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).
Highlights
Speech and handwriting are probably the most difficult tasks performed by human beings, because they differentiate us from animals
Experimental results of BIOSECUR-ID database, which is the largest one according to the number of users and files, reveal that in-airL is almost negligible in the case of signatures, but interestingly, it is three times larger for skilled forgeries than for genuine signatures
mild cognitive impairment (MCI)/control Crossed pentagons Spiral 3D house Clock Spontaneous sentence Sentence copied Sentence dictation point out that this kind of measurements offers a large set of features that can be extracted, such as speed and acceleration of trajectories and complexity measurements extracted from coordinates x, y
Summary
Speech and handwriting are probably the most difficult tasks performed by human beings, because they differentiate us from animals. Handwriting analysis is a good way to study the human brain in a non-invasive way. This knowledge, once acquired, can be applied to artificial systems that emulate the human brain. We will analyze this kind of movements, which will be defined in posterior sections as in-air at long distance. This kind of movements can be used to improve artificial intelligence for biometric applications such as health and security [1,2,3,4]
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