Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.

/pdf/dysgraphia-detection-through-machine-learning-1b4rd2zb77.pdf

Dysgraphia detection through machine learning.

Nowadays, mobile applications seem to play a vital role in both dyslexia screening and intervention, accumulating numerous assets and comforts in according to dyslexics’ needs and learning pace. Initial detection of students’ disabilities, followed by a corresponding intervention program were the best combination of actions in an attempt to any problem be confronted effectively. In our today’s society, mobile apps have been considered as an alternative approach for the success of these well-known key steps, as well. The aim of this literature review is to propose a great number of screening and treatment mobile applications for dyslexia. During the last decades these  have been developed or have been implemented, focused on different aspects of dyslexia symptoms (reading, writing, mathematical difficulties, memory etc.) The studies are categorized into two sections, based on the purpose for which mobile applications are used at a time. The first part reflects a mobile app as a detection tool, while the second part presents it as intervention tool.

/pdf/mobile-applications-an-emerging-powerful-tool-for-dyslexia-27e9scrba7.pdf

Mobile Applications, An Emerging Powerful Tool for Dyslexia Screening and Intervention: A Systematic Literature Review

Abstract Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions.

/pdf/machine-learning-techniques-in-adaptive-and-personalized-3bckhbrd.pdf

Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness

Writing abilities are impacted by dysgraphia, a condition of learning disability. It might be challenging to diagnose dysgraphia at an initial point of a child's upbringing. Problematic abilities linked to Dysgraphia difficulties that is utilized in detecting the learning disorder. The features used in this research to identify dysgraphia include handwriting and geometric features that is reclaimed using kekre-discrete cosine mathematical model. The feature learning step of deep transfer learning makes good use of the obtained features to identify dysgraphia. The results of the data collection indicate that this study can use handwritten images to detect children who have dysgraphia. Compared to past investigations, this experiment has shown a significant improvement in the capacity to identify dysgraphia using handwritten drawings. The proposed approach is compared with the machine learning and deep learning approaches where the Kekre-Discrete Cosine Transform with Deep Transfer Learning (K-DCT-DTL) outperforms the existing approaches. The proposed K-DCT-DTL approach attains 99.75% of highest accuracy that exhibits the efficiency of the proposed method. 

Dysgraphia disorder forecasting and classification technique using intelligent deep learning approaches

Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task.  It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared are KNN (K-Nearest Neighbors), Naive Bayes, Decision tree, Random Forest, SVM (Support Vector Machine) on the Dysgraphia dataset. Results indicate the highest performance of the Random forest classification model for Dysgraphia identification.

https://www.turcomat.org/index.php/turkbilmat/article/download/6142/5109

Comparison of Classification Methods used in Machine Learning for Dysgraphia Identification

Dyslexia, Dysgraphia and Dyscalculia are significant learning disabilities that affect around 10% of children in the world. Despite the advancement of technology literacy in the community, limited attention has been given for screening and intervention of these disabilities using mobile applications in Sri Lanka. In this research, one of the first deep learning and machine learning based mobile applications, named “Pubudu” was developed for screening and intervention of dyslexia, dysgraphia and dyscalculia supporting local languages. In “Pubudu” we have followed up clinical screening and diagnostic procedures recommended by health professionals for screening and intervention. The screening of dyslexia, letter dysgraphia and numeric dysgraphia was carried out using deep neural network and the screening for dyscalculia was carried out using machine learning techniques. Intervention techniques are implemented using gamified environments. System testing was carried out using 50 differently abled children and 50 typical children. With the initial dataset 88%, 58%, 99% screening accuracies are achieved in neural networks for letter dysgraphia, dyslexia and numeric dysgraphia screening while dysgraphia, whereas 90% accuracy was achieved for dyscalculia. Handwritten letters and numbers were fed as inputs to CNN model in letter dysgraphia and numeric dysgraphia while embedded audio clips of letter pronunciation were fed in to voice recognition CNN model in dyslexia. “Pubudu” shows significant potential for screening and intervention of dyslexia, dysgraphia and dyscalculia in local languages motivating children and interactively making them able and would be an enabling app for most of the underprivileged children in Sri Lanka.

Pubudu: Deep Learning Based Screening And Intervention of Dyslexia, Dysgraphia And Dyscalculia

This paper aims to diagnose children with specific learning disabilities and provide treatments via a mobile game. Learning disabilities are neurological disorders that affect the brain. Children with learning disabilities have trouble with learning compared to their fellow peers and quite often fall back academically since a majority of them go undiagnosed. The specific learning disabilities for which this paper provides screening are dyslexia a reading disability, dyscalculia a mathematical disability, letter dysgraphia and numeric dysgraphia are both writing disabilities. Deep learning and machine learning techniques are used in the screening process of these specific learning disabilities. Trained convolutional neural networks are used to detect the spoken letter/word, detect the written letter/word and detect the written number on the mobile application. Outputs from the convolutional neural network are fed into the models used for screening learning disabilities. The machine learning algorithms used in building the models include k-nearest neighbors, random forest and support vector machine. Screening results from the models built in this research provided an accuracy of 89%, 90%, 92%, 92% for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia respectively. This is the first game based screening and intervention tool for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia.

A Gamified Approach for Screening and Intervention of Dyslexia, Dysgraphia and Dyscalculia

Attention Deficit Hyperactivity Disorder (ADHD) is a common disorder, which can be identified mostly among children which often lasts till their adulthood. A person with ADHD would not be able to pay attention, would not think about consequences of their actions(impulsivity) and sometimes could be hyperactive (overly active). According to the statistical reports of ADHD Institute, worldwide prevalence of ADHD is ~2.2% overall (range, 0.1-8.1%) which has been estimated only within the children and adolescents (aged < 18 years) [1]. A person with ADHD could have troubles with socializing, making friends and may struggle with studies as well. It is stated that people with ADHD could often choose smaller rewards over bigger rewards considering only the time they have to spend on. A physical examination, series of interviews, reports from associates of the patient are some of the existing methodologies used to diagnose ADHD. Adult ADHD Self-Report Scale Symptom Checklist (ASRS), Barkley Adult ADHD Rating Scale - V, Brown ADD Scales (Adult), Conners’ Adult ADHD Rating Scales (CARRS) and Copeland Symptom Checklist for Attention Deficit Disorders - Adult Version are some of the screening tools and checklist methods used currently. Above mentioned tools and methods mainly focus on adults. It is complicated to test children as they have certain behaviors at several ages that make symptoms of this disorder undistinguishable. The current methods of diagnosing have issues such as lack of concentration to the patient, difficulty to monitor child/adult under different environments, maintaining patient’s records etc. We have developed a localized mobile application which includes questionnaire and gaming activities to identify and diagnose symptoms such as Hyperactivity, Impulsivity, Inattention and organizing disorders. This application is not restricted for adults as above-mentioned tools and methods that are currently being used. If we can identify these disorders as early as possible this would deliver many benefits to the children’s growth and future.

IHI:– A Mobile Application for ADHD Analysis and Detection

Ornamental Fish Industry continues to be one of the fastest growing sectors worldwide. Healthy fish production at aquariums requires intensive care and ensures a stable and an optimum production environment inside the fish tanks, which is a challenging task. Unfortunately, due to the limitations in fish industry, productivity of well-developed, healthy fish has drastically depreciated. Limited skills and knowledge of aquarists have been a challenging task which has led to inaccurate predictions on certain factors such as quantification and length of estimation, amounts and types of fish food and servicing the filters at proper time intervals. Existing aquariums depend on the experience and availability of the aquarists, which can be a challenging process in real life. Developing a system to regulate these major concerns is a prominent solution. This research is done to propose an automated method, with the help of several fish aquariums and existing research papers, to encounter the mentioned major concerns which affects the aquarists and other stakeholders.

Pasangi Ratnayake

Papers

Pubudu: Deep Learning Based Screening And Intervention of Dyslexia, Dysgraphia And Dyscalculia

A Gamified Approach for Screening and Intervention of Dyslexia, Dysgraphia and Dyscalculia

IHI:– A Mobile Application for ADHD Analysis and Detection

Smart Caring System for Ornamental Fish