work was extended by the authors in References [52; 90]. In Reference [90], CNNs were used to learn features directly from time-series-based images. The main hypothesis was that texture-oriented features are able to encode the tremors during handwriting. In Reference [52], the recurrence plot technique was used to map the pen signals into the image domain; then, these images were used to instruct a CNN on how to learn discriminating features. A recurrence plot enables to visualize repeated events of higher dimensions through projections onto low-dimensional representations. [PERSON] et al. [29] assessed the validity of the digitized Archimedes spiral drawing as a biomarker for the early diagnosis of PD. Spatial and temporal variables of handwriting were, in general, significantly different between PD subjects and controls. A model using all featuresshowed high discriminating validity. Therefore, the authors claimed spiral analysis to be a promising quantitative biomarker for the early disease diagnosis. [PERSON] et al. [91] asked patients and healthy subjects to draw a horizontal line on the tablet's surface, keeping the pen's velocity as constant as possible. The choice of this simple task was made with the expectation that one should be able to detect differences between the groups even in very simple tasks, as the impairment manifests independently of the complexity of the task. Indeed, good accuracy performance were obtained with a Bayesian classifier. It is worth noting that, as an additional contribution, the authors introduced a new metric, termed normalized velocity variability, which quantifies the variability of the pen's horizontal speed as the line is drawn. The majority of the studies focused on the binary discrimination healthy vs unhealthy, independently of the degree of disease severity. In other words, the Parkinsonian group is typically considered as a single cluster in which all subjects share the same degree of disease severity. In Reference [92], [PERSON] et al. addressed this issue by investigating the correlation between the speed and pen pressure while sketching a spiral and the severity of disease symptoms. The strongest correlation was found with a combination of these two parameters, which turned out to be useful for the automatic differentiation between the low and high degree of severity. However, this measure was not able to differentiate between low and middle and between middle and high disease severity. In Reference [93], classification accuracy was refined by focusing on angular features and the count of direction inversion during the sketching of the spiral. In Reference [24], [PERSON] et al. also addressed this problem by performing a classification study on only a subset of the PaHaW dataset, focusing on the earlier and mild degree of disease severity. They found that classification performance significantly drops when considering this subset, instead of taking into account the entire dataset including the more severe cases. In this work, the authors also showed how a multi-expert approach based on ensembling the different tasks at disposal can provide better results than combining the features coming from each task into a unique high dimensional feature vector. [PERSON] et al. [94] further explored the application of deep learning techniques to aid PD diagnosis through handwriting by exploiting recurrent neural networks. These networks were used to obtain automatically significant features without human intervention from the time series data of the ParkinsonHW dataset [43]. [PERSON] et al. proposed a new methodology for the kinematic feature analysis of PD handwriting based on fractional derivatives of arbitrary order. Promising results using this techniques have been reported in Reference [95].