  Using kinematic properties of handwriting to support the diagnosis of
neurodegenerative disease is a real challenge: non-invasive detection
techniques combined with machine learning approaches promise big steps forward
in this research field. In literature, the tasks proposed focused on different
cognitive skills to elicitate handwriting movements. In particular, the meaning
and phonology of words to copy can compromise writing fluency. In this paper,
we investigated how word semantics and phonology affect the handwriting of
people affected by Alzheimer's disease. To this aim, we used the data from six
handwriting tasks, each requiring copying a word belonging to one of the
following categories: regular (have a predictable phoneme-grapheme
correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme
correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter
strings that conform to phoneme-grapheme conversion rules). We analyzed the
data using a machine learning approach by implementing four well-known and
widely-used classifiers and feature selection. The experimental results showed
that the feature selection allowed us to derive a different set of highly
distinctive features for each word type. Furthermore, non-regular words needed,
on average, more features but achieved excellent classification performance:
the best result was obtained on a non-regular, reaching an accuracy close to
90%.
