Features of digitally captured signatures vs. pen and paper signatures: Similar or completely different?

Dynamic feature analysis of handwritten electronic signatures based on Fourier transform

The utilization of handwritten electronic signatures has expanded in various application scenarios, leading to an increased demand for identification. Unlike handwriting signatures, handwritten electronic signatures offer the advantage of extracting dynamic feature data, including writing pressure, velocity, and acceleration. In this study, the Fourier transform was employed to extract 18 characteristics from the time domain and frequency domain of writing pressure, velocity, and acceleration. The experimental findings revealed distinguishable differences between genuine signatures and random forgeries in writing pressure. However, no statistically significant differences were observed in writing velocity and writing acceleration. Moreover, significant differences were detected in most characteristics when comparing genuine signatures with freehand imitation forgeries and tracing imitation forgeries. The canonical discriminant analysis was performed between the genuine and Non-genuine signatures; the cross-validation estimated the discriminating power of these characteristics with a satisfactory result. The study proposed a new approach to analyzing handwritten electronic signatures using time-domain and frequency-domain characteristics and demonstrated its effectiveness in the examination.

Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits

Introduction

Since the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors.

Methods

Forty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability.

Results

Compared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4-7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important.

Conclusion

Indicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed.

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.

Self-identification of electronically scanned signatures (ESS) and digitally constructed signatures (DCS)

The use of electronic signatures as a form of identification is increasingly common, yet they have been shown to lack the dynamic features found in online signatures. In this study, handwritten signatures were scanned to produce electronically scanned signatures (ESS) which were then digitally altered to produce digitally constructed signatures (DCS). The ESS and DCS were presented back to participants to identify which were genuine. Only 1% of participants correctly identified all signatures, with a mean score of 57.6% identifications. The lack of self-recognition of ESS raises questions on their reliability and usefulness as means of personal identification.