## 5 Conclusions  
This study deals with advanced approaches to PD dysgraphia diagnosis and monitoring based on FC integrated with online handwriting/drawing parameterization. To the best of our knowledge, it is the first work that performs a complex investigation into the possibilities for FC in online handwriting processing and proposes new advances in kinematic analyses based on FD. Although the conventional features provided better and very high classification accuracy, which is at the top of the state-of-the-art analyses based on the PaHaW database (ACC = 97.74%, \(\text{SEN}=95.50\%\), and \(\text{SPE}=100\%\)), the newly designed parameters were proven to work better for specific tasks (continuous and/or repetitive, such as the Archimedean spiral) and for specific applications, i.e., PD severity estimation (EER = 12.51%). However, our results need to be confirmed by subsequent scientific research.  
This study has several limitations and suggestions for further improvements. Since the dataset is small, to be able to generalize the results, bigger databases should be involved. On the other hand, it is common to have such small numbers of PD patients and HC samples in PD dysgraphia analysis, e.g., see our review in Table 1. Next, we considered only the kinematic measures. To better evaluate the discrimination power of the FD features and better evaluate their ability to estimate PD severity or progress, other feature types, such as temporal, spatial, and dynamic, should be included in future comparisons. Finally, the FD-based parameters could be further explored. For instance, we can consider other approximations (e.g., Caputo) or employ FC for other measures (e.g., entropies).  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/8/12/2566/s1](http://www.mdpi.com/2076-3417/8/12/2566/s1)]([http://www.mdpi.com/2076-3417/8/12/2566/s1](http://www.mdpi.com/2076-3417/8/12/2566/s1)), Table S1: Feature relevance from multivariate regression (modeling PD duration).  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; methodology, [PERSON] ([PERSON]), M.F.-Z. and [PERSON]. ([PERSON]); software, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]), M.F.-Z., [PERSON] ([PERSON]), I.R. and L.B.; resources, I.R. and L.B.; data curation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), Z.G., V.Z., T.K., L.B. and I.R.; writing--original draft preparation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), Z.G. and M.F.-Z.; visualization, [PERSON] ([PERSON]) and [PERSON]; supervision, [PERSON] ([PERSON]), [PERSON], [PERSON], M.F.-Z. and K.L.-d.-I; project administration, [PERSON] ([PERSON]), [PERSON], I.R., M.F.-Z. and K.L.-d.-I; funding acquisition, [PERSON] ([PERSON]), [PERSON], I.R., M.F.-Z. and K.L.-d.-I.  
**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 734718 (CoBeN). In addition, this work was supported by the grant of the Czech Science Foundation 18-168355 (Research of advanced developmental dysgraphia diagnosis and rating methods based on quantitative analysis of online handwriting and drawing) and the following projects: LO1401, FEDER and MEC, and TEC2016-77791-C4-2-R from the Ministry of Economic Affairs and Competitiveness of Spain. This article is based upon work from COST Action CA15225, a network supported by COST (European Cooperation in Science and Technology), and, for the research, infrastructure of the SIX Center was used.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Disclaimer:** The article reflects only the authors' view, and the Research Executive Agency (REA) is not responsible for any use that may be made of the information it contains.