Recommendations for capturing signatures digitally to optimize their suitability for forensic handwriting examination

Handwriting Changes in Alzheimer's Disease: A Systematic Review

BACKGROUND

Handwriting is a complex process involving fine motor skills, kinesthetic components, and several cognitive domains, often impaired by Alzheimer's disease (AD).

OBJECTIVE

Provide a systematic review of handwriting changes in AD, highlighting the effects on motor, visuospatial and linguistic features, and to identify new research topics.

METHODS

A search was conducted on PubMed, Scopus, and Web of Science to identify studies on AD and handwriting. The review followed PRISMA norms and analyzed 91 articles after screening and final selection.

RESULTS

Handwriting is impaired at all levels of the motor-cognitive hierarchy in AD, particularly in text, with higher preservation of signatures. Visuospatial and linguistic features were more affected. Established findings for motor features included higher variability in AD signatures, higher in-air/on-surface time ratio and longer duration in text, longer start time/reaction time, and lower fluency. There were conflicting findings for pressure and velocity in motor features, as well as size, legibility, and pen lifts in general features. For linguistic features, findings were contradictory for error patterns, as well as the association between agraphia and severity of cognitive deficits.

CONCLUSIONS

Further re-evaluation studies are needed to clarify the divergent results on motor, general, and linguistic features. There is also a lack of research on the influence of AD on signatures and the effect of AD variants on handwriting. Such research would have an impact on clinical management (e.g., for early detection and patient follow-up using handwriting tasks), or forensic examination aimed at signatory identification.

Digitally captured signatures: A method for the normalization of force through calibration and the use of the zeta function

Digitally captured signatures (DCS) are entering the scope of the Forensic Handwriting Examiner (FHE), and the meaningful comprehension and comparison of the captured information is essential in order to proceed to the examination of authenticity of DCS. In DCS solutions, force is captured and presented as pressure levels, but the relation between exercised force and the assigned pressure levels is not provided by the manufacturers of these solutions. For this purpose, we constructed an experimental array that allows the correlation of exercised force and assigned pressure levels, in a combination of three different digitizers, six different styli and four different capturing software. This process let us calculate the correlation function that assigns pressure levels to force for each solution (called the Zeta Function). Through this process, it was observed that different solutions follow different Zeta functions. To address this problem, a methodology for normalization of captured data between different solutions was created and demonstrated, using the calculated Zeta function and its inverse.