# Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
## 3 Research Directions
### Parkinson's Disease
#### 3.1.3 Disease Diagnosis  
In Reference [22], [PERSON] et al. focused on approaches for PD diagnosis based on the pressure information provided by the electronic biosensor BiSP pen. It turned out that the most discriminating feature, which achieved an Area Under the ROC Curve (AUC) equal to 0.933, was based on the difference between the controlled writing pressure in the \(x\)-\(y\) direction and the tilt tremor of the pen. Itwas observed, in fact, that, for PD patients, the tremor control is better achieved during movements (like handwriting) instead of constant pressure (pen tilt). A remarkable contribution to the application of machine learning algorithms to the automatic discrimination of PD was provided by [PERSON] et al. All their studies were carried out on a same dataset, i.e., PaHaW, which the authors made freely available. In Reference [85], by comparing the predictive potential of models built on every task individually and models trained merging all tasks, the authors found that the best classification performance was reached by the combination of all tasks. In Reference [86], the authors investigated the extent to which classification performance can be improved considering not only on-surface but also in-air movement, since the two modalities appear to carry on nonredundant information. They found that in-air features outperform on-surface features. These findings were further improved in Reference [87], where different feature selection strategies were employed. In addition to conventional kinematic handwriting measures, [PERSON] et al. [37] also computed novel measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. These features provided more insight and better understanding of the data. It is worth noting that, in this study, only on-surface movement was considered. In Reference [88], instead, the authors employed these novel features also considering in-air movement. In Reference [38], the authors introduced additional features based on the pressure exerted over the writing surface. The fundamental pressure features were the value of pressure as captured by the tablet during the particular task and the rate at which pressure changes with respect to time. Then, they introduced correlation coefficients to capture the relationship between pressure and kinematic features. It is worth remarking that, in all of the studies by [PERSON] et al., the spiral task was undertaken with no significant impact on classification. This may have been due to the use of measures only tailored to handwriting; instead, visual features, for example, those provided by deep learning algorithms [25; 89], seem to overcome this issue. [PERSON] et al. [21] assessed whether simple characteristics of handwriting can provide quantitative measures to accurately differentiate between PD patients and controls. Study participants were requested to write their name and to copy an address. Significant group effects were observed: compared to controls, patients wrote smaller letters, applying less pressure and requiring more performance time. A discriminant function was found for the effective group classification of all participants. Furthermore, the authors highlighted the importance to analyze handwriting not only on-paper but also in-air, as significant differences were observed between these two writing conditions. In fact, as the authors wrote, in-air time is a manifestation of \"planning the next movement\", which can reflect cognitive ability and supply information about the writer. In Reference [23], [PERSON] et al. proposed NewHandPD, a dataset of signals extracted from the BiSP smart pen comprising spiral and meander drawings. Each sensor of the device outputs the whole signal acquired during the handwriting tasks; thus, it can be subsequently represented as a time series. The authors used CNNs and meta-heuristic-based optimization techniques to fine-tune the network hyper-parameters due to their ability to learn without human intervention.