SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems.

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG's resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature's image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.

The Improved Biometric Identification of Keystroke Dynamics Based on Deep Learning Approaches.

In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promising avenue. This study highlights the untapped potential of keystroke dynamics, emphasizing its non-intrusive nature and distinctiveness. While keystroke dynamics analysis has not achieved widespread usage, ongoing research indicates its viability as a reliable biometric identifier. This research builds upon the existing foundation by proposing an innovative deep-learning methodology for keystroke dynamics-based identification. Leveraging open research datasets, our approach surpasses previously reported results, showcasing the effectiveness of deep learning in extracting intricate patterns from typing behaviors. This article contributes to the advancement of biometric identification, shedding light on the untapped potential of keystroke dynamics and demonstrating the efficacy of deep learning in enhancing the precision and reliability of identification systems.

ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition.

Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different writing styles, cursive scripts, and limited data make Urdu text recognition a complicated task. Major languages, such as English, have experienced advances in automated recognition, whereas low-resource languages, such as Urdu, still lag. Transformer-based models are promising for automated recognition in high- and low-resource languages such as Urdu. This paper presents a transformer-based method called ET-Network that integrates self-attention into EfficientNet for feature extraction and a transformer for language modeling. The use of self-attention layers in EfficientNet helps to extract global and local features that capture long-range dependencies. These features proceeded into a vanilla transformer to generate text, and a prefix beam search is used for the finest outcome. NUST-UHWR, UPTI2.0, and MMU-OCR-21 are three datasets used to train and test the ET Network for a handwritten Urdu script. The ET-Network improved the character error rate by 4% and the word error rate by 1.55%, while establishing a new state-of-the-art character error rate of 5.27% and a word error rate of 19.09% for Urdu handwritten text.

The role of handwriting in English word acquisition among elementary students.

In order to investigate whether handwriting has an advantage in learning word form, sound, and meaning, this study randomly selected 40 elementary school student participants (20 males, 20 females, aged 11.4 ± 1.34 years). Using an experimental approach, we compared the learning outcomes of word sound matching, word meaning matching, and word form judgment tasks under two conditions: handwriting and visual learning. After three consecutive days of learning and testing, we found that handwriting generally outperformed visual learning in terms of accuracy and response time in word form, sound, and meaning learning. Additionally, we observed differences in the timing of significant discrepancies in learning outcomes between the two methods across the three tasks. Specifically, in terms of accuracy, discrepancies first appeared in the word sound matching task on the first day, followed by the word form judgment task, and lastly the word meaning matching task. Regarding response time, significant differences between learning methods first emerged in the word form judgment task, followed by the word sound and word meaning tasks. Thus, combining accuracy and response time data, we conclude that handwriting is more advantageous than visual learning for word acquisition, with a differential impact on word form, sound, and meaning, where word form and sound are prioritized over meaning.

Handwriting difficulties in children with dyslexia: Poorer legibility in dictation and alphabet tasks, slowness in the alphabet task.

Several studies have shown that children with dyslexia (DYS), in addition to their reading and spelling deficits, encounter handwriting difficulties that are still poorly understood in terms of their nature and origin. The present study aimed to better understand the handwriting difficulties of children with DYS by comparing their handwriting quality and speed in two tasks, a dictation task and an alphabet task, which required fewer spelling skills than the dictation task. Twenty-nine French-speaking children (Mage = 9.5 years) participated in the study, including 18 children with DYS and nine typically developing (TD) children matched on chronological age. The children performed control tasks, a dictation task with words varying in graphic and orthographic complexity and an alphabet writing task. Accuracy, handwriting quality (legibility), and fluency (speed, writing and pause time) were carefully measured using a digital tablet. GLMM analysis and t tests showed that children with DYS made more aesthetic errors (handwriting quality) in both the dictation and alphabet task than TD children. They also wrote more slowly than TD children in the alphabet task (speed, pause time). These findings suggest that children with DYS present handwriting difficulties, even in a simple alphabet task. In dictation, they seem to favour speed at the expense of handwriting quality.

Signatures as an object of autoforgery (self-forgery). / Podpisy jako obiekt autofalszerstwa.

The study presents the results of research aimed at isolating the graphic features most frequently and least frequently modified by people committing autoforgery (self-forgery) of signatures in situations where the appearance of their natural signatures is not known to the recipient. The research covered a total of over 12,000 signatures from 200 individuals. The most successful attempts at autoforgery of legible and illegible signatures of each test subject were selected for the final evaluation. It was found that autoforgery changes are most often focused on the most striking features of the signatures, such as the structure of letters in the initial part of the signature, size, readability, impulse, and slope. Secondary features, more difficult to notice or those whose existence the writers are not aware of (such as the presence or absence of additions, the arrangement of letters in relation to each other, the shape and direction of signature lines, the format of legible signatures) are usually omitted in autoforgery activities. Detecting autoforgery can be a big challenge for experts, because in practice, any significant differences between the questioned signature and comparative signatures are often mistakenly considered to be the result of forgery. Therefore, in order to detect autoforgery, it is necessary to analyze the structure of easily noticeable features that most influence the so-called pictorial effect of the signature in combination with the unattractive features that remain unchanged in most cases of autoforgery. The more characteristic the latter are, the more their consistency in the questioned and comparative material proves self-forgery, regardless of the differences in the primary features. In the case of a forged signature, the opposite is true: the most easily noticeable features of the signature are imitated by the forger, and the differences occur mainly in secondary features.

Handwriting Markers for the Onset of Alzheimer's Disease.

INTRODUCTION: Alzheimer's disease has an impact on handwriting (AD). Numerous researchers reported that fact. Therefore, examining handwriting characteristics could be a useful way to screen for AD. The aim of the article is to present the reliability and effectiveness of the AD-HS tool. METHODS: Most of the existing studies examine either linguistic manifestations of writing or certain motor functions. However, handwriting is a complex of cognitive and motor activities. Since the influence of AD on handwriting is individual, it is important to analyze the complete set of handwriting features. The AD-HS instrument is based on this principle. Validation of the AD-HS instrument for revealing cognitive impairment in AD-diagnosed persons in comparison to the control group. The study is based on the evaluation of free handwritten texts. AD-HS includes 40 handwriting and 2 linguistic features of handwritten texts. It is based on the standard protocol for handwriting analysis. The cumulative evaluation of all features builds a quantitative AD-Indicator (ADI) as a marker of possible AD conditions. The analyzed experiment includes 53 AD-diagnosed persons and a control group of 192 handwriting specimens from the existing database. RESULTS: AD-HS shows a distinct difference in evaluated ADI for the participants (the mean value equals 0.49) and the control group (the mean value equals 0.28). CONCLUSION: The handwriting marker of AD could be an effective supplement instrument for earlier screening. It is also useful when traditional biomarkers and neurological tests could not be applied. AD-HS can accompany therapy as an indication of its effect on a person.

Handwriting Movement Abnormalities in Older Adults with Silent Cerebral Small Vessel Disease-A Preliminary Study.

BACKGROUND: The features of cerebral small vessel disease (CSVD) range from occurrence of asymptomatic radiological markers to symptomatic characteristics that include cognitive deficits and gait decline. The aim of the present study was to examine whether handwriting movement is abnormal in older people with CSVD through handwriting and drawing tasks using digitized handwriting kinematic assessment technology. METHODS: Older subjects (n = 60) were grouped according to Fazekas score, with 16 in the Severe CSVD group, 12 in the Non-severe group and 32 in the Healthy group. Kinematic data were recorded and analyzed during handwriting and drawing tasks: signature; writing of Chinese characters ("" and ""); and Archimedes' spiral drawing. RESULTS: The Severe CSVD group showed lower velocity and higher tortuosity during signature writing, lower velocity of stroke #4 of "" and vertical size of "" than did the Non-severe and Healthy groups. Both Severe CSVD and Non-severe CSVD subjects displayed higher average normalized jerk than did the Healthy group. Partial correlation analysis adjusting for age, gender, education, and mini-mental state evaluation (MMSE) showed that CSVD burden was positively associated with tortuosity of signature and average normalized jerk of Archimedes' spiral, and was negatively associated with velocity of strokes #3 and #4 of "", as well as vertical size of "". CONCLUSIONS: Older adults with CSVD showed abnormal handwriting movement. And the handwriting abnormalities captured by digitized handwriting analysis were correlated with CSVD severity in users of simplified Chinese characters.

NeuroDiag: Software for Automated Diagnosis of Parkinson's Disease Using Handwriting.

OBJECTIVE: A change in handwriting is an early sign of Parkinson's disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. METHODS: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. RESULTS: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD. CONCLUSION: In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement - This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson's disease using automated handwriting analysis software, NeuroDiag.

LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis.

BACKGROUND AND OBJECTIVES: Dynamic handwriting analysis, due to its noninvasive and readily accessible nature, has emerged as a vital adjunctive method for the early diagnosis of Parkinson's disease (PD). An essential step involves analysing subtle variations in signals to quantify PD dysgraphia. Although previous studies have explored extracting features from the overall signal, they may ignore the potential importance of local signal segments. In this study, we propose a lightweight network architecture to analyse dynamic handwriting signal segments of patients and present visual diagnostic results, providing an efficient diagnostic method. METHODS: To analyse subtle variations in handwriting, we investigate time-dependent patterns in local representation of handwriting signals. Specifically, we segment the handwriting signal into fixed-length sequential segments and design a compact one-dimensional (1D) hybrid network to extract discriminative temporal features for classifying each local segment. Finally, the category of the handwriting signal is fully diagnosed through a majority voting scheme. RESULTS: The proposed method achieves impressive diagnostic performance on the new DraWritePD dataset (with an accuracy of 96.2%, sensitivity of 94.5% and specificity of 97.3%) and the well-established PaHaW dataset (with an accuracy of 90.7%, sensitivity of 94.3% and specificity of 87.5%). Moreover, the network architecture stands out for its excellent lightweight design, occupying a mere 0.084M parameters, with only 0.59M floating-point operations. It also exhibits nearly real-time CPU inference performance, with the inference time for a single handwriting signal ranging from 0.106 to 0.220 s. CONCLUSIONS: We present a series of experiments with extensive analysis, which systematically demonstrate the effectiveness and efficiency of the proposed method in quantifying dysgraphia for a precise diagnosis of PD.

How word semantics and phonology affect handwriting of Alzheimer's patients: A machine learning based analysis.

Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.

Benefits of a Light-Painting Technique for Learning to Write New Characters: A Proof of Concept With Adults.

Learning to write relies on the effective integration of sensory feedback and a transition from motor control based on written tracings to motor control based on expert writing movements. This study aimed to test whether the photographic technique of light painting (LP) would facilitate this transition. To achieve this, we conducted two experiments using different LP setups. LP involves moving a light source in a dark environment while taking a long-exposure photograph. LP entails both a real-time reduction of product-related visual information and a post-trial addition of process-related visual information. In the first experiment, we conducted a pre-test, training, and post-test in which 16 adults wrote four new characters with the non-dominant hand. During the training sessions, participants stood and wrote in a vertical frame (1 × 1.2 m) two characters in the control condition (with a marker on the vertical support) and two characters in the LP condition (with a flashlight in the air). In the test phases, participants were seated at a table and copied the four characters into a square (4 cm * 4 cm) on a fixed sheet of graphics paper. As in-air writing strongly differs from classical handwriting situations, we performed a second LP experiment. The aim was to implement LP training in a condition closer to writing. Sixteen new participants followed the same protocol but sat at a table and wrote in a horizontal square (20 cm * 20 cm). In both experiments, participants who trained with the LP method wrote faster and with less pressure than those trained in the control condition. We also observed an improvement in spatial accuracy in Experiment 2, whatever the training condition. LP seemed to have led participants to focus on the writing process, probably because it modified the nature and timing of the visual information used for writing. LP may be a promising technique for remediating writing difficulties.

The internal structure of handwriting proficiency in beginning writers.

Fluent and automatized handwriting frees cognitive resources for more complex elements of writing (i.e., spelling or text generation) or even math tasks (i.e., operating) and is therefore a central objective in primary school years. Most previous research has focused on the development of handwriting automaticity across the school years and characteristics of handwriting difficulties in advanced writers. However, the relative and absolute predictive power of the different kinematic aspects for typically developing beginning handwriting remains unclear. The purpose of the present study was to investigate whether and to what extent different kinematic aspects contribute to handwriting proficiency in typically developing beginning handwriters. Further, we investigated whether gender, socioeconomic background, or interindividual differences in executive functions and visuomotor integration contribute to children's acquisition of handwriting. Therefore, 853 first-grade children aged seven copied words on a digitized tablet and completed cognitive performance tasks. We used a confirmatory factor analysis to investigate how predefined kinematic aspects of handwriting, specifically the number of inversions in velocity (NIV), pen stops, pen lifts, and pressure on the paper, are linked to an underlying handwriting factor. NIV, pen stops, and pen lifts showed the highest factor loadings and therefore appear to best explain handwriting proficiency in beginning writers. Handwriting proficiency was superior in girls than boys but, surprisingly, did not differ between children from low versus high socioeconomic backgrounds. Handwriting proficiency was related to working memory but unrelated to inhibition, shifting, and visuomotor integration. Overall, these findings highlight the importance of considering different kinematic aspects in children who have not yet automatized pen movements. Results are also important from an applied perspective, as the early detection of handwriting difficulties has not yet received much research attention, although it is the base for tailoring early interventions for children at risk for handwriting difficulties.

Kindergarten Visual-Perceptual and Motor Skills and Behavioral Traits Predict First-Grade Chinese Handwriting Legibility and Speed.

IMPORTANCE: Clarifying the relationship between kindergarteners' characteristics and their future handwriting performance is beneficial for the early detection of children at risk of handwriting difficulties. OBJECTIVE: To determine which visual-perceptual and motor skills and behavioral traits significantly predict kindergartners' Chinese handwriting legibility and speed in the first grade. DESIGN: One-year longitudinal, observational design. SETTING: Kindergarten and elementary schools. PARTICIPANTS: One hundred six kindergarten children (53 boys and 53 girls; ages 5 or 6 yr) were recruited. OUTCOMES AND MEASURES: The participants completed two subtests of the Bruininks-Oseretsky Test of Motor Proficiency-Second Edition, Test of Visual Perceptual Skills-Third Edition, Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery™ VMI), and the Attention-Deficit/Hyperactivity Disorder Test-Chinese Version in kindergarten. Their handwriting legibility (character accuracy and construction) and speed were assessed by investigator-developed Chinese handwriting tests in the first grade. RESULTS: Multivariate regression analyses indicated the independent predictive power of spatial relationships (p = .042) and inattention (p = .004) for character accuracy. Visual-motor integration (VMI; p = .008) and inattention (p = .002) were the key predictors of character construction. Manual dexterity (p = .001) was the only significant predictor of writing speed. CONCLUSIONS AND RELEVANCE: Kindergarteners who perform poorly in spatial relationships, VMI, manual dexterity, and attention are likely to have less legible Chinese handwriting and slow writing speed in first grade. Plain-Language Summary: Children's visual-perceptual and motor skills and behavioral traits in kindergarten can predict their Chinese handwriting legibility and speed in first grade. This study found that kindergarteners who performed poorly in spatial relationships, VMI, manual dexterity, and attention were likely to have less legible Chinese handwriting and slow writing speed in the first grade.

Intensive training programme improves handwriting in a community cohort of people with Parkinson's disease.

BACKGROUND: People with Parkinson's disease (PwP) often report problems with their handwriting before they receive a formal diagnosis. Many PwP suffer from deteriorating handwriting throughout their illness, which has detrimental effects on many aspects of their quality of life. AIMS: To assess a 6-week online training programme aimed at improving handwriting of PwP. METHODS: Handwriting samples from a community-based cohort of PwP (n = 48) were analysed using systematic detection of writing problems (SOS-PD) by two independent raters, before and after a 6-week remotely monitored physiotherapy-led training programme. Inter-rater variability on multiple measures of handwriting quality was analysed. The handwriting data was analysed using pre-/post-design in the same individuals. Multiple aspects of the handwriting samples were assessed, including writing fluency, transitions between letters, regularity in letter size, word spacing, and straightness of lines. RESULTS: Analysis of inter-rater reliability showed high agreement for total handwriting scores and letter size, as well as speed and legibility scores, whereas there were mixed levels of inter-rater reliability for other handwriting measures. Overall handwriting quality (p = 0.001) and legibility (p = 0.009) significantly improved, while letter size (p = 0.012), fluency (p = 0.001), regularity of letter size (p = 0.009), and straightness of lines (p = 0.036) were also enhanced. CONCLUSIONS: The results of this study show that this 6-week intensive remotely-monitored physiotherapy-led handwriting programme improved handwriting in PwP. This is the first study of its kind to use this tool remotely, and it demonstrated that the SOS-PD is reliable for measuring handwriting in PwP.

Effects of methylphenidate and physiotherapeutic treatment on graphomotor movements in children with ADHD.

In addition to the core symptoms defining ADHD, affected children often experience motor problems; in particular, graphomotor movements including handwriting are affected. However, in clinical settings, there is little emphasis on standardized and objective diagnosing and treatment of those difficulties. The present study investigated for the first time the effects of methylphenidate as well as physiotherapeutic treatment on objectively assessed graphomotor movements compared to a control condition, i.e. parental psychoeducation, in 58 children (mean age: 9.52 ± 1.91 years) newly diagnosed with ADHD in an outpatient clinic for child and adolescent psychiatry. Families were invited to join one of the treatment groups. Before and after 8 weeks of treatment, children performed six different tasks on a digitizing tablet which allowed the objective analysis of three important kinematic parameters of graphomotor movements (fluency, velocity, and pen pressure) in different levels of visual control and automation. Graphomotor movement fluency and velocity improves over time across the groups, especially in tasks with eyes closed. We did not find clear evidence for beneficial effects of methylphenidate or physiotherapeutic treatment on children's overall graphomotor movements suggesting that treatments need to be better tailored towards specific and individual deficits in graphomotor movements.

Discriminative multimodal learning via conditional priors in generative models.

Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all modalities and class labels are available for model training, e.g. images or handwriting, but where some modalities and labels required for downstream tasks are missing, e.g. text or annotations. We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities. We, to counteract these problems, introduce a novel conditional multi-modal discriminative model that uses an informative prior distribution and optimizes a likelihood-free objective function that maximizes mutual information between joint representations and missing modalities. Extensive experimentation demonstrates the benefits of our proposed model, empirical results show that our model achieves state-of-the-art results in representative problems such as downstream classification, acoustic inversion, and image and annotation generation.

Discriminative Power of Handwriting and Drawing Features in Depression.

This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.