A Comparative Study of In-Air Trajectories at Short and Long Distances in Online Handwriting

Abstract

Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the writing surface. This is due to the fact that current acquisition devices do not provide height formation. However, it is quite straightforward to differentiate movements at two different heights (a) short distance: height lower or equal to 1 cm above a surface of digitizer, the digitizer provides x and y coordinates; (b) long distance: height exceeding 1 cm, the only information available is a time stamp that indicates the time that a specific stroke has spent at long distance. Although short distance has been used in several papers, long distances have been ignored and will be investigated in this paper. In this paper, we will analyze a large set of databases (BIOSECUR-ID, EMOTHAW, PaHaW, OXYGEN-THERAPY, and SALT), which contain a total amount of 663 users and 17,951 files. We have specifically studied (a) the percentage of time spent on-surface, in-air at short distance, and in-air at long distance for different user profiles (pathological and healthy users) and different tasks; (b) the potential use of these signals to improve classification rates. Our experimental results reveal that long distance movements represent a very small portion of the total execution time (0.5% in the case of signatures and 10.4% for uppercase words of BIOSECUR-ID, which is the largest database). In addition, significant differences have been found in the comparison of pathological versus control group for letter “l” in PaHaW database (p = 0.0157) and crossed pentagons in SALT database (p = 0.0122).

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

“…Online handwriting signals were recorded with a sampling frequency of f s = 150 Hz. The following time sequences were acquired: x and y coordinates (x[t], y[t]); time-stamp (t); in-air/on-surface (on-surface movement is a movement of a pen when its tip is touching the surface, e.g., paper (i.e., it provides the information about the pen writing/drawing on the paper); vice versa, in-air movement is a movement of a pen when its tip is up to 1.5 cm above the surface [42,43] Tramvaj dnes už nepojede. The tram will no longer go today.…”

Advanced Parametrization of Graphomotor Difficulties in School-Aged Children

“…More specifically, instead of a conventional data acquisition using a pen and paper, digitizing tablets (digitizers) have been used to record a variety of signals describing the evolution of handwriting in time. Such a collection of data about handwriting (i. e. that one associated with timestamps) is referred to as online handwriting [29]. Using advanced digital signal processing algorithms a variety of handwriting parameters (commonly referred to as handwriting features) quantifying kinematic (velocity, acceleration, jerk) as well as dynamic (pen pressure, tilt and azimuth) components contributing to the execution of the handwriting process have been designed [6], [30]- [32].…”

Quantitative Analysis of Relationship Between Hypokinetic Dysarthria and the Freezing of Gait in Parkinson’s Disease

“…For example, Rosenblum and Luria ([ 61 ]) proposed a system that can be used to monitor cognitive deficits in PD and AD patients using an online handwriting analysis. The advantages and limitations of in-air trajectories analysis for the purpose of PD, AD and mild cognitive impairment (MCI) diagnosis were discussed by Alonso-Martinez et al ([ 2 ]).…”