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13 pages, 1585 KiB

Open AccessArticle

Analyzing Arabic Handwriting Style through Hand Kinematics

by Vahan Babushkin, Haneen Alsuradi, Muhamed Osman Al-Khalil and Mohamad Eid

Sensors 2024, 24(19), 6357; https://doi.org/10.3390/s24196357 - 30 Sep 2024

Cited by 1 | Viewed by 1147

Handwriting style is an important aspect affecting the quality of handwriting. Adhering to one style is crucial for languages that follow cursive orthography and possess multiple handwriting styles, such as Arabic. The majority of available studies analyze Arabic handwriting style from static documents, focusing only on pure styles. In this study, we analyze handwriting samples with mixed styles, pure styles (Ruq’ah and Naskh), and samples without a specific style from dynamic features of the stylus and hand kinematics. We propose a model for classifying handwritten samples into four classes based on adherence to style. The stylus and hand kinematics data were collected from 50 participants who were writing an Arabic text containing all 28 letters and covering most Arabic orthography. The parameter search was conducted to find the best hyperparameters for the model, the optimal sliding window length, and the overlap. The proposed model for style classification achieves an accuracy of 88%. The explainability analysis with Shapley values revealed that hand speed, pressure, and pen slant are among the top 12 important features, with other features contributing nearly equally to style classification. Finally, we explore which features are important for Arabic handwriting style detection. Full article

(This article belongs to the Special Issue Sensor-Based Behavioral Biometrics)

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19 pages, 6821 KiB

Open AccessArticle

An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks

by Mariusz Kurowski, Andrzej Sroczyński, Georgis Bogdanis and Andrzej Czyżewski

Electronics 2021, 10(4), 456; https://doi.org/10.3390/electronics10040456 - 12 Feb 2021

Cited by 15 | Viewed by 6103

Abstract

Handwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated analysis method for the dynamic electronic representation of handwritten signature authentication was researched. The developed algorithms are based on the dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the designed electronic pen described in the paper. The triplet loss method was used to train a neural network suitable for writer-invariant signature verification. For each signature, the same neural network calculates a fixed-length latent space representation. The hand-corrected dataset containing 10,622 signatures was used in order to train and evaluate the proposed neural network. After learning, the network was tested and evaluated based on a comparison with the results found in the literature. The use of the triplet loss algorithm to teach the neural network to generate embeddings has proven to give good results in aggregating similar signatures and separating them from signatures representing different people. Full article

(This article belongs to the Special Issue Application of Neural Networks in Biosignal Process)

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20 pages, 3255 KiB

Open AccessArticle

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

by Eugênio Peixoto Júnior, Italo L. D. Delmiro, Naercio Magaia, Fernanda M. Maia, Mohammad Mehedi Hassan, Victor Hugo C. Albuquerque and Giancarlo Fortino

Sensors 2020, 20(20), 5840; https://doi.org/10.3390/s20205840 - 15 Oct 2020

Cited by 18 | Viewed by 5161

Abstract

In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic. Full article

(This article belongs to the Special Issue Sensors with Machine Learning Methods for Assisted Systems - Recent Advances and Future Trends)

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12 pages, 2881 KiB

Open AccessArticle

Meta-Analyzing the Writing Process of Structural Language to Develop New Writing Analysis Elements

by Eun Bin Kim, Eun Young Kim and Onseok Lee

Appl. Sci. 2020, 10(10), 3479; https://doi.org/10.3390/app10103479 - 18 May 2020

Cited by 3 | Viewed by 4013

Abstract

As the basis of communication, a writer is often identified through their handwriting characteristics. In clinical practice, static elements of handwriting are evaluated and scored, which might be used for subjective judgment in health situations. By investigating the dynamic information in space when writing Hangul, in this study, we present how to analyze Hangul writing characteristics and build new writing analysis elements in the structural language. The ample characters included 14 consonants and 10 vowels. The cloud of line distribution (COLD) method was used to visualize on-stroke characteristics when writing each character. If the written character showed a straight line (the angle of the letter being 0), the feature distribution appeared on the x-axis of the polar domain. If the written character had many kinks (the angle of the letter being −90 or 90), the feature distribution appeared on the polar domain’s y-axis. In-air movement was visualized using principal component analysis (PCA), and typical in-air movement had an annular shape, which might be used as a new element in handwriting analysis. This study shows the possibility of using such a tool for the writing analysis of structural languages. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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33 pages, 501 KiB

Open AccessArticle

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

by Gennaro Vessio

Appl. Sci. 2019, 9(21), 4666; https://doi.org/10.3390/app9214666 - 1 Nov 2019

Cited by 68 | Viewed by 9192

Abstract

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. Full article

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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11 pages, 283 KiB

Open AccessArticle

Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis

by Donato Impedovo, Giuseppe Pirlo and Gennaro Vessio

Information 2018, 9(10), 247; https://doi.org/10.3390/info9100247 - 3 Oct 2018

Cited by 75 | Viewed by 7968

Abstract

Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease. Full article

(This article belongs to the Special Issue eHealth and Artificial Intelligence)

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