Characterization of the Handwriting Skills as a Biomarker for Parkinson’s Disease

Abstract

In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson's disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, knearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.

Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

“…Similar accuracy rates were obtained in a different study by [47], which analyzed various handwriting tasks to discriminate between Parkinson's disease (PD) vs. elder healthy controls. Better accuracy rates around 96% were also obtained in [47] when comparing PD vs. young healthy controls. The in-air movement also supports the diagnosis of Alzheimer's disease (AD) [48].…”

“…While significant scientific developments exist in the field of biometric recognition using the handwritten signature for security [4,18], few efforts have been made in general handwriting [20] and drawing [38] for security. Few efforts have also been deployed in health applications [3,17,47,83,99] as well as in possibilities of combining security and health.…”

“…As a summary of the research discussed in the present paper, on the one hand, the state of the art in applications based on online handwritten tasks can be considered: a) Mature in security applications based on signature [4,18] b) Incipient in security applications based on the handwritten text [20,36] c) Incipient in security applications based on drawing tasks [37,38] d) Incipient in health applications based on handwriting text and drawing tasks [3,47,[100][101][102]] e) Incipient in health applications based on signature [3,18,47,99] On the other hand, it is difficult to find published research in joint applications where security is studied in combination with one of the following health-related aspects: f) Is the user to authenticate under stress? g) Is he suffering any disease that makes him unable to understand the real implication on his acts?…”

“…For the future, we see four main areas of research in online handwriting analysis for security and health, as shown in Table 2: & Processing, analysis, and recognition of handwriting signals; with special emphasis in motor models based on neuroscience [103] and methods based on deep learning [101,104] able to make the most of large-scale datasets [105]. & Support for early diagnosis [99] of pathologies using handwriting, especially neurodegenerative diseases [3,17,47,83]. & Continuously monitoring the evolution of neurodegenerative diseases, rehabilitation, and healthy aging [56,82]; using handwriting and other touch interaction signals [106].…”

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

“…At the time of writing, Castrillón et al [45] are developing a large set of Parkinsonian handwritten patterns, including samples from adult and young healthy individuals. Concerning AD, the Handwriting Analysis against Neuromuscular Disease (HAND) project, among its goals, intended to release a large dataset of a battery of handwriting tasks performed by elderly controls and by people suffering from MCI and neurodegenerative dementia [32,46].…”