# Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
## 2 Typical Experimental Design
### Feature Extraction  
The horizontal and vertical components of handwriting, as recorded by the tablet, are typically segmented into on-surface and in-air strokes in accordance with the button status. A _stroke_ corresponds to a single trait of the handwritten pattern which is connected and continuous, i.e., between two consecutive pen-lifts. By using the Cartesian coordinates of the sampled points and their time stamps, several features can then be calculated for both on-surface and in-air strokes. Kinematic features include number of strokes; tangential, horizontal, and vertical displacement, velocity, acceleration, and jerk; number of changes of velocity/acceleration (NCV/NCA); and NCA \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Groups** & **Apparatus** & **Tasks** & **Reference** \\ \hline PaHaW & 37 PD, 38 EC & Wacom Intuos 4M & Spiral drawing, repetition of characters, words, and sentence writing & [PERSON] et al. [38] \\ NewHandPD & 31 PD, 35 EC & Biometric Smart Pen & Spiral and meander drawing & [PERSON] et al. [23] \\ ParkinsonHW & 62 PD, 15 EC & Wacom Cintiq 120 VX & Spiral drawing and stability test & [PERSON] et al. [43] \\ ISUNIBA & 29 AD, 12 EC & Wacom Intuos Touch 5 & Repetition of a single word & [PERSON] et al. [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets (PD = Parkinson’s disease; AD = Alzheimer’s disease; EC = elderly controls). and NCV relative to writing duration. Displacement corresponds to the straight-line distance between two consecutive sampled points: it provides a good approximation of the pen trajectory. From displacement, velocity, acceleration, and jerk can be straightforwardly calculated as the first, second, and third derivatives of displacement, respectively. Analogously, displacement, velocity, acceleration, and jerk can be calculated with respect to both the horizontal and vertical directions. NCV and NCA are the mean number of local extrema of tangential velocity and acceleration, respectively. Spatiotemporal features include stroke size and duration; speed and stroke speed; stroke height and width; on-surface and in-air time; total time; normalized on-surface and in-air time; and in-air/on-surface ratio. In order to make use of the pressure signal, the following dynamic handwriting measures are also typically calculated: mean pressure; number of changes of pressure (NCP); and relative NCP. NCP was proposed in Reference [38], and its meaning is analogous to the concept of NCV/NCA, explained above. In order to capture the randomness and irregularity of fine movements, which are difficult to analyze using only the abovementioned features, the following features can also be computed for both the on-surface and in-air horizontal and vertical components of handwriting [37]: Shannon and Renyi entropy; signal-to-noise ratio (SNR); and empirical mode decomposition (EMD). EMD iteratively decomposes the signal into so-called intrinsic mode functions (IMFs), which are functions that satisfy two requirements: (1) the number of extrema and the number of zero crossings are either equal or differ at most by one, and (2) the mean of their upper and lower envelopes equals zero. It is worth noting that, to obtain complete statistical representations of the available features, statistical functions of the feature vector are also computed. They include means, percentiles, moments, and other statistical functions (range, median, mode, standard deviation, etc.). In addition, note that features are generally normalized before classification so as to have zero mean and unit variance. An alternative approach to modeling the handwritten patterns is to use the Kinematic Theory of Rapid Human Movements [47; 48] and, in particular, the so-called sigma-lognormal (\(\Sigma\Lambda\)) model [49]. This model has been used with successful results in many practical applications, for example, for developing an online signature verification system [50] and for analyzing graphomotor performance in kindergarten children [51].