Quantitative assessment of micrographia and tremor in static handwriting samples: analysis, diagnostic tools and accommodation.
The two most common movement disorders, Essential Tremor (ET) and Parkinson's disease (PD) affect about 11 million people only in the US. While PD and ET can be assessed through clinical tests, these tests remain relatively subjective, require expertise to implement, and may not always render reliable results when initial symptoms are subtle. An alternative to this is to study handwriting of subjects, since PD and ET strongly affect handwriting a precision task. A large pool of static existing handwriting samples provides rich information regarding symptomatic effects of PD and ET and their progression. However, detection of subtle yet relevant changes in handwriting as a manifestation of symptomatic progression or therapeutic response in PD and ET is quite challenging. A computerized toolkit based on quantitative analysis of static handwriting samples would be valuable as it could be used to supplement and support clinical assessments, help monitor symptomatic changes, and potentially link this monitoring to PD or ET. Especially, if it could detect relevant changes in handwriting morphology, thus enhancing the resolution of detection procedure. In this dissertation, innovative computerized metrics are presented, by which static handwriting samples can be analyzed to decide whether studied conditions affect the samples. Specifically, these metrics are tested and validated in their ability to measure (a) micrographia effects by comparing normal writing samples with symptomatic ones