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In this paper we present a software tool for displaying and analyzing handwriting signals recorded by digitizing devices that capture both in-air and on-surface trajectories. This tool has two salient features: it can visualize the invisible trajectories described by the writing device while it is not exerting any pressure on the writing surface and it can calculate entropies and fractal dimensions, two measures that are relevant when analyzing handwriting signals for health-related purposes, mainly in the diagnose of some neurodegenerative conditions like Parkinson’s disease and Alzheimer’s disease.

Essential tremor (ET) in the western world is the most common movement disorder, and 50-70% of essential tremor cases are estimated to be genetic in origin 14. This work on selection of nonlinear biomarkers derived from drawings and handwriting is part of a wider cross-study for the diagnosis of essential tremor led by Biodonostia Institute. These biomarkers include not only classic linear features, but also non-linear: fractal dimension and entropy. The presence of integrated features of other diseases such as stress is also analyzed. In future works, these new biomarkers will be integrated with the ones obtained in the wider study of Biodonostia. Note that the use of these methods provide undoubted benefits towards the development of more sustainable, low-cost, high-quality, and non-invasive technologies. These systems are easily adaptable to the user and environment, and can be very useful in real complex environments with regard to a social and economic point of view.

Among neural disorders related to movement, essential tremor has the highest prevalence, in fact, it is twenty times more common than Parkinson&rsquo, s disease. The drawing of the Archimedes&rsquo, spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users, and taking into account social and economic points of view, it could be very helpful in real complex environments.

This paper presents a handwritten text biometric recognition system suitable to be applied to short sequences of text (words). Strokes are considered the structural units of handwriting with words being regarded as two separate sequences: one of pen-down and one of pen-up strokes. Unsupervised categorization by means of a self-organized map allows mapping strokes to integers and the efficient comparison of the sequences by means of dynamic time warping. Measures obtained from each sequence are combined in a later step. This separation gives us the opportunity to show that pen-up strokes possess a surprisingly high discriminative power, while the performance of the combination suggests they may carry non-redundant information with respect to pen-down strokes. A writer identification rate of 92.38% and a minimum of detection cost function of 0.046 (4.6%) is achieved with 370 users and just one word. Results are improved up to 96.46% and 0.033 (3.3%) when combining two words.

This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer recognition and that they can be effectively combined to enhance the recognition accuracy. With samples from the BiosecurID database, we have analysed the entropy of each kind of trajectories, as well as the amount of information they share, and the difference between intra- and inter-writer measures of the mutual information. The results show that when pressure is not taken into account, the amount of information is similar in both types of trajectories. Furthermore, even if they share some information, in-air and on-surface trajectories appear to be notably non-redundant.

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1-1 %, and an annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis of PD is 55 and most patients are between 50 and 80 years old. The most obvious symptoms are movement-related; these include tremor, rigidity, slowness of movement and walking difficulties. Frequently these are the symptoms that lead to the PD diagnoses. Later, thinking and behavioral problems may arise, and other symptoms include cognitive impairment and sensory, sleep and emotional problems. In this paper we will present an ongoing project that will evaluate if voice and handwriting analysis can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice and handwritten analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems.

In this paper we present experimental results comparing on-line drawings for control population (left and right hand) as well as Alzheimer disease patients. The drawings have been acquired by means of a digitizing tablet, which acquires time information angles and pressures. Experimental measures based on pressure and in-air movements appear to be significantly different for both groups, even when control population performs the tasks with the non-dominant hand.

In this paper we present some experiments to automatically classify online handwritten text based on capital letters. Although handwritten text is not as discriminative as face or voice, we still found some chance for gender classification based on handwritten text. Accuracies are up to 74%, even in the most challenging case of capital letters..

This paper is an extended version of one paper published in the 4th IEEE International Work Conference on Bioinspired Intelligence, Donostia, Spain, 9–12 June 2015 Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson's disease. Interestingly, about 50%-70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes' spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments. This work has been partially supported by the University of the Basque Country under project ref. UPV/EHU-58/14, SAIOTEK program and others from the Basque Government, the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03, the University of Vic-Central University of Catalonia under the research grant R0904, INNPACTO program from the Spanish Government, and UPV/EHU Summer Courses Foundation.