# Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
## 3 Research Directions
### Disease Diagnosis
[104] evaluated the usefulness of handwriting features obtained with an electronic pen to distinguish MCI patients from controls. Subjects with confirmed MCI needed more time to complete two out of three writing tasks, as their writing was significantly slower. These results were associated with a longer time to complete a single stroke of written text. The written text was also noticeably larger in the MCI group in all three tasks. [PERSON] et al. [30] investigated movement kinematics between patients with early dementia due to probable AD, patients with amnestic MCI, and cognitively healthy control individuals while copying a three-dimensional house using a digitizing tablet. Receiver operating characteristic (ROC) curves and logistic regression analyses were conducted to explore whether alterations in movement kinematics could be used to discriminate patients with MCI and AD from controls. In-air time differed significantly between the three groups, showing an excellent sensitivity and a moderate specificity to discriminate MCI subjects from normal elderly and an excellent sensitivity and specificity to discriminate patients affected by mild AD from healthy individuals. On-surface time differed only between controls and patients with AD but not between controls and patients with MCI. Furthermore, the total time (i.e., in-air plus on-surface time) did not differ between patients with MCI and early dementia due to AD. In Reference [105], [PERSON] et al. reported the results of an experiment analogous to the previous one, employing the same apparatus and the same participants. What differed from the previous study was the task the participants were requested to perform, which consisted in a digitized version of the classic Clock Drawing Test. While the traditional CDT revealed only poor sensitivity but excellent specificity in discriminating MCI patients from healthy individuals, excellent sensitivity and a good specificity were obtained in discriminating these groups when considering the digital version of the test. In Reference [106], this research was extended by comparing the digital Clock Drawing Test with the traditional Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological test score. Digital Clock Drawing Test (dCDT) provided a slightly better diagnostic accuracy for the discrimination of amnestic MCI from controls than using the CERAD score. Instead, in differentiating patients with mild AD from controls, both tests provided excellent accuracy. [PERSON] et al. [107] proposed a novel paradigm for studying handwriting changes due to cognitive decline (or aging) by addressing the major limitations of the state-of-the-art solutions. The first one is the assumption of a unique behavioural trend for each cognitive profile. This restriction was relaxed by allowing, for each profile, the emergence of a multi-modal behavioural pattern reflecting the diversity of behaviours within a given healthy condition. The authors achieved this by using unsupervised or semi-supervised learning algorithms to uncover homogeneous groups of subjects and byanalyzing how much information these clusters carry on about the cognitive profiles. The second main limitation is the encoding of handwriting spatiotemporal dynamics only by using global or semi-global parameters, assumed implicitly to be discriminant. The proposed method is based on a representation learning approach which is suitable for treating sequential data from which temporal feature representations can be uncovered. A key advantage of this temporal representation learning is that it is fully explainable, as it can be visualized and easily understood. The main finding of the work, from a diagnostic perspective, is that MCI individuals tend to form clusters either with controls or AD patients, revealing that MCI individuals have fine motor skills with characteristics from both the two extreme groups. An approach similar to the previous one was followed by the authors in Reference [108].