Many tasks in forensic examination of handwritten documents require classification of writing instruments that have ink of similar properties as the ink found on a questioned document. In this paper, we propose a new methodology for non-destructive identification of inks based on optical properties and reflectance spectra of the ink, measured from handwriting strokes. Building on this methodology, we developed an interactive database that we call the "Pen Ink Library", which lists 718 various writing instruments and enables systematic comparison and semi-automatic search of writing instruments, using the measured characteristics of their ink. To highlight the significance and applicability of the database, we additionally exploit the large amounts of collected measurements to design computer-based data analysis methods for classification and comparative analysis of ink samples. For validation of the semi-automatic search functionality of the Pen Ink Library we performed a series of blind tests using twenty randomly selected writing instruments. Here, an instrument with the same brand and model was found in nine cases, and an instrument with a different brand and model, but with identical spectrum and optical parameters, was found in five cases. Cross-validation of the computer-based data analysis methods on the measurements from the database yielded above 90% accuracy of the classification method and 5.3% to 12.7% error rate of the comparative analysis method.
- Publication type
- Journal Article MeSH
IMPORTANCE: Approximately 7% to 30% of children contend with handwriting issues (HIs) in their school years. However, research studies to define and quantify HIs, as well as practical assessment tools, are lacking. OBJECTIVE: To confirm the validity and reliability of two screening scales for HIs: the Handwriting Legibility Scale (HLS) and the Concise Assessment Scale of Children's Handwriting (BHK). DESIGN: Structural equation modeling (SEM) and confirmatory factor analysis (CFA) of five different models were used to examine the construct and discriminant validity of both scales. Furthermore, internal consistency and interrater agreement were evaluated. The association among scales, grades, and children's self-evaluation was also explored. SETTING: Elementary schools and state counseling centers in the Czech Republic. PARTICIPANTS: On a voluntary basis, 161 children from elementary schools and state counseling centers in the Czech Republic were enrolled. The variable of children with typical handwriting development versus HIs was missing for 11 children. Thus, for discriminant validity analysis, 150 data records from children were used. OUTCOMES AND MEASURES: The HLS and BHK were used to evaluate the handwriting quality of the transcription task. The Handwriting Proficiency Screening Questionnaires for Children was used for children's self-evaluation. RESULTS: The study confirmed the validity and reliability of the shortened BHK and HLS. A strong relationship was found between the BHK and HLS, grades, and children's self-evaluation. CONCLUSIONS AND RELEVANCE: Both scales are recommended for occupational therapy practice worldwide. Further research should focus on developing standards and providing sensitivity studies. What This Article Adds: Both the HLS and the BHK are recommended for occupational therapy practice. Practitioners should also take the child's well-being into consideration in handwriting quality assessment.
- MeSH
- Child MeSH
- Occupational Therapy * MeSH
- Humans MeSH
- Handwriting * MeSH
- Psychometrics MeSH
- Reproducibility of Results MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic MeSH
Úvod: Tužkový úchop se považuje za jeden z ukazatelů vyspělosti jedince v rámci grafomotoriky. Úchop tužky a dovednost jejího používání se vyvíjí postupně, v průběhu zrání centrálního nervového systému, a je spojen s kognitivními funkcemi, motivací a učením. Zpočátku dítě používá hrubý úchop, ale s přibývajícím věkem a tréninkem tužkový úchop vyzrává do některé ze svých základních podob. Cíl: Analyzovat aktuálně používané typy tužkových úchopů u vysokoškolských studentů. Metodika: Pomocí videografie byly zjišťovány typické způsoby úchopu psacího náčiní v závislosti na rychlosti psaní u souboru 100 studentů Univerzity Palackého v Olomouci ve věku 19–25 let. Výsledky: Bylo zjištěno, že čeští vysokoškolští studenti používají v současnosti nejčastěji modifikované špetkové úchopy – tříprstou přitaženou špetku a tříprstou otevřenou špetku. K psaní nejčastěji používají kuličkové psací pomůcky. Některé signifikantní rozdíly v úchopech byly zaznamenány při změně rychlosti psaní.
Introduction: A pencil grip is considered as one of the indicators of graphomotoric maturity of an individual. The pencil grip and skills for its use develop gradually during the neuromaturation of the central nervous system as it is connected with cognitive functions, motivation and learning. A child uses initially a palmar grip only, but with age it evolves into one of its final forms. Aim: To analyse currently used types of pencil grips in university students. Methods: Videography was used to monitor the commonly used types of pencil grips depending on the writing speed in the cohort of 100 students of Palacký University in Olomouc aged 19–25 years. Results: The findings show that modifications of digital grips, modified dynamic tripod and open web space tripod are currently the most common pencil grips used in the Czech university students. The most frequently used writing tool is a roller pen. Several significant changes in a pencil grip occurred with changing the speed of handwriting. Conclusion: Currently the most frequently used types of pencil grips in Czech university students are modified tripod digital grips.
Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model. Several regularization methods have been introduced to prevent the overfitting problem. In the research presented in this manuscript, the overfitting challenge was tackled by selecting a proper value for the regularization parameter dropout by utilizing a swarm intelligence approach. Notwithstanding that the swarm algorithms have already been successfully applied to this domain, according to the available literature survey, their potential is still not fully investigated. Finding the optimal value of dropout is a challenging and time-consuming task if it is performed manually. Therefore, this research proposes an automated framework based on the hybridized sine cosine algorithm for tackling this major deep learning issue. The first experiment was conducted over four benchmark datasets: MNIST, CIFAR10, Semeion, and UPS, while the second experiment was performed on the brain tumor magnetic resonance imaging classification task. The obtained experimental results are compared to those generated by several similar approaches. The overall experimental results indicate that the proposed method outperforms other state-of-the-art methods included in the comparative analysis in terms of classification error and accuracy.
- MeSH
- Algorithms MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Brain Neoplasms * MeSH
- Neural Networks, Computer * MeSH
- Handwriting MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Along with the growing popularity of electronic documents authorised with digitally captured signatures, such evidence has appeared in the work of forensic practitioners. Many different vendors offer signature pads with varying specifications. It is therefore expected that forensic handwriting experts will be called upon to compare questioned and known samples captured with completely or partially different hardware and software combinations. Such cases may be challenging as numerical handwriting data produced by various equipment may differ not only in the type of information captured and its quality, but also in its structure and coding. In this research, numerical data of handwriting - i.e. spatial coordinates, force, and time values - were acquired with 26 different combinations of hardware and software to study characteristics of their coding. The analysis of samples revealed that scaling of numerical data is not only hardware but also software dependent. Therefore, their compliance with the ISO/IEC 19794-7 standard is recommended to improve the data interoperability. This standard emphasizes the importance of supplementing numerical signature data with scaling ratios of the used signing solution. The paper also includes descriptions of several phenomena observed in the acquired data to highlight possible pitfalls in performing inter-solution comparisons in casework.
- Publication type
- Journal Article MeSH
Dysgraphia (D) is a complex specific learning disorder with a prevalence of up to 30%, which is linked with handwriting issues. The factors recognized for assessing these issues are legibility and performance time. Two questionnaires, the Handwriting Proficiency Screening Questionnaire (HPSQ) for teachers and its modification for children (HPSQ-C), were established as quick and valid screening tools along with a third factor - emotional and physical well-being. Until now, in the Czechia, there has been no validated screening tool for D diagnosis. A study was conducted on a set of 294 children from 3rd and 4th year of primary school (132 girls/162 boys; Mage 8.96 ± 0.73) and 21 teachers who spent most of their time with them. Confirmatory factor analysis based on the theoretical background showed poor fit for HPSQ [χ2(32) = 115.07, p < 0.001; comparative fit index (CFI) = 0.95; Tucker-Lewis index (TLI) = 0.93; root mean square error of approximation (RMSEA) = 0.09; standard root mean square residual (SRMR) = 0.05] and excellent fit for HPSQ-C [χ2(32) = 31.12, p = 0.51; CFI = 1.0; TLI = 1.0; RMSEA = 0.0; SRMR = 0.04]. For the HPSQ-C models, there were no differences between boys and girls [Δχ2(7) = 12.55, p = 0.08]. Values of McDonalds's ω indicate excellent (HPSQ, ω = 0.9) and acceptable (HPSQ-C, ω = 0.7) reliability. Boys were assessed as worse writers than girls based on the results of both questionnaires. The grades positively correlate with the total scores of both HPSQ (r = 0.54, p < 0.01) and HPSQ-C (r = 0.28, p < 0.01). Based on the results, for the assessment of handwriting difficulties experienced by Czech children, we recommend using the HPSQ-C questionnaire for research purposes.
- Publication type
- Journal Article MeSH
The purpose of the study was to determine if a generalized motor program (GMP) exists for writing, as has been previously reported. Beginning with a 1942 experiment by Lashley, and continuing with a 1976 (Raibert) example, writers of some motor learning texts have asserted that one can write with different effectors (nonpreferred hand, mouth, foot, etc.) and the results are quite similar, thus demonstrating that writing is a generalized motor program. The task has not been reported in recent literature. In order to determine if the results reported were generalizable, the researchers recruited 31 individuals who volunteered to write a short sentence under five conditions: 1) preferred hand, 2) preferred hand with wrist stabilized, 3) non-preferred hand, 4) mouth, and 5) foot. Participants ranged in age from 19 to 75 and were grouped as follows: < 25 yrs, n = 15; 25–44 yrs, n = 6; > 44, n = 10. Although all of the samples were legible in Conditions 1 and 2, legibility deteriorated significantly in Conditions 4 and 5. Contrary to expectations, there were no significant differences between the samples produced by based on age groupings. The authors concluded that most adults cannot write legibly with their mouths or feet, contrary to what has been previously reported.
- Keywords
- motorické programy, čitelnost,
- MeSH
- Adult MeSH
- Functional Laterality MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Motor Skills * MeSH
- Foot MeSH
- Handwriting MeSH
- Writing * history MeSH
- Hand MeSH
- Aged MeSH
- Statistics as Topic MeSH
- Mouth MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Observational Study MeSH
OBJECTIVE: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. METHODS AND MATERIAL: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). RESULTS: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. CONCLUSION: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.
- MeSH
- Biomechanical Phenomena * MeSH
- Diagnosis, Differential MeSH
- Middle Aged MeSH
- Humans MeSH
- Parkinson Disease diagnosis MeSH
- Handwriting * MeSH
- Aged MeSH
- Case-Control Studies MeSH
- Support Vector Machine MeSH
- Pressure MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. METHODS: We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls. RESULTS: By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy. CONCLUSIONS: Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD.
- MeSH
- Algorithms MeSH
- Biomechanical Phenomena MeSH
- Diagnosis, Differential MeSH
- Middle Aged MeSH
- Humans MeSH
- Motor Skills MeSH
- Parkinson Disease diagnosis physiopathology MeSH
- Movement * MeSH
- Handwriting * MeSH
- Reproducibility of Results MeSH
- Hand physiology MeSH
- Aged MeSH
- Case-Control Studies MeSH
- Support Vector Machine MeSH
- Decision Support Systems, Clinical MeSH
- Artificial Intelligence MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Cíl:Cílem naší studie bylo kvantitativně vyhodnotit poruchy písma u pacientů s Parkinsonovou nemoci (PN) ve srovnání s věkově a pohlavím vázanými zdravými kontrolami (ZK) pomocí digitalizačního tabletu. Soubor a metoda: Prospektivně jsme zařadili 40 pacientů s PN (průměrný věk 68,6 ? 11,36 let, délka trvání nemoci 8,02 ? 4,79 let) a 40 věkem a pohlavím vázaných ZK (průměrný věk 62,55 ? 11,22 let). Všichni jedinci byli praváci bez přítomnosti deprese či demence. Každý subjekt podstoupil sedm cvičení pro vyšetření písma a kresbu Archimédovy spirály a elips s pomocí digitalizačního tabletu. Byly hodnoceny rychlostní parametry mikrografie a kresby při pohybu pera po tabletu i nad tabletem. Pro statistickou analýzu dat jsme použili Mann‑Whitneyho U test a Spearmanovy korelace s korekcí na opakovaná měření (Benjamini‑Hochbergova metoda). Výsledky: U PN ve srovnání se ZK jsme při psaní na tabletu zjistili statisticky významné snížení v parametrech: okamžitá rychlost, okamžité zrychlení, okamžitá změna zrychlení v čase. Změny se zvýrazňovaly s délkou psaného segmentu. Ještě významnější byly rozdíly mezi oběma skupinami při hodnocení pohybu pera nad tabletem, tj. před vlastním zahájením psaní, při přípravě na pohyb. Zaznamenali jsme pokles sledovaných hodnot až o 20 % ve srovnání se ZK. Závěr: U pacientů s PN jsme prokázali specifické změny nejen při vlastním psaní, ale i ve fázi přípravy na psaní, které lze kvantifikovat pomocí digitalizačního tabletu. Výsledky studie mohou mít přímý klinický dopad: umožní nám studovat mikrografii jako možný časný klinický marker rozvoje PN.
Aim: The aim of this study was to assess micrographia in patients with Parkinson's disease (PD) as compared to healthy controls (HC) using a digitizing tablet. Methods: We included 40 PD (mean 68.6 ? 11.36 years, duration of illness 8.02 ? 4.79 years) and 40 age- and sex-matched HC (mean 62.55 ? 11. 22 years). All subjects were right-handed, without the presence of depression or dementia. Each subject underwent seven exercises for writing and drawing of Archimedes spiral and ellipses using a digitizing tablet. The speed parameters of micrographia and drawing during the movement of a pen in the air and on the tablet were evaluated. The Mann-Whitney U test, Spearman correlation and Benjamini-Hochbergs method were used for statistical data analysis. Results: A statistically significant reduction in parameters of velocity, acceleration, and jerk was found when comparing both groups during writing. Changes were more pronounced with increased length of the written segment. The differences between the two groups were more pronounced when the in-air movements were assessed, i.e. during movement preparation. The values decreased up to 20% compared to HC. Conclusion: PD-specific changes assessed with a digitizing tablet were demonstrated not only during writing but also during preparation for writing. The results of the study may have a direct clinical impact: further research into its use as a clinical marker of early PD is likely to follow.
- MeSH
- Time MeSH
- Diagnostic Techniques, Neurological * MeSH
- Middle Aged MeSH
- Humans MeSH
- Statistics, Nonparametric MeSH
- Neurobehavioral Manifestations MeSH
- Parkinson Disease * physiopathology MeSH
- Movement physiology MeSH
- Motor Activity physiology MeSH
- Handwriting * MeSH
- Writing MeSH
- Aged MeSH
- Statistics as Topic MeSH
- Muscle Rigidity etiology complications MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
