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.

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.

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.

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.

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%.

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.

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.

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.

Reliability of ordinal outcomes in forensic black-box studies.

Forensic science disciplines such as latent print examination, bullet and cartridge case comparisons, and shoeprint analysis, involve subjective decisions by forensic experts throughout the examination process. Most of the decisions involve ordinal categories. Examples include a three-category outcome for latent print comparisons (exclusion, inconclusive, identification) and a seven-category outcome for footwear comparisons (exclusion, indications of non-association, inconclusive, limited association of class characteristics, association of class characteristics, high degree of association, identification). As the results of the forensic examinations of evidence can heavily influence the outcomes of court proceedings, it is important to assess the reliability and accuracy of the underlying decisions. "Black box" studies are the most common approach for assessing the reliability and accuracy of subjective decisions. In these studies, researchers produce evidence samples consisting of a sample of questioned source and a sample of known source where the ground truth (same source or different source) is known. Examiners provide assessments for selected samples using the same approach they would use in actual casework. These studies often have two phases; the first phase comprises of decisions on samples of varying complexities by different examiners, and the second phase involves repeated decisions by the same examiner on a (usually) small subset of samples that were encountered by examiners in the first phase. We provide a statistical method to analyze ordinal decisions from black-box trials with the objective of obtaining inferences for the reliability of these decisions and quantifying the variation in decisions attributable to the examiners, the samples, and statistical interaction effects between examiners and samples. We present simulation studies to judge the performance of the model on data with known parameter values and apply the model to data from a handwritten signature complexity study, a latent fingerprint examination black-box study, and a handwriting comparisons black-box study.

Functional Assessment of Handwriting Among Children: A Systematic Review of the Psychometric Properties.

IMPORTANCE: Handwriting legibility and speed assessments have a critical role in identifying and evaluating handwriting problems, which are common among children. OBJECTIVE: The objective was to evaluate the psychometric properties and clinical utility of handwriting assessments for children ages 3 to 16 yr. DATA SOURCES: A systematic review was conducted in CINAHL, PubMed (MEDLINE), Scopus, and education databases, with no time limits. The search strategy included a combination of the following keywords: handwriting, write, children, assessment, and validity. The exclusion criteria were assessment tools that were electronic, that focused on cognitive components of handwriting, or that only evaluated alphabets other than Latin. STUDY SELECTION AND DATA COLLECTION: The systematic review was carried out on the basis of the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. The protocol was registered in the Prospective Register of Systematic Reviews (PROSPERO). FINDINGS: The 14 included instruments had a total sample of 4,987 children. Internal consistency ranged from moderate (.73; Writing Readiness Inventory Tool in Context) to high (.98; Letter Writing). The interexaminer reliability values of the 11 direct assessment instruments ranged from .77 (Systematic Screening for Handwriting Difficulties) to 1.00 (Handwriting Speed Test). CONCLUSIONS AND RELEVANCE: In this systematic review, existing tools were evaluated by clinical utility and the quality of psychometric properties. Direct assessments showed good psychometric properties. Indirect and self-assessment tools demonstrated poor psychometric properties. Further research on screening tools and tools in other languages is needed. What This Article Adds: Specific learning disorders (e.g., dysgraphia) negatively affect academic learning and, when prolonged in time, self-concept. However, handwriting legibility and speed assessments could be used to identify and evaluate these learning disorders if an early referral to occupational therapy is carried out.

Chinese calligraphic handwriting practice promotes positive affect in adolescents: Converging evidence from correlational and experimental designs.

Chinese calligraphic handwriting (CCH) practice has proven to be beneficial to several aspects of psychological health and has been considered a complementary therapy for mental disorders. However, less is known about whether CCH practice benefits an individual's positive affect, a core component of affective well-being. Therefore, we examined this question in Chinese adolescents by combining correlational and experimental methods. In Study 1 (N = 1460), we found that high school students who practiced CCH had higher positive affect in their daily lives than those who did not practice CCH. In Study 2, a training experiment was conducted with college students, who were randomly assigned to receive either CCH training or no treatment. A six-day short-term CCH training led to a significant benefit in positive affect in the training group (N = 23) relative to the control group (N = 27). Together, our study provides converging evidence that CCH practice can promote positive affect in adolescents, further supporting the promotion of CCH in mental health practice.

Effect Of Texting And Handwriting On Hand-Grip And Key-Pinch Strength Among Female-Collegiate Students: Randomized Controlled Trial.

OBJECTIVE: To evaluate immediate and long-term effect of texting or handwriting on hand-grip and key-pinch strength among healthy female students. Methods: The single-blind, randomised controlled trial was conducted between February and April 2021 after approval from the ethics review committee of the College of Medical Rehabilitation Sciences, Taibah University, Saudi Arabia, and comprised female Physio Therapy students aged 19-23 years who were right-hand dominant and had normal body mass index. The subjects used smartphones and electronic gadgets for >2hrs daily, writing more than 10min/day. They were randomised using sealed envelopes into five groups. Group A practised 10min texting, group B 15min texting, group C 10min writing, group D 15min writing, and group E used the phones only for talking or watching, with no texting or writing, and was taken as the control group. Hand-grip strength and key- pinch strength were assessed one minute before starting, and within one minute after having finished the assigned hand activity. All measurements were recorded in the sitting position using a single hand-grip dynamometer. Data was analysed using SPSS 23. RESULTS: Of the 65 individuals assessed, 60(92.3%) were initially enrolled, but the study was finished by 50(83.3%) subjects with a mean age of 20.88±0.98 years and mean body mass index 20.8±2.30kg/m2. There were 12(24%) subjects in group A, 7(14%) in group B, 12(24%) in group C, 10(20%) in group D and 9(18%) in group E. The association of the time-based groups with hand-grip and key-pinch strength was not significant (p>0.05). CONCLUSIONS: Texting and handwriting did not have any significant immediate effect on hand- grip or key-pinch strength. Clinical Trial Number: (NCT04810416).

Handwriting Changes in Alzheimer's Disease: A Systematic Review.

BACKGROUND: Handwriting is a complex process involving fine motor skills, kinesthetic components, and several cognitive domains, often impaired by Alzheimer's disease (AD). OBJECTIVE: Provide a systematic review of handwriting changes in AD, highlighting the effects on motor, visuospatial and linguistic features, and to identify new research topics. METHODS: A search was conducted on PubMed, Scopus, and Web of Science to identify studies on AD and handwriting. The review followed PRISMA norms and analyzed 91 articles after screening and final selection. RESULTS: Handwriting is impaired at all levels of the motor-cognitive hierarchy in AD, particularly in text, with higher preservation of signatures. Visuospatial and linguistic features were more affected. Established findings for motor features included higher variability in AD signatures, higher in-air/on-surface time ratio and longer duration in text, longer start time/reaction time, and lower fluency. There were conflicting findings for pressure and velocity in motor features, as well as size, legibility, and pen lifts in general features. For linguistic features, findings were contradictory for error patterns, as well as the association between agraphia and severity of cognitive deficits. CONCLUSIONS: Further re-evaluation studies are needed to clarify the divergent results on motor, general, and linguistic features. There is also a lack of research on the influence of AD on signatures and the effect of AD variants on handwriting. Such research would have an impact on clinical management (e.g., for early detection and patient follow-up using handwriting tasks), or forensic examination aimed at signatory identification.

Sentence memory recall in adolescents: Effects of motor enactment, keyboarding, and handwriting during encoding.

BACKGROUND: Prior research has shown that memory for action sentences is stronger when stimuli are enacted during encoding than simply listened to: the so-called enactment effect. The goal of the present study was to explore how writing during encoding-through handwriting and through keyboarding-fares compared with enacting, in supporting memory recall. METHODS: One hundred Norwegian high school students (64 girls, 36 boys) aged 16-21 years (M = 17.1) participated in the study. Four lists of verb-noun sentences with 12 sentences in each list were presented in four encoding conditions: (i) motor enactment, (ii) verbal listening, (iii) handwriting, and (iv) keyboarding. RESULTS: Results revealed a significant main effect of encoding condition, with the best memory gained in the enactment condition. Regarding writing, results showed that handwriting and keyboarding during encoding produced the lowest recall in comparison with the enactment and verbal listening conditions. CONCLUSION: These results thus provide additional support for the enactment effect. While there has been much discussion on the relative benefits of handwriting versus keyboarding on student performance, both seemed to be equally poor strategies for the particular learning task explored here, potentially through increased cognitive load.

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination.

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults' handwriting, children's handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children's handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children's handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.

Handwriting legibility and fluency and their patterns of concurrent relations with spelling, graphomotor, and selective attention skills.

Recent research suggests that handwriting comprises two separate subskills: legibility and fluency. It remains unclear, however, how these subskills differ in their relationship to other abilities associated with handwriting, including spelling, graphomotor, and selective attention skills. In this study, we sought to examine the extent and nature of concurrent relationships that may exist among these skills. Children in Year 3 (n = 293), Year 4 (n = 291), and Year 5 (n = 283) completed a large, group-administered battery to assess each of the above skills. Using multigroup structural equation modeling, we found that spelling, graphomotor, and selective attention skills together explained a moderate amount of variance in handwriting legibility (R2 = .37-.42) and fluency (R2 = .41-.58) and that these subskills differed in their concurrent relations. Graphomotor skills accounted for a relatively greater proportion of variance in legibility than did spelling. Conversely, there were relatively stronger contributions from variations in spelling ability to variations in fluency than from graphomotor skills. Furthermore, selective attention predicted handwriting fluency only, and it partially mediated the influence of graphomotor skills. This study further demonstrates that handwriting legibility and fluency are separable and complex skills, each differentially related to spelling, graphomotor, and attentional abilities even during later primary school years.