The assessment of handwriting is fundamental for identifying difficulties, which may have long-term negative consequences. However, standard evaluation typically focuses only on the final handwritten product. For this reason, Italian guidelines recommended supporting traditional evaluation with digital tools to also analyze the handwriting process. A sensorized ink pen used on paper was employed by over 700 students, ranging from first grade in Italian primary school to third grade in lower secondary school, to execute two tasks of the BVSCO-3, the gold standard for handwriting assessment. From sensorized ink pen data, handwriting indicators in the domains of Time, Force, Smoothness, Tilt, and Frequency were extracted. These indicators were then analyzed to examine their correlation with clinical scores, to model cross-sectional trends across grades, and to identify handwriting difficulties. The correlation analysis revealed significant relationships between the indicators and the clinical score, particularly for the Time domain. A cross-sectional statistical analysis showed that the indicators follow developmental trends compatible with handwriting learning curves reported in the literature: for many indicators, a performance plateau was reached in grade 3, from both motor and processing perspectives. Lastly, binary classification models successfully distinguished subjects with handwriting difficulties (based on BVSCO-3 results) from proficient writers. The sensorized ink pen allowed uncovering relevant characteristics of children's handwriting process, while guaranteeing ecological data acquisition conditions. Its use could pave the way for a prompt identification of handwriting difficulties in school settings, thus facilitating an efficient referral to clinical services.

The growing global elderly population is expected to increase the prevalence of frailty, posing significant challenges to healthcare systems. Frailty, a syndrome associated with ageing, is characterised by progressive health decline, increased vulnerability to stressors and increased risk of mortality. It represents a significant burden on public health and reduces the quality of life of those affected. The lack of a universally accepted method to assess frailty and a standardised definition highlights a critical research gap. Given this lack and the importance of early prevention, this study presents an innovative approach using an instrumented ink pen to ecologically assess handwriting for age group classification. Content-free handwriting data from 80 healthy participants in different age groups (20-40, 41-60, 61-70 and 70+) were analysed. Fourteen gesture- and tremor-related indicators were computed from the raw data and used in five classification tasks. These tasks included discriminating between adjacent and non-adjacent age groups using Catboost and Logistic Regression classifiers. Results indicate exceptional classifier performance, with accuracy ranging from 82.5% to 97.5%, precision from 81.8% to 100%, recall from 75% to 100% and ROC-AUC from 92.2% to 100%. Model interpretability, facilitated by SHAP analysis, revealed age-dependent sensitivity of temporal and tremor-related handwriting features. Importantly, this classification method offers potential for early detection of abnormal signs of ageing in uncontrolled settings such as remote home monitoring, thereby addressing the critical issue of frailty detection and contributing to improved care for older adults.

BackgroundMild cognitive impairment (MCI) is a precursor of dementia. Therefore, MCI identification and monitoring are crucial to delaying dementia onset. Given the limits of existing clinical tests, objective support tools are needed. ObjectiveThis work investigates quantitative handwriting analysis, tailored to enable domestic monitoring, as a noninvasive approach for MCI screening and assessment. MethodsA sensorized ink pen, used on paper and equipped with sensors, memory, and a communication unit, was used for data acquisition. The tasks included writing a grocery list and free text to mimic daily life handwriting, and a clinical dictation test (parole-non-parole [PnP] test), featuring regular, irregular, and made-up words, aimed at assessing MCI dysgraphia. From the recorded data, 106 indicators describing the performance in terms of time, fluency, exerted force, and pen inclination were computed. A total of 57 patients with MCI were recruited, of whom 45 performed a test-retest protocol. The indicators were examined to assess their test-retest reliability. The indicators from the test repetition were used to assess their relationship with the scores of clinical tests via correlation analysis. For the PnP test, differences in the indicators among the 3 types of words were statistically investigated. These analyses were conducted separately for the cursive (2/3 of the sample) and block letters (1/3 of the sample) allographs, with the level of significance set at 5%. Data from healthy older adults were available for the grocery list (34 participants) and free text (45 participants) tasks. These were exploited to build machine learning classification models for the distinction between patients with MCI and healthy controls. ResultsWhen dealing with reliability, 93% and 44% of the indicators were characterized by a significant reliability of at least moderate intensity for cursive and block letters respectively. As for the correlation analysis, patients with preserved cognitive status and daily life functionality were associated with significantly better temporal performances, both in free writing and PnP. The analysis of PnP highlighted the presence of surface dysgraphia in the recruited sample, as irregular words showed significantly worse temporal indicators with respect to regular and made-up ones. The classification models’ built-in free writing data achieved accuracies ranging from 0.80 to 0.93 and F1-scores from 0.81 to 0.92 according to the input dataset. ConclusionsThe presented results suggest the suitability of ecological handwriting analysis for the all-around monitoring of MCI, from early screening to disease progression evaluation.

The screening of specific learning disabilities faces many challenges, such as: 1) the lack of an educational alliance between schools, families, and clinicians; 2) the lack of quantitative data about children's difficulties and their progression over time; or 3) the inefficiency of access to care when it is truly needed. To address these issues, this work presents ESSENCE, a platform aimed at supporting schools throughout the entire process, from identifying children with difficulties to reporting cases to child neuropsychiatrists. Following an iterative co-design process with all relevant stakeholders, several system components were defined, developed, and refined with users' feedback. The final prototype was field-tested over one year by approximately 70 children, their teachers, and some clinicians. Compliance, the System Usability Scale, and custom satisfaction questionnaires were used to evaluate the system. Compliance was high, with at least five sessions conducted in 80% of the weeks. 82% of children reported good usability, and 92% were satisfied with the experience. The quantitative data collected through ESSENCE enhance the process of identifying specific learning disabilities, making it more targeted and beneficial for both children in need and the health-care system.

A prompt diagnosis of specific learning disabilities (SLDs) is prevented by an overwhelmed healthcare system. As teachers lack clinical preparation, school-based screening needs to be improved. This work proposes methods to (1) identify children’s profiles, (2) select children who need a visit, and (3) provide a better understanding of the characteristics connected with the start of the clinical pathway. We analyzed data from 364 children referred to clinical consultation. Starting from a 96-item screening questionnaire filled in by teachers at school, we computed a severity score for 19 different sub-domains of learning leveraging item response theory. Then, we performed cluster analysis with K-means to segment the population according to children’s capabilities. For each cluster, we leveraged leave-one-out on balanced outcomes (clinical pathway VS school training) with different machine learning models, and we leveraged Shapley values to explain the results. Cluster analysis revealed two children’s profiles, grouped by severity. Though, the proportion of children who started the clinical pathway was not statistically different. Indeed, also children with less difficulties should be taken into consideration, as they may suffer from SLDs without comorbidities. As for the classification, median area under the precision-recall curve was 0.96 for one cluster with a Support Vector Classifier (SVC), and 0.69 for the other cluster with Naive Bayes (NB). Between-cluster differences in performance suggest different degrees of complexity in children’s profiles. Yet, also the latter can be considered a good results, considering the heterogeneity of data creators. Shapley values revealed that the SVC on the first cluster tended to rank children by severity, whilst NB on the second cluster shows that the difficulties can interact in a more complex way. This work represents a step forward in the management of SLDs, from an early and preclinical setting.Clinical relevanceThis work provides methods to get insights on the reasons for referring children with specific learning disabilities to the clinic.

Lack of handwriting automatization in childhood can cause difficulties within and outside the school context. Therefore, the objective quantification of the handwriting performance is key. A Smart Ink Pen (SIP) used on paper demonstrated its validity in characterizing primary school children’s handwriting process, although missing information on the handwriting product. To overcome this limitation, a trace reconstruction algorithm was developed, based on the force and IMU signals measured by the SIP. A total of 353 words "uno", written in cursive from the BVSCO-3 battery by 50 Italian students of the 5th grade of primary school, was reconstructed. The quality of the reconstructions was validated through an Optical Character Recognition (OCR) algorithm (Google Vision), using the scans of the actual traces as a reference. The character recognition rates were 81.02% and 59.49%, the character error rates 21.48% and 47.65%, for scans and reconstructions, respectively. A deeper analysis revealed that 15% of the reconstructions were read in the opposite direction by the OCR algorithm, likely due to a non sufficient sampling rate for the last portion of the words. A characterization of the differences between the good, bad and opposite reconstructions allowed to identify some directions of improvement. An increase of the SIP sampling rate, a better modeling of the thickness of the trace, a finer estimation of the relative distance between the IMU sensor and the tip and the reconstruction of the tip trajectory during in-air movements could improve the trace reconstruction algorithm. In addition, the possibility to leverage transfer learning approaches on the OCR algorithm using the reconstructed traces for additional training could further improve the performances in terms of character recognition rate. However, the preliminary results obtained are promising and highlight the possibility of reconstructing handwriting traces while maintaining the naturalness of the gesture.Clinical relevance— This contribution establishes the possibility of reconstructing handwritten traces from the kinematic and force signals recorded by an ecological sensorized ink pen.

Difficulties in mastering handwriting affect 10-30% of pupils, with detrimental effects if untreated. Thus, their early detection is of utter importance. Technology can enrich the standard pen-and-paper assessment with quantitative information on the handwriting process, provided it can describe both its development and impairments in childhood. In this work, 530 pupils from 1 st to 5 th grade performed a subtest from the Italian clinical gold standard for writing fluency assessment. They wrote the sequence of numbers as words, as fast as possible, for one minute in uppercase font. An innovative sensorized ink pen (SIP), able to collect kinematic and dynamic signals while writing on paper, was used for data acquisition. From the data, temporal, fluency, and pressure indicators were computed and subjected to statistical analysis to evaluate handwriting learning curves and sensitivity to difficulties, revealed by the writing fluency test. All domains showed a significant learning effect (p-value < 0.001). A relevant evolution in the handwriting pattern in the first three years emerged, with significant differences between adjacent classes (p-value < 0.001), followed by a plateau. The indicators were also statistically different (p-value < 0.001) between proficient and nonproficient writers. Beginner and nonproficient writers were characterized by reduced speed both with the SIP on paper and in air, jerky movements and scarce modulation of the force exerted on the writing surface. These findings, in line with the existing literature, foster the integration of the SIP in the early screening of handwriting difficulties.

To improve the screening of handwriting disorders in children, a technology-enhanced telemedicine approach is presented. A co-design process resulted into two clinical tests: (1) a dysorthography test, to be performed on tablet, and (2) a dysgraphia test, to be performed on paper with a smart ink pen. Clinicians and children tested the platform to assess usability by means of questionnaires, and reached good-to-excellent results (test 1: median score 90 [85; 97.5] for clinicians and 87.5 [81.9; 93.1] for children; test 2: 77.5 [69.4, 85] for children). Significant correlations between clinical scores and multi-domain indicators from the smart ink pen showed good potential to enrich the tests with information on difficulties, to provide targeted intervention. This suggests the feasibility of the approach for a real-world usage.

The Coma Recovery Scale-revised (CRS-r) is the gold standard for the behavioral assessment of patients with Disorders of Consciousness (DoCs). However, the misdiagnosis rate is around 40%. For this reason, recent guidelines suggested enhancing the assessment with neurophysiological measures: among these, surface electromyography (sEMG) represents a convenient bedside solution. This work presents the use of the STRIVEfc system, a wearable device that allows CRS-r administration while recording four sEMG signals. STRIVEfc was employed in 40 sessions on 33 DoCs patients and the sEMG was analyzed to look for voluntary and consistent over threshold (OT) muscular activities. Their duration, amplitude, and number were retained and compared between patients in Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS), revealing more numerous and significantly longer OTs in the latter group. Lastly, the EMG information was exploited to enrich the behavioral assessment by building the instrumented CRS-r score (ICRS-r). In 9/16 UWS sessions, the ICRS-r score suggested a higher-level functioning, not translated into a behavioral response, compatible with MCS diagnosis. Overall, the use of STRIVEfc allows to reveal hidden muscular patterns not detectable by the clinician, thus improving the characterization of DoCs patient’s functional capabilities and supporting the diagnostic process.

The early detection of Mild Cognitive Impairment (MCI) is fundamental to initiate treatments for delaying the onset of dementia. Currently, the Mini Mental State Examination (MMSE) is one of the most common clinical scales used by geriatricians to assess cognitive function. A deviation of 1 to 3 points from the maximum score (30) is considered as sign of relevant cognitive decline. However, objective and affordable tools are needed to complement the screening process. The quantitative analysis of handwriting represents a suitable solution, as the gesture is significantly impaired in MCI subjects in terms of time, speed, fluency and applied pressure. This works presents the development and testing of classification models able to separate subjects at risk of cognitive decline (MMSE <= 28) from controls (MMSE > 28), starting from free-content handwriting data acquired with a smart ink pen, used on paper, from which 36 indicators were computed. Data were collected in 2 phases. The former involved 45 subjects and served for models training. In the latter, data were acquired from 23 subjects in a domestic longitudinal framework and were partially used for model refinement, but mainly for testing. Three different algorithms were tried (support vector machine, random forest and Catboost) The best test performances on the longitudinal data were obtained by a Catboost classifier, achieving accuracy 93.33%, precision 88.89%, recall 100% and f1 score 94.12%. The results support the use of computerized handwriting analysis as screening tool for cognitive decline detection.

Early screening of handwriting difficulties is key to start remediation activities that help distinguishing between a simple delay and dysgraphia. Technology is fundamental in this process, as also claimed by guidelines for dysgraphia diagnosis: it allows to implement artificial intelligence techniques to help in the discrimination of the difficulty. To this end, a serious game was leveraged to assess handwriting laws altered in dysgraphia starting from symbols drawing. 66 first and second graders were longitudinally tested both with the serious game and with a handwriting proficiency test. Objective features computed from the game were tested to understand if they significantly differed between children at risk and not at risk of dysgraphia, according to a standardized clinical test used to assess handwriting. Then, machine learning models were leveraged to predict the risk and understand the areas of difficulty. On average, 62% of the features significantly differ between risk levels for first graders, whilst only 35% for second graders, thus revealing a better sensitivity in younger children. This is encouraging for an early observation. As for machine learning, a Logistic classifier was able to predict risk with an area under the precision-recall curve of 0.84 for the risk class and 0.98 for the non-risk class. The results of this study could be a valid help for an artificial intelligence-enhanced screening of dysgraphia.

IntroductionSince the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors.MethodsForty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability.ResultsCompared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4–7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important.ConclusionIndicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed.

Handwriting difficulties need to be addressed early to avoid several problems to children, both at school and in everyday life, but dysgraphia diagnosis cannot be performed before handwriting maturation. To solve this issue, we hypothesize that the analysis of drawings produced in a pre-literacy stage can predict handwriting problems that will occur years later. We designed a three-year longitudinal study from the last year of kindergarten to the end of second grade with two aims: (1) to longitudinally assess the evolution of drawing features, and (2) to understand if the features collected at pre-literacy can predict future handwriting problems. Hence, features were tested for statistically significant variation among the five time points available to assess their longitudinal evolution in time. Moreover, we trained machine learning models to select the most important features collected at pre-literacy and to assess their predictive capabilities, with dysgraphia risk assessed at the end of second grade. 202 children completed the longitudinal study. We found that 81% of the feature was sensitive to longitudinal maturation and that it is possible to predict the difficulties with a weighted area under the precision-recall curve of 0.72. This is a step forward towards an early intervention for handwriting problems.

Handwriting skills could be highly impaired in patients affected by Parkinson’s disease (PD), and for this reason its analysis had always been considered relevant. In handwriting assessment, Archimedes spiral drawing is one of the most proposed tasks, due to its peculiar shape and ease of execution. In the last decades, digitizing tablets had been widely employed for the evaluation of the spiral performance, providing a cheap and non-invasive way to gather quantitative information, to be combined with the classical clinical examination. Despite this advantage, such approach cannot easily be adopted in an unsupervised scenario and lacks the natural feel of the traditional pen-and-paper approach. This work aims at overcoming these limitations by employing a smart ink pen, designed to write on paper and instrumented with inertial and force sensors, to automatically collect data related to spiral drawing execution of PD patients (n=30) and age-matched healthy controls (n=30). From the raw data, several time and frequency domains features were extracted and compared between the groups. The statistical analysis revealed some significant differences, showing less smooth acceleration and force profiles for PD patients. However, given the heterogeneous symptoms presented by the PD cohort, a detailed analysis of exemplifying PD patients was conducted, showing the ability of Archimedes spiral drawing to capture and quantify PD characteristic features. Clinical Relevance— Among the first clinical manifestations of the pathology, handwriting impairment appears in PD patients. It is often underestimated and not investigated properly. This easy-to-use tool could be very useful as a largescale screening, but also for treatment efficacy evaluation and for the identification of PD subgroups.