Handwriting analysis to support neurodegenerative diseases diagnosis: A review

Stefano, Claudio De; Fontanella, Francesco; Impedovo, Donato; Pirlo, Giuseppe; Freca, Alessandra Scotto di

doi:10.1016/j.patrec.2018.05.013

Cited by 119 publications

(76 citation statements)

References 73 publications

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“…It is worth remarking that surveys on this topic have already been provided in References [12][13][14][15]. Neils-Strunjas et al [12] discussed papers focusing only on the static characteristics of handwriting in AD.…”

Section: Related Surveysmentioning

confidence: 99%

“…In particular, the authors proposed to extend the concept of "dysgraphia" also to PD, as it encompasses all deficit characteristics of Parkinsonian handwriting. De Stefano et al [14] and Impedovo and Pirlo [15] recently proposed surveys focusing both on PD and AD. In Reference [14], De Stefano et al made a categorization of works into statistical and classification studies, based on the methodological approach followed by the reviewed experiments.…”

Section: Related Surveysmentioning

confidence: 99%

“…De Stefano et al [14] and Impedovo and Pirlo [15] recently proposed surveys focusing both on PD and AD. In Reference [14], De Stefano et al made a categorization of works into statistical and classification studies, based on the methodological approach followed by the reviewed experiments. However, while the paper extensively reviews works on AD and MCI as well, at the time of writing, less research was done on classification of PD, thus forcing the authors to exclude several findings that are currently available.…”

Section: Related Surveysmentioning

confidence: 99%

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Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

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Section: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

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Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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“…This procedure results in a 921-dimensional feature vector per task containing a total of 452 kinematic features, 354 nonlinear dynamics features and 115 neuromotor features. Although we have not included all features proposed in the literature, we consider that this feature set is representative of the state-of-the-art [13].…”

Section: A Feature Extractionmentioning

confidence: 99%

“…Seven tasks are considered from a corpus with 16 PD/16 HC [11]. All these efforts and others have been summarized in recent surveys [12] [13]. To the best of our knowledge, this paper introduces the largest database for the analysis of online handwriting of PD patients and control users including both young healthy and elderly people with similar age and educational level with respect to the PD patients.…”

Section: Introductionmentioning

confidence: 99%

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

Castrillon

Acien

Orozco-Arroyave

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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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.

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The Impact of Neurological Disorders on Handwriting: Implications for Forensic Document Examination

Bansal,

Mishra,

Singhal

2024

WIREs Forensic Science

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Handwriting analysis is a valuable tool in forensic document examination, but the interpretation of observed findings can be complicated when individuals with neurological disorders are involved. The process of handwriting necessitates complex neuro‐muscular synchronization, which requires a much more intricate connection between the brain, muscles, and nerves than simply placing pen to paper. People with neurological illnesses, including Parkinson disease, Multiple System Atrophy, Alzheimer disease, and so on, may have trouble writing by hand. These illnesses can impair writing abilities physically and cognitively, resulting in inconsistencies or erratic handwriting that may be challenging to decipher. Because these illnesses impact a person's handwriting, it becomes challenging for document examiners to make an opinion about the handwriting of the author. A literature search was performed using PubMed, ScienceDirect, and Google Scholar databases to analyze the association between neurological disorders and handwriting and their implications for handwriting analysis. This review article explores the effects of neurological disorders on handwriting. It emphasizes the importance of examining and analyzing handwriting impacted by these illnesses with care and knowledge. Document examiners, forensic professionals, and investigators must comprehend these challenges brought on by neurological illnesses. In forensic settings, specialized training, expertise, and suitable forensic handwriting examination methods (collection, analysis, interpretation, and reporting) should be adopted effectively in cases that involve people suffering from neurological disorders. This review majorly focuses on the analysis and interpretation stage of the forensic handwriting examination.

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Handwriting analysis to support neurodegenerative diseases diagnosis: A review

Cited by 119 publications

References 73 publications

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

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

The Impact of Neurological Disorders on Handwriting: Implications for Forensic Document Examination

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