Pen Pressure Research Articles

Computerized assessment of handwriting in de novo Parkinson's disease: A kinematic study

IntroductionDysgraphia, a recognized PD motor symptom, lacks effective clinical assessment. Current evaluation relies on motor assessment scales.Computational methods introduced over the past decade offer an objective dysgraphia assessment, considering size, duration, speed, and handwriting fluency. Objective evaluation of dysgraphia may be of help for early diagnosis of PD. ObjectiveComputerized assessment of dysgraphia in de novo PD patients and its correlation with clinical scales. MethodsWe evaluated 38 recently diagnosed, premedication PD patients and age-matched controls without neurological disorders. Participants wrote “La casa de Pamplona es bonita” three times on paper and once on a Wacom tablet under the paper, totaling four phrases. Writing segments of 5–10 s were analyzed. The Wacom tablet captured kinematic data, including mean velocity, mean acceleration, and pen pressure. Data were saved in.svc format and analyzed using specialized software developed by Tecnocampus Mataró. Standard clinical practice data, Hoehn & Yahr staging, and UPDRS scales were used for evaluation. ResultsSignificant kinematic differences existed; patients had lower mean speed (27 ± 12 vs. 48 ± 18, p < 0.0001) and mean acceleration (7.2 ± 3.9 vs. 15.01 ± 7, p < 0.0001) than controls. Mean speed and mean acceleration correlated significantly with UPDRS III scores (speed: r = −0.52, p < 0.0007; acceleration: r = 0.60, p < 0.0001), indicating kinematic parameters' potential in PD evaluation. ConclusionsDysgraphia is identifiable in PD patients, even de novo, indicating an early symptom and correlates with clinical scales, offering potential for objective PD patient evaluation.

Measurement of Pen Pressure of Offline Signatures Using 3D Digital Microscopy and Its Utility in Determining Authorship

Measurement of Pen Pressure of Offline Signatures Using 3D Digital Microscopy and Its Utility in Determining Authorship

Review on handwriting examination on unusual surface

Individuals have a unique handwriting style, which can aid investigators in identifying suspects and perpetrators at a crime scene. A handwriting analysis can also provide information about the crime's circumstances, including its social and physical surroundings. Handwriting investigations use a variety of analytical factors, including pen pressure, line quality, alignment, and the spacing between words and letters, to determine the mode, conditions, and surroundings of the crime. However, the use of atypical surfaces at crime scenes for handwriting evidence may pose a problem to detectives as they may affect the normal handwriting of the victim, requiring more complex analytical instruments and lengthy investigations. This paper analyzes the success rate of forensic investigations and conclusions linked to handwriting analysis, including the impact of various writing implements on non-traditional and unique surfaces.

Data Testing in Digital Transmission: Tamil Literature

The developing area of Optical Character Recognition (OCR) is digital handwriting recognition. Manual writing is replaced by a digital writing pad. The font and shape of the letters vary while writing digitally. The writer’s digital pen pressure and position on the digital pad cause covert text file problems during OCR recognition. When converting OCR to text, an error occurs because of the variations in letter shapes. In languages like Tamil, Chinese, Arabic, and Telugu, where the alphabet is made up of bends, curves, and rings, the aforementioned issue occurs. Tamil has more word mistakes in OCR-to-text conversion because the alphabet comprises curves and angles that must be correctly transcribed. The ResNet (Residual Neural Network) Two-Stage Bottleneck Architecture (RTSBA) is suggested in this paper. In order to recognize text written in Tamil on a digital writing pad, this article suggests using ResNet (Residual Neural Network) Two-Stage Bottleneck Architecture (RTSBA). The suggested RTSBA reduces the complexity of the Tamil alphabet recognition problem by using two distinct phases of neural networks. There are fewer inputs and variables in the early stages. Time and computational complexity are minimized in the last phase. A two-channel and two-stream transformer, long short-term memory, Inception-v3, recurrent neural networks, convolutional neural networks, and other conventional algorithms have been compared to the suggested algorithm. The digital writing pad-handwritten and HP lab datasets demonstrate the effectiveness of proposed methods like RTSBA, which yield accuracy rates of 98.7% and 97.1%, respectively.

Is There a Deficit in Product and Process of Handwriting in Children with Attention Deficit Hyperactivity Disorder? A Systematic Review and Recommendations for Future Research.

Handwriting abnormalities in children with attention deficit hyperactivity disorder (ADHD) have sometimes been reported both (i) at the product level (i.e., quality/legibility of the written trace and speed of writing) and (ii) at the process level (i.e., dynamic and kinematic features, such as on-paper and in-air durations, pen pressure and velocity peaks, etc.). Conversely, other works have failed to reveal any differences between ADHD and typically developing children. The question of the presence and nature of handwriting deficits in ADHD remains open and merits an in-depth examination. The aim of this systematic review was, therefore, to identify studies that have investigated the product and/or process of handwriting in children with ADHD compared to typically developing individuals. This review was conducted and reported in accordance with the PRISMA statement. A literature search was carried out using three electronic databases. The methodological quality of the studies was systematically assessed using the Critical Appraisal Skills Program (CASP) criteria. Twenty-one articles were identified. Of these, 17 described handwriting quality/legibility, 12 focused on speed and 14 analyzed the handwriting process. All the studies (100%) with satisfactory methodology procedures reported an impaired product and process in children with ADHD, while 25% evidenced a difference in the speed of production. Most importantly, the studies differed widely in their methodological approaches. Substantial gaps remain, particularly with regard to ascertaining comorbidities, ADHD subtypes and the medical status of the included children. The lack of overall homogeneity in the samples calls for higher quality studies. We conclude with recommendations for further studies.

Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19

Parkinson's disease (PD) is a neurodegenerative disorder causing a decline in dopamine levels, impacting the peripheral nervous system and motor functions. Current detection methods often identify PD at advanced stages. This study addresses early-stage detection using handwriting analysis, specifically exploring the PaHaW dataset for pen pressure and stroke movement data. Evaluating online and offline features, the research employs pre-trained CNN models (VGG 19 and AlexNet) for offline datasets, achieving an overall accuracy of 0.53. For online datasets, velocity, and acceleration features are extracted and classified using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and recurrent neural networks (RNN), with GRU yielding the highest accuracy at 0.57. Notably, the convolution-based model C-Bi-GRU surpasses other architectures with a remarkable 0.75 accuracy, emphasizing its efficacy in early PD detection. These findings underscore the potential of handwriting analysis as a diagnostic tool for PD, contributing valuable insights for further research and development in medical diagnostics.

Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset.

선택적 함묵증 청소년의 자기표현 및 관계 향상을 위한 미술치료 단일사례연구

Objectives The purpose of this study is to investigate the effect of art therapy in the improvement of self-ex-pression and interpersonal relationships in adolescents with selective mutism. Methods The research subject is a third-year middle school male student with selective mutism who meets all five criteria of DSM-5 diagnosis. The art therapy of this study was conducted from November 2019 to February 2020, totaling 19 sessions. The research subject received one session per week, each lasting 45 minutes. However, sessions that were conducted in parallel with test and counseling, and the interview counseling of father were ex-tended to 90 minutes each. The parents, who were going through divorce, received individual counseling, family counseling session was conducted once, the father, mother, and a homeroom teacher were provided counseling as needed after the sessions with the subject. The study used HTP for examining changes in self-expression of the research subject with selective mutism, and KSD for exploring changes in relationships. HTP and KSD were evaluated by comparative analysis of pre- and post-test results. The process of change was qualitatively analyzed based on the artwork created during art activities, session transcripts, and counseling journals for each session. Results Firstly, in the post-test of the HTP, the size of the drawings increased by approximately three times, the positioning and composition became more stable, the shading in the human figures disappeared and the depiction of a smiling expression indicated an improvement in self-expression. Secondly, in the post-test of the KSD, the size of the drawings increased by approximately four times, and the positioning, pen pressure, use of an eraser being more stable and the inner aspect of the face being apparent indicated an improvement in interpersonal relationships. Thirdly, throughout the progression across sessions, the subject exhibited emotional stability and genuine self-expression, leading to positive changes in both self-expression and interpersonal relationships. Conclusions considering the combined results, art therapy had demonstrated significant effects on self-ex-pression and interpersonal relationships in adolescents with selective mutism.

Exploring Gender-Specific Features from Handwriting for Devanagari Script

Handwriting is a very important mode used for communication. It has been in use for a long time for various purposes. Handwriting is found in many criminal cases and its analysis is quite tedious work to do and even time-consuming. But it would be better if the gender of the writer is known; it would be helpful in narrowing the search of the suspects. In this work, we have tried to explore certain features which could be used for the identification of the gender of the writer. Various researches have been done to determine gender from handwriting in various languages. No such work has been done on any of the Indian languages yet. We have selected the Marathi language, the script used for writing is the Devanagari script, which is the script for some other languages used in India. The database was created and the features were extracted manually including line quality, pen pressure, formation of dots, and many more. However, few features are seen in males while some others are in females. The study is still not conclusive and looking for further work on it.

Development of a signature verification model based on a small number of samples

AbstractIn this study, an improved AlexNet and transfer learning architecture was used to construct a signature recognition model based on a small number of samples to verify offline signatures. To enhance the features of signatures, drawing boards were used to extract the pen pressure and brush stroke of these signatures. The signatures of eight individuals were used to build the samples and test the generalizability of the model for signatures of different individuals. Visualization technology was first used to adjust the architecture, activation function, and normalization of the model, improving the accuracy from 77.50 to 96.87%. Subsequently, transfer learning was used to solve the problem of the small number of samples, and the number of channels of the model was changed to improve its feature extraction. After all samples were prepared, 560 samples of the source database and 80 samples of the target database were used for transfer learning, which further improved the accuracy to 97.28%. To verify whether the model can recognize newly collected signatures, several original signatures, simple forged signatures, and skilled forged signatures were tested, revealing recognition rates of 91.25%, 95.63%, and 85.63%, respectively. In addition, the overall precision, recall, and F1 score of the model were 90.68%, 91.25%, and 90.97%, respectively, confirming an improvement in the feature extraction capability by extracting brush stroke and that transfer learning can be used with a small number of samples to create a model with excellent recognition rates and generalizability.

A Comparative Study of Tamil Handwriting to Determine the Sex of an Individual by Analyzing the Class Characteristics

Handwriting analysis is a subset of forensic science that deals with questioned documents. The main objective of handwriting analysis is to analyze, compare and evaluate handwriting characteristics to determine the sex of an individual. The class characteristics that were analyzed in this study are alignment, spacing, slant, line quality, pen pressure, movement, size, and speed of an individual’s handwriting, the primary goal of this study is to determine the sex of an individual through handwriting analysis; thus, the total number of samples 200 in which 100 male and 100 female transitive Tamil handwriting samples were collected in the age group ranges from 20 to 40 on hp A4 sheet without any indentation marks on papers. All individuals are strictly asked to write with Rorito bright max ball pen. With the help of a Scale, Pencil, three folding magnifiers 10x (Micrometer), and a Pro illuminated magnifier 4x the sample was analyzed, and interpreted the class characteristics between males and females. The alignment, spacing, line quality, and speed of males show a significant difference from in females and entered the data in the form coding system. The area of this study is some part of Puducherry. It would be helpful for the document examiner to proceed and take initiative in cases related to the determination of sex in male and female in forensic questioned document examination. By concluding this study, the sex can be identified by analyzing the Tamil handwriting.

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks.

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation-dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant's class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1-score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross-movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross-movement analysis and the artificial generation of larger amounts of data.

Forensic handwriting examination: An updated clinical review

ABSTRACT Handwriting is a unique property of an individual; no two individual has similar handwritings. Forensic handwriting examination is an important part of forensic document examination. Handwriting at crime scene provides evidence or clues about type of crime, nature of crime to the investigating officer and also find out identity of suspect or criminal. Handwriting examination involves the examination of various parameters in the writing which includes handwriting positions, surface used for handwriting, pen pressure, handwriting strokes, slants in writing, letter spacing, line quality, etc., handwriting examination is done based on the principle that no two individuals can write exactly alike. Forensic handwriting analysis is a pattern-based science that examines significant, repeated handwriting traits in questioned writing comparing and evaluating these traits in the known writing. If significant similarities are determined without differences, this may lead to a handwriting recognition. Nowadays, an automated computer-based identification and examination system are developed for forensic document examination which is more feasible to use a computer-assisted system in forensic identification of source issues related with questioned document analysis. Experimental eye-tracking is a technologically novel way of forensic handwriting examination. These studies may have future relevance in developing technology that can be linked to the computer in evaluating handwriting, especially in programming software that evaluates handwriting similar to the way a human examiner evaluates handwriting.

Detection of Abnormal Patterns in Children’s Handwriting by Using an Artificial-Intelligence-Based Method

Using camera-based algorithms to detect abnormal patterns in children’s handwriting has become a promising tool in education and occupational therapy. This study analyzes the performance of a camera- and tablet-based handwriting verification algorithm to detect abnormal patterns in handwriting samples processed from 71 students of different grades. The study results revealed that the algorithm saw abnormal patterns in 20% of the handwriting samples processed, which included practices such as delayed typing speed, excessive pen pressure, irregular slant, and lack of word spacing. In addition, it was observed that the detection accuracy of the algorithm was 95% when comparing the camera data with the abnormal patterns detected, which indicates a high reliability in the results obtained. The highlight of the study was the feedback provided to children and teachers on the camera data and any abnormal patterns detected. This can significantly impact students’ awareness and improvement of writing skills by providing real-time feedback on their writing and allowing them to adjust to correct detected abnormal patterns.

Relationship between Product and Process Characteristics of Handwriting Skills of Children in the Second Grade of Elementary School.

The purpose of this study was to analyze the relationship between the quality and speed of handwriting and the process characteristics of the handwriting of children in the second grade of elementary school considered as a function of age and gender. A sample of 57 children (mean age 7.25 years, SD 0.43) participated in the study. The Concise Assessment Method for Children's Handwriting (BHK) was used to assess the quality and speed of handwriting. The characteristics of the process of handwriting were assessed using MovAlyzeR® software. The handwriting of boys showed a significantly greater number of strokes and slanted more to the right than the handwriting of girls. Handwriting quality and speed significantly correlated with several process characteristics: the number of strokes, reaction time, duration, relative pen-down duration, average pen pressure, vertical size, horizontal size, road length, and average absolute velocity. This research contributes to the construction of normative values in the process characteristics of the handwriting of elementary school children and provides a promising step towards the early identification of difficulties that can lead to dysgraphia, thus preventing later difficulties in handwriting.

Age detection from handwriting using different feature classification models

Digitized handwritten documents have been used for various purposes, including age detection, a crucial area of research in fields like forensic investigation and medical diagnosis. Automated age recognition is deemed to be a difficult task, due to the great degree of similarity and overlap across people’s handwriting. Consequently, the efficiency of the classification system is determined by extracting pertinent features from handwritten documents. This research proposes a set of age-related features suggested by a graphologist to classify handwritten documents into two age groups: youth adult and mature adult. The extracted features are: slant irregularity (SI), pen pressure irregularity (PPI), text lines irregularity (TLI) and the percentage of black and white pixels (PWB). Support Vector Machines (SVM) and Neural Network (NN) classifiers have been used to train, validate and test the proposed approach using two different datasets: the FSHS and the Khatt datasets. When applied to the FSHS dtaset using SVM and NN approaches, the proposed method resulted in a classification accuracy of 71% and 63.5%, respectively. Meanwhile, when applied to the Khatt dataset, our method outperformed state-of-the-art methods with a classification accuracy of 65.2% and 67% utilising SVM and NN classifiers, respectively. These are the best rates available right now in this field.

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.

The impact of in-air features on the diagnosis of developmental dysgraphia

Handwriting problems, also known as dysgraphia, are defined as a disorder or difficulty in producing written language associated with writing mechanics. The occurrence of handwriting problems among elementary students varies from 10 to 34%. With negative impacts on educational performance, handwriting problems cause low self-confidence and disappointment in the students. In this research, a pen-tablet was employed to sample children’s handwriting, which revealed online features of handwriting such as kinematic and temporal features as well as wrist and hand angles and pen pressure on the surface. This digitizer could also extract the online handwriting features when the pen was not in contact with the surface. Such features are called in-air features. The purpose of this study was to propose a method for diagnosing dysgraphia along with an evaluation of the impact of in-air features on the diagnosis of this disorder. A rich dataset (OHF-1) of online handwriting features of dysgraphic and non-dysgraphic students was prepared. After the extraction of a huge set of features and choosing a feature selection method, three machine learning methods, i.e. SVM, Random Forest and AdaBoost were compared and with the SVM method, an accuracy of 85.7% in diagnosing dysgraphia was achieved, when both in-air and on-surface features were included. However, while using purely in-air data or merely on-surface features, accuracies of 80.9% and 71.4% were achieved, respectively. Our findings showed that in-air features had a significant amount of information related to the diagnosis of dysgraphia. Consequently, they might serve as a significant part of the dysgraphia diagnosis.

Controlled mechano-luminescence properties of SrGa2O4:Tb3+ co-doping with Dy3+ and Eu3+ ions.

Trap-controlled mechano-luminescence (ML) featuring photon emission under mechanical stimuli provides promising applications such as dynamic imaging of force, integrated optical sensing, information storage, and anti-counterfeiting encryption. However, the corresponding emission with a single color still limits the application of ML materials. Here, a trap-controlled ML phosphor of SrGa2O4:Tb3+ (SGO:Tb3+) with a green-emission is investigated with an adjustable ML color. The relationship between ML and thermoluminescence (TL) is verified by co-doping with Dy3+ and Eu3+ ions for the manipulation of the constructed traps. Accordingly, the as-explored ML phosphor with multicolor output is employed to create encrypted anticounterfeiting patterns, which produces bright and spatially resolvable optical codes under the single-point dynamic pressure of a ballpoint pen. Hence, it provides a new approach to achieve ML with multicolor and gives us an insight into understanding the mechanism of the ML procedure.

Tamil OCR Conversion from Digital Writing Pad Recognition Accuracy Improves through Modified Deep Learning Architectures

Digital handwritten recognition is an emerging field in optical character recognition (OCR). A digital writing pad replaces manual writing. In digital writing, the alphabet changes in font and shape. During OCR recognition, covert text file errors occur due to digital pen pressure and digital pen position on the digital pad by the writer. The shape changes in the alphabet lead to an error during the conversion of OCR to text. The above problem arises in Tamil, Chinese, Arabic, and Telugu, where the alphabet consists of bends, curves, and rings. OCR‐to‐text conversion for the Tamil language has more word errors due to angles and curves in the alphabet, which need to be converted accurately. This paper proposes ResNet two‐stage bottleneck architecture (RTSBA) for Tamil language‐based text recognition written on a digital writing pad. In the proposed RTSBA, two separate stages of neural networks reduce the complexity of the Tamil alphabet recognition problem. In the initial stage, the number of inputs and variables is reduced. In the final stage, time and computation complexity are reduced. The proposed algorithm has been compared with traditional algorithms such as long short‐term memory, Inception‐v3, recurrent neural networks, convolutional neural networks, and a two‐channel and two‐stream transformer. Proposed methods, such as RTSBA applied in the digital writing pad‐handwritten and HP lab datasets, achieved an accuracy of 98.7% and 97.1%, respectively.