Analysis of Interpretable Handwriting Features to Evaluate Motoric Patterns in Different Neurodegenerative Diseases

Clinicians currently use handwriting as one of the methods to establish the presence and monitor the progression of neurodegenerative diseases (NDs). While common handwriting evaluation methods are valuable means to detect fine motor and cognitive impairments associated with NDs, these are observer-dependent and subjective. In the present study, we analyzed a broad array of interpretable features, some proposed for the first time in this study, obtained from online handwriting data of participants with NDs and control subjects (CTRL). ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). Hand-writing data from three different neuropsychological tasks was used: Copy Text task, Copy Cube task, and Copy Image task. Then, we arranged three complementary sets of features and conducted a statistical analysis to test their significance between groups. Overall results suggested that subjects with AD reported a significantly higher $(p < 0.05)$ amount of data points and total duration with respect to the CTRL group in almost all the tasks under assessment. On the other hand, subjects with PD showed a significantly lower $(p < 0.05)$ horizontal width (both on tablet and in the air). Even though the AD and PDM groups showed a significantly lower velocity and acceleration $(p < 0.05)$, their number of inversions in velocity and acceleration was significantly greater $(p < 0.05)$, which indicates disfluency in writing. The features that we have used were found to provide good results in differentiating the studied groups and could be considered as part of diagnostic tools for the assessment and monitoring of NDs in clinical trials.