1,720,977 research outputs found
Il mio orizzonte. Il valore dell'orientamento nelle dis-abilità cognitivo relazionali. Atti del Convegno (Varese, 23-25 ottobre 2024)
No full textNeuropsychological functioning in young people with Attention deficit/hyperactivity disorder: the impact of specific learning disorders
No full textTeleassessment can overestimate the risk of learning disability in first and second grade of primary school
Get PDFBackground
Early administration of reading, writing and math standardised tests allows us to assess the risk of developing a learning disorder and to plan a specific intervention. The ease of access to technological tools and past pandemic restrictions have led to the abandonment of face-to-face assessment in favour of teleassessment methods. Although these kinds of assessments sometimes seem comparable in the literature, their equivalence is not clearly defined. The first aim of our research was to test the comparability of the two modalities using a complete battery of neuropsychological tests. Second, we addressed whether the administration order could influence performance.
Methods
Using a within-subject sample design, we compared face-to-face and teleassessment performance in reading, writing and math tasks in 64 children attending first and second year of primary school.
Results
Teleassessment scores were lower than face-to-face; math tests weighted on difference. Differences were mitigated by previous experience with face-to-face modality.
Conclusions
Although there was considerable overlap between the two administration methods, teleassessment could lead to overestimation of the risk for learning disorders
A comparison of different instruments to assess reading in Italian (MT-battery, DDE-2-battery and ALCE battery): alternative or complementary tools?
No full textDevelopment and Validation of an iPad-based Serious Game for Emotion Recognition and Attention Tracking towards Early Identification of Autism
Get PDFThe diagnosis of Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) can be challenging due to limited accessibility and subjective assessments. Autistic individuals often present difficulties in emotional regulation, emotion recognition and imitation, and in maintaining focus. Emotional expressions and attention are thus hallmarks of ASD and ADHD and can be analyzed to identify these conditions. In this study, we developed and validated a serious game that integrates emotion recognition and attention tracking as a novel tool for identification of ASD and ADHD. Leveraging the TrueDepth camera capabilities, our game provides a cost-effective and user-friendly alternative to current face-tracking technologies. We compared the accuracy of emotion recognition using Euclidean distance with calibrated reference expressions and a calibration-free system based on a machine learning model using Random Forest. We also identified children at risk of ADHD using the Bells test and constructed a machine learning model, utilizing Support Vector Machine and Leave-One-Out Cross Validation, trained on attention data and game data to predict this risk. Our game was tested on 20 adults to validate the emotion recognition system, and then on 17 children of the primary school to assess usability and test the constructed models. The emotion recognition system achieved an accuracy of 0.78 for adults and 0.45 for children, while the machine learning model predicted seven emotions in children with an accuracy of 0.50, suggesting the potential for eliminating the need for calibration. The model also obtained good results in predicting valence and arousal values. The attention model showed excellent validation scores (accuracy: 0.94), indicating the possibility of extending it to a larger cohort. The System Usability Score was excellent (85.0), and children found the game enjoyable, making it a promising tool for ASD and ADHD identification
A CO-DESIGNED PLATFORM FOR A TECHNOLOGY-BASED EARLY IDENTIFICATION TO SUPPORT SPECIFIC LEARNING DISORDERS SCREENING IN SCHOOLS
No full textIn the first years of schooling, children build the basis of their knowledge and life skills; this development may be hindered by specific learning disorders (SLDs) that impact learning and, consequently, self-esteem, with effects that last throughout life. Their screening is difficult because of the complexity in distinguishing a learning delay from an actual disorder at an early stage. Still, it is essential to detect as soon as possible the presence of SLDs to effectively treat them. This is the rationale of the IndiPote(dn)S project. It addresses children from the last year of kindergarten to the second year of primary school; first, teachers observe children in school activities and report their abilities following grids of learning weaknesses precursors, designed by psychologists and child neuropsychiatrists (CNP); then, weak children are trained with a Vademecum of pen-and-paper or manual activities; finally, they are observed again. If the problems are not solved, further insight from a CNP is asked with priority. The process is performed by trained teachers, who however introduce subjectivity. In this context, technology can be beneficial to detecting signs not visible to the naked eye, structuring data collection, and standardizing the observation and training, both fundamental to gaining insights on children's learning trajectory in the screening path. This work aimed to co-design and develop a complete instrument to systematize data collection (Aim 1) and provide technological support to the screening and training (Aim 2). As for Aim 1, after brainstorming with stakeholders to understand the paper-based process, a web-app platform was devised. Specifically, the web-app choice was made for its capability of running on different devices (e.g. PCs and tablets), requiring only Internet access to work. The platform provides basic functions to input data on class composition; grade-specific questionnaires on children’s weaknesses and their training; and outcome of CNPs’ visits. The platform was proposed to schools in iterative testing that lasted from 2019 to 2023 and involved more than 130 schools and 15 thousand children on average per year. Yearly enhancements have been made thanks to users’ feedback and prompts. During this period, the focus was to assess adherence by measuring compliance, and the effectiveness of the screening by measuring the percentage of true positives in the reporting to CNP. Since its potential in the pilot phase, a final scalable version of the platform was produced to enable widespread adoption. The mean compliance obtained during iterative testing was 87.5%, whereas the true positives in CNP reporting resulted to be 75.8%. As for Aim 2, the final platform was also enriched with a module able to connect the observation and training activities with technologies like serious games or smart objects, paired with a reasoner that will be used to provide suggestions on training, describe the learning trajectory, and predict the outcome. The use case of a smart ink pen for screening will be presented. In conclusion, the platform is a promising tool for weakness identification, fundamental for the SLDs screening
Preliminary Validation of a Cursive Handwriting Reconstruction Algorithm from a Sensorized Ink Pen
No full textLack 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
A co-designed platform for a technology-based early identification to support specific learning disorders screening in schools
No full textIn the first years of schooling, children build the basis of their knowledge and life skills; this development may be hindered by specific learning disorders (SLDs) that impact learning and, consequently, self-esteem, with effects that last throughout life. Their screening is difficult because of the complexity in distinguishing a learning delay from an actual disorder at an early stage. Still, it is essential to detect as soon as possible the presence of SLDs to effectively treat them. This is the rationale of the IndiPote(dn)S project. It addresses children from the last year of kindergarten to the second year of primary school; first, teachers observe children in school activities and report their abilities following grids of learning weaknesses precursors, designed by psychologists and child neuropsychiatrists (CNP); then, weak children are trained with a Vademecum of pen-and-paper or manual activities; finally, they are observed again. If the problems are not solved, further insight from a CNP is asked with priority. The process is performed by trained teachers, who however introduce subjectivity. In this context, technology can be beneficial to detecting signs not visible to the naked eye, structuring data collection, and standardizing the observation and training, both fundamental to gaining insights on children's learning trajectory in the screening path. This work aimed to co-design and develop a complete instrument to systematize data collection (Aim 1) and provide technological support to the screening and training (Aim 2). As for Aim 1, after brainstorming with stakeholders to understand the paper-based process, a web-app platform was devised. Specifically, the web-app choice was made for its capability of running on different devices (e.g. PCs and tablets), requiring only Internet access to work. The platform provides basic functions to input data on class composition; grade-specific questionnaires on children’s weaknesses and their training; and outcome of CNPs’ visits. The platform was proposed to schools in iterative testing that lasted from 2019 to 2023 and involved more than 130 schools and 15 thousand children on average per year. Yearly enhancements have been made thanks to users’ feedback and prompts. During this period, the focus was to assess adherence by measuring compliance, and the effectiveness of the screening by measuring the percentage of true positives in the reporting to CNP. Since its potential in the pilot phase, a final scalable version of the platform was produced to enable widespread adoption. The mean compliance obtained during iterative testing was 87.5%, whereas the true positives in CNP reporting resulted to be 75.8%. As for Aim 2, the final platform was also enriched with a module able to connect the observation and training activities with technologies like serious games or smart objects, paired with a reasoner that will be used to provide suggestions on training, describe the learning trajectory, and predict the outcome. The use case of a smart ink pen for screening will be presented. In conclusion, the platform is a promising tool for weakness identification, fundamental for the SLDs screening
Sindrome di Tourette, disturbo da deficit dell'attenzione/iperattività e deficit delle funzioni esecutive
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