Contextual bias by Forensic Document Examination trainees: An empirical study from China.

The impact of contextual bias has been repeatedly demonstrated across forensic domains; however, research on this topic in China is scarce. To examine the prevalence of contextual bias in pattern feature-comparison disciplines, we conducted an experiment involving 24 forensic document examination students. The aim was to determine whether knowledge of different contextual information influenced their forensic decision-making. Participants were divided into different context groups and tasked with examining whether questioned signatures with ambiguous features matched reference signatures. The results of independent-samples t-tests for their decision score data in the two context groups exhibited a statistically significant difference (p < 0.05, Cohen's d > 0.8). Moreover, the submitted forensic reports by participants disclosed a biased evaluation of handwriting features. These findings show how contextual information can bias forensic decision-making in handwriting examination. Context management with complementary strategies such as case triage, cognitive training and decision-making transparency must be implemented to minimize bias in handwriting examination.

Comparing the Pre-writing Skills of Diplegic Cerebral Palsy Children to Those of Normal Children.

INTRODUCTION: The pencil grasp and drawing patterns are specific to different age levels. So, if one knows a certain pattern for that particular age, it will guide the intervention plan for children with cerebral palsy (CP). The chances of improvement in diplegic CP are possible with the help of early intervention; therefore, early intervention is only possible if one knows the areas of delay and the age at which the intervention should be started. MATERIAL AND METHODS: It was a cross-sectional, case-control study. A total of 60 children were selected for the study, of which 30 (50%) were normal and 30 (50%) had diplegic cerebral palsy. A convenient sampling method is used for evaluation. RESULTS: The t-value for pencil grasp between the two groups, i.e., normal and CP diplegic, was 3.515 (P=0.001), revealing a significant difference in the grasp pattern of the two groups. Similarly, the t-value for drawing patterns between the two groups, i.e., normal and CP diplegic, was 5.796 (P = 0.001). A significant difference was found in the drawing patterns of both groups. CONCLUSION: Our study found that diplegic CP children performed lower on the Erhardt Developmental Prehension Assessment (EDPA) and showed larger variation in the pencil grasp and drawing than the normal children.

The "t" time: Investigating handedness through strokes and slopes.

This study investigated the stroke and slope characteristics in left-handed and right-handed handwriting. Stroke (letters t, f, d, and H) and slope (letters t, f, l, d, and g) directions were analyzed on in-house samples (n = 64), revealing statistically significant differences (p ≤ 0.05) between the groups. Right-handers predominantly exhibited left-to-right strokes (98%-100%), while left-handers showed greater variability. Although statistically significant for most letters analyzed, slope direction did not demonstrate consistent patterns. A logistic regression model was developed and validated on the same sample to classify handedness based on the averaged strokes of the letter "t." The model was further tested on samples (n = 252) from a publicly available handwriting database. If the model classified the sample as produced by left hand, it was correct in 100% of cases. In contrast, when the model classified writing as right-handed, it was correct in 73%-97% of cases, depending on the validation sample. The model classified writing as of left-handed origin if more than 36% of the letters "t" had a stroke from right to left, while otherwise, writing was classified as of right-handed origin. The developed method showed great potential for classifying the handedness of the author of disputed handwriting, thus eliminating individuals as text authors or narrowing down the pool of potential authors.

MXene-Based Skin-Like Hydrogel Sensor and Machine Learning-Assisted Handwriting Recognition.

Conductive hydrogels are widely used in flexible sensors owing to their adjustable structure, good conductivity, and flexibility. The performance of excellent mechanical properties, high sensitivity, and elastic modulus compatible with human tissues is of great interest in the field of flexible sensors. In this paper, the functional groups of trisodium citrate dihydrate (SC) and MXene form multiple hydrogen bonds in the polymer network to prepare a hydrogel with mechanical properties (Young's modulus (23.5-92 kPa) of similar human tissue (0-100 kPa)), sensitivity (stretched GF is 4.41 and compressed S1 is 5.15 MPa-1), and durability (1000 cycles). The hydrogel is able to sensitively detect deformations caused by strain and stress and can be used in flexible sensors to detect human movement in real time such as fingers, wrists, and walking. In addition, the combination of matrix sensing and machine learning was successfully used for handwriting recognition with an accuracy of 0.9744. The combination of machine learning and flexible sensors shows great potential in areas such as healthcare, information security, and smart homes.

A Pix2Pix Architecture for Complete Offline Handwritten Text Normalization.

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture.

Development of potential dysgraphia handwriting dataset.

This report presents a dataset of offline handwriting samples among Malaysian schoolchildren with potential dysgraphia. The images contained Malay sentences written by primary school students and children under intervention by the Malaysia Dyslexia Association (PDM). Students were expected to copy and write the sentences provided on the paper form that was used to gather data. Students were required to write three sets of sentences. The paper was digitalized by scanning it and converting it into digital form. Furthermore, the images were pre-processed using image processing techniques by converting the images into binary format and interchanging the foreground and background colors. The images were then classified into two categories, namely potential dysgraphia and low potential dysgraphia. The dataset comprised a total of 249 handwriting images, obtained from a sample of 83 participants who were selected in the data collection process, with 114 for potential dysgraphia and 135 for low potential dysgraphia. Both categories of handwriting images were prepared in black and white images.

Evaluating handwriting in children with developmental coordination disorder (DCD): Temporal, spatial, pressure and grip-force measures.

BACKGROUND: Writing involves complex sensorimotor and biomechanical processes that regulate pressure on the writing surface. Researchers analyze writing to understand kinetics and kinematics by evaluating temporal, spatial, and pressure aspects, yet discerning writing surface pressure and pen-grip force remains challenging. AIMS: To compare handwriting kinetics (pen grip-force and surface pressure) and kinematics (temporal-spatial) of children with developmental coordination disorder (DCD) with those of typically developing (TD) children. METHODS AND PROCEDURES: Twenty-seven children with DCD aged 7-12 years and 27 TD children matched by age and gender copied a 29-word passage onto a computerized tablet. Temporal, spatial and surface pressure as well as pen grip-force were measured with a tablet and a wearable device respectively. OUTCOMES AND RESULTS: The DCD group displayed significantly longer total writing time, mean letter time, and greater letter height, width, variance, spacing, area, and erasures than the TD group. Although there were no significant between-group differences in the surface pressure or maintaining pressure, the DCD group displayed weaker grip-force, p = .01, with greater variance. CONCLUSIONS AND IMPLICATIONS: The DCD group's weaker grip-force dynamics correlated with reduced legibility, form, and prolonged writing duration, revealing insights into handwriting mechanisms, particularly grip force, crucial for effective clinical interventions.

Cognitive strategies utilised by early learners when writing alphabet-letters from memory.

INTRODUCTION: Occupational therapy handwriting studies have shown cognitive instructional techniques are effective methods to assist beginning or challenged handwriters. However, information about how children's cognitive (thinking) skills impact their handwriting development is scarce. This study examined the cognitive strategy efficiency of early learners while writing the alphabet-letters from memory. METHODS: Participants included 408 mainstream children in their first two years of formal schooling (Year 1 and Year 2). Children were asked to write the 26-lowercase alphabet-letters from memory. Data were collected using alphabet-letters generated by the children and observation of their cognitive strategy use while writing. The measurement tool employed was the Perceive, Recall, Plan, and Perform (PRPP) System of Task Analysis (Stage Two). RESULTS: The results found that Year 1 and Year 2 children had difficulties applying cognitive strategies across all four PRPP quadrants while writing the lowercase alphabet-letters from memory. Application of the cognitive strategies from the Plan and Recall quadrants showed the most inefficiencies. The lowest PRPP subquadrant scores were observed in Evaluating, Recall Facts, Sensing, and Programming. Except for the Attending subquadrant and the descriptor 'Regulates', Year 2 children were significantly better than Year 1 children at applying cognitive strategies from all other PRPP quadrants and subquadrants. CONCLUSION: The findings provide beginning evidence that efficient use of cognitive strategies from all four PRPP quadrants is important for children achieving proficiency in accurate and automatic alphabet-letter-writing skills. Recommendations for occupational therapy handwriting assessment and intervention are provided. PLAIN LANGUAGE SUMMARY: Research suggests that occupational therapists can best help children with handwriting difficulties by using a cognitive approach. This approach involves instructing children how to form letters correctly, encouraging self-evaluation, and providing opportunities to practice handwriting. However, the cognitive (thinking) skills required for children to write alphabet-letters accurately are not well understood. To investigate this, a study was conducted on 408 mainstream children in their first two years of formal schooling (Year 1 and Year 2). The study examined how accurately the children could write lowercase alphabet-letters from memory and which cognitive skills they used to do so. The cognitive skills investigated were related to attention, perception, recalling, planning, and performing. The assumption tested was that mistakes in correctly writing alphabet-letters could be attributed to difficulties in using these cognitive skills effectively. The study found that Year 1 and Year 2 children made errors in over half of the 26-lowercase alphabet-letters they wrote. The cognitive skills, which scored the lowest were children's ability to evaluate their writing, recall how and where to write letters, gather information from their vision, and think carefully to ensure accuracy while writing. This implied that these thinking skills had the most impact on the children's ability to write alphabet-letters correctly. More research is needed to confirm these findings and determine the reliability of the tool used in this study to measure the children's thinking skills.

Intelligent Human-Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition.

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human-computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.

DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task.

Handwriting difficulty is a defining feature of Chinese developmental dyslexia (DD) due to the complex structure and dense information contained within compound characters. Despite previous attempts to use deep neural network models to extract handwriting features, the temporal property of writing characters in sequential order during dictation tasks has been neglected. By combining transfer learning of convolutional neural network (CNN) and positional encoding with the temporal-sequential encoding of long short-term memory (LSTM) and attention mechanism, we trained and tested the model with handwriting images of 100,000 Chinese characters from 1064 children in Grades 2-6 (DD = 483; Typically Developing [TD] = 581). Using handwriting features only, the best model reached 83.2% accuracy, 79.2% sensitivity, 86.4% specificity, and 91.2% AUC. With grade information, the best model achieved 85.0% classification accuracy, 83.3% sensitivity, 86.4% specificity, and 89.7% AUC. These findings suggest the potential of utilizing machine learning technology to identify children at risk for dyslexia at an early age.

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.

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.

ENFSI 2022 multidisciplinary collaborative exercise: organisation and outcomes.

The use of collaborative exercises (CE) and proficiency tests (PT) as part of the governance programme for any forensic science laboratory has become commonplace and recommended by several international organisations. Traditionally these have been discipline-specific exercises testing a laboratory's ability in a single area of forensic science. However, the "real" world is normally more complex and, in many instances, forensic material must be examined for a number of different evidence types. This article summarises the concepts, planning, design, preparation, implementation, co-ordination and evaluation of the 2022 Multidisciplinary Collaborative Exercise (2022-MdCE) covering a range of forensic disciplines, specifically DNA, fingerprint, documents and handwriting. The exercise consisted of a questioned letter with typescript text and a signature. In addition, the letter contained a visible bloody fingermark in the area of the signature, a visible staining in the lower left-hand corner, a latent fingermark and an indented impression. The analysis of the results showed that, in the investigation of the bloody fingermark, the priority was given to the DNA examination. Some critical issues emerged in relation to the biological (DNA)/ink sampling strategies when applied before fingermark visualisation. Another outcome of the exercise has been to demonstrate the importance of indented impressions, which have been underestimated by a significant number of participants. As setters, more in-depth studies are needed to produce consistent samples. This concerns all the disciplined involved but especially DNA and fingermarks. Based on this exercise, it is believed that this approach to testing of forensic disciplines allows the analysis of good practice within the various scientific areas, as well as scrutinising the process and sequence of events for examining the material within a forensic laboratory in the best conservative way for all kind of evidences.

Motor Adaptation Deficits in Children with Developmental Coordination Disorder and/or Reading Disorder.

Procedural learning has been mainly tested through motor sequence learning tasks in children with neurodevelopmental disorders, especially with isolated Developmental Coordination Disorder (DCD) and Reading Disorder (RD). Studies on motor adaptation are scarcer and more controversial. This study aimed to compare the performance of children with isolated and associated DCD and RD in a graphomotor adaptation task. In total, 23 children with RD, 16 children with DCD, 19 children with DCD-RD, and 21 typically developing (TD) children wrote trigrams both in the conventional (from left to right) and opposite (from right to left) writing directions. The results show that movement speed and accuracy were more impacted by the adaptation condition (opposite writing direction) in children with neurodevelopmental disorders than TD children. Our results also reveal that children with RD have less difficulty adapting their movement than children with DCD. Children with DCD-RD had the most difficulty, and analysis of their performance suggests a cumulative effect of the two neurodevelopmental disorders in motor adaptation.

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

Mismatch between fixed classroom furniture and anthropometric measurements among university students: Relationships to ergonomic risk.

BACKGROUND: Appropriate arrangement of classroom ergonomics is necessary for maintaining health and improving academic performance, learning, and motivation. OBJECTIVE: We aimed to determine the anthropometric measurements and ergonomic risk levels of students during a handwriting activity on a fixed desk and chair and to analyze the mismatch and relationships between these factors. METHODS: This study included 149 university students (female:73, male:76). Anthropometric measurements (lower and upper extremity length, shoulder height, elbow-rest height, hip-popliteal length, popliteal height, knee height, the distance between tragus and wall, and between acromion and wall) were done with a tape measure. The Rapid Upper Limb Assessment (RULA) tool was conducted to determine the ergonomic risk level while students wrote a standard text on a fixed desk and chair as if they were taking notes in the classroom. The mismatch was evaluated between backrest height and sitting shoulder height, seat height, and popliteal height. The Pearson Chi-Square and the Spearman Correlation test were used for statistical analysis. RESULTS: The median values of the participants' age, height, weight, and BMI were 22 years, 1.70 m, 68 kg, and 23.18 kg/cm2, respectively. Most students had inadequate ergonomic posture while writing on fixed furniture at the university (Median RULA grand score: 4). More mismatches for seat height (54.4%) were found in high ergonomic risk levels but mismatches for backrest height did not follow a similar result. CONCLUSIONS: More investigation should be conducted with prospective studies including interventions like adjustable furniture.

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.

Cognitive impairment assessment through handwriting (COGITAT) score: a novel tool that predicts cognitive state from handwriting for forensic and clinical applications.

Introduction: Handwriting deteriorates proportionally to the writer's cognitive state. Such knowledge is of special importance in the case of a contested will, where dementia of the testator is claimed, but medical records are often insufficient to decide what the testator's cognitive state really was. By contrast, if the will is handwritten, handwriting analysis allows us to gauge the testator's cognitive state at the precise moment when he/she was writing the will. However, quantitative methods are needed to precisely evaluate whether the writer's cognitive state was normal or not. We aim to provide a test that quantifies handwriting deterioration to gauge a writer's cognitive state. Methods: We consecutively enrolled patients who came for the evaluation of cognitive impairment at the Outpatient Clinic for Cognitive Impairment of the Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Sciences (DINOGMI) of the University of Genoa, Italy. Additionally, we enrolled their caregivers. We asked them to write a short text by hand, and we administered the Mini Mental State Examination (MMSE). Then, we investigated which handwriting parameters correlated with cognitive state as gauged by the MMSE. Results: Our study found that a single score, which we called the COGnitive Impairment Through hAndwriTing (COGITAT) score, reliably allows us to predict the writer's cognitive state. Conclusion: The COGITAT score may be a valuable tool to gage the cognitive state of the author of a manuscript. This score may be especially useful in contested handwritten wills, where clinical examination of the writer is precluded.

Analysis of spelling errors from the 'dyslexic sight words' list.

In this study the spelling errors of the 'Dyslexic Sight Words - DSW' list are analyzed according to the semiological classification. The spelling errors were made by schoolchildren with and without dyslexia. The high number of inaccuracies observed in the writing of the Group with dyslexia (GD) was often related to the complexity of syllabic structures and orthographic irregularity. The syllabic structures, in addition to the consonant-vowel (CV) pattern, often pose challenges for all students as they move through the alphabetic writing phase, early in literacy. This classification provides an understanding of the characteristics of Natural Spelling and Arbitrary Spelling, providing support for the teaching-learning of words by dyslexic students and is also relevant for the design of Portuguese language teacher training policies. In the teaching of the orthographic norm, the success and error when writing words should be followed by a reflection (metaorthographic skill) and monitoring of learning, both on the part of the teacher and on the part of the learner, reinforcing the knowledge of spelling patterns that will be triggered as the student is exposed to the explicit formal teaching of spelling.

The relevance of motor sequence and visual perception skills in learning Chinese handwriting.

This study explored visual perception skills and the ability to write according to standard stroke order and their links to the learning of Chinese handwriting. Thirty-seven children (aged 6-8) (15 boys and 22 girls) participated in a handwriting test and visual perception evaluation (Test of Visual Perceptual Skills-3rd Edition, TVPS-3). A computerized system was used to evaluate the stroke order accuracy, legibility, and automation of stroke movements. The stroke order accuracy was found to positively correlate with the scores of TVPS-3 (r = .498, p < .05) and to significantly correlate with handwriting legibility (r = .435, p < .05) as well as the automation of stroke movements (r = .494, p < .01). This study revealed that visual perception skill is related to stroke order accuracy and provides directions to assist students who encounter difficulties in learning Chinese handwriting.