Advanced Parameterisation of Online Handwriting in Writers with Graphomotor Disabilities

Grafomotorick© obt­e (GD) vraznÄ ovlivuj­ kvalitu ivota koln­m vÄkem poÄ­naj­c, kde se vyv­jej­ grafomotorick© schopnosti, a do dchodov©ho vÄku. VÄasn diagnza tÄchto obt­­ a terapeutick zsah maj­ velk vznam k jejich zlepen­. Vzhledem k tomu, e GD souvis­ z v­cermi symptomy v oblasti kinematiky, zkladn­ kinematick© parametry jako rychlost, zrychlen­ a vih prokzaly efektivn­ kvantizaci tÄchto symptom. Objektivn­ vpoÄetn­ syst©m podpory rozhodovn­ pro identifikaci a vyeten­ GD vak nen­ dostupn. A proto je hlavn­m c­lem m© disertaÄn­ prce vzkum pokroÄil© metody parametrizace online p­sma pro analzu GD se speciln­m zamÄen­m na vyuit­ metod zlomkov©ho kalkulu. Tato prce je prvn­, kter experimentuje s vyuit­m derivac­ neceloÄ­seln©ho du (FD) pro analzu GD pomoc­ online p­sma z­skan©ho od pacient s Parkinsonovou nemoc­ a u dÄt­ koln­ho vÄku. Byla navrena a evaluovna nov metoda parametrizace online p­sma zaloena na FD vyuit­m Grnwald-Letnikova p­stupu. Bylo dokzno, e navren metoda vznamnÄ zlepuje diskriminaÄn­ s­lu a deskriptivn­ schopnosti v oblasti Parkinsonick© dysgrafie. StejnÄ tak metoda pozitivnÄ ovlivnila i nejmodernÄj­ techniky v oblasti analzy GD u dÄt­ koln­ho vÄku. Vyvinut parametrizace byla optimalizovna s ohledem na vpoÄetn­ nroÄnost (a o 80 %) a tak© na vyladÄn­ du FD. Ke konci prce byly porovnny v­cer© p­stupy vpoÄtu FD, jmenovitÄ Riemann-Liouvillv, Caputv spoleÄnÄ z Grnwald-Letnikovm p­stupem za Äelem identifikace tÄch nejvhodnÄj­ch pro jednotliv© oblasti analzy GD.Graphomotor disabilities (GD) significantly affect the quality of life beginning from the school-age, when the graphomotor skills are developed, until the elderly age. The timely diagnosis of these difficulties and therapeutic interventions are of great importance. As GD are associated with several symptoms in the field of kinematics, the basic kinematic features such as velocity, acceleration, and jerk were proved to effectively quantify these symptoms. Nevertheless, an objective computerized decision support system for the identification and assessment of GD is still missing. Therefore, the main objective of my dissertation is the research of an advanced online handwriting parametrization utilized in the field of GD analysis, with a special focus on methods based on fractional calculus. This work is the first to experiment with fractional-order derivatives (FD) in the GD analysis by online handwriting of Parkinsonâs disease (PD) patients and school-age children. A new online handwriting parametrization technique based on the Grnwald-Letnikov approach of FD has been proposed and evaluated. In the field of PD dysgraphia, a significant improvement in the discrimination power and descriptive abilities was proven. Similarly, the proposed methodology improved current state-of-the-art techniques of GD analysis in school-aged children. The newly designed parametrization has been optimized in the scope of the computational performance (up to 80 %) as well as in FD order fine-tuning. Finally, various FD-approaches were compared, namely Riemann-Liouville, Caputoâs, together with Grnwald-Letnikov approximation to identify the most suitable approach for particular areas of GD analysis.

Exploration of Various Fractional Order Derivatives in Parkinson's Disease Dysgraphia Analysis

Parkinson's disease (PD) is a common neurodegenerative disorder with a prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor symptoms of PD such as rigidity and bradykinesia affect the muscles involved in the handwriting process resulting in handwriting abnormalities called PD dysgraphia. Nowadays, online handwritten signal (signal with temporal information) acquired by the digitizing tablets is the most advanced approach of graphomotor difficulties analysis. Although the basic kinematic features were proved to effectively quantify the symptoms of PD dysgraphia, a recent research identified that the theory of fractional calculus can be used to improve the graphomotor difficulties analysis. Therefore, in this study, we follow up on our previous research, and we aim to explore the utilization of various approaches of fractional order derivative (FD) in the analysis of PD dysgraphia. For this purpose, we used the repetitive loops task from the Parkinson's disease handwriting database (PaHaW). Handwritten signals were parametrized by the kinematic features employing three FD approximations: Gr\"unwald-Letnikov's, Riemann-Liouville's, and Caputo's. Results of the correlation analysis revealed a significant relationship between the clinical state and the handwriting features based on the velocity. The extracted features by Caputo's FD approximation outperformed the rest of the analyzed FD approaches. This was also confirmed by the results of the classification analysis, where the best model trained by Caputo's handwriting features resulted in a balanced accuracy of 79.73% with a sensitivity of 83.78% and a specificity of 75.68%.Comment: Print ISBN 978-3-031-19744-

Proceedings of the 12th International Conference on Technology in Mathematics Teaching ICTMT 12

Innovation, inclusion, sharing and diversity are some of the words that briefly and suitably characterize the ICTMT series of biennial international conferences – the International Conference on Technology in Mathematics Teaching. Being the twelfth of a series which began in Birmingham, UK, in 1993, under the influential enterprise of Professor Bert Waits from Ohio State University, this conference was held in Portugal for the first time. The 12th International Conference on Technology in Mathematics Teaching was hosted by the Faculty of Sciences and Technology of the University of Algarve, in the city of Faro, from 24 to 27 June 2015, and was guided by the original spirit of its foundation. The integration of digital technologies in mathematics education across school levels and countries, from primary to tertiary education, together with the understanding of the phenomena involved in the teaching and learning of mathematics in technological environments have always been driving forces in the transformation of pedagogical practices. The possibility of joining at an international conference a wide diversity of participants, including school mathematics teachers, lecturers, mathematicians, mathematics educators and researchers, software designers, and curriculum developers, is one facet that makes this conference rather unique. At the same time, it seeks to foster the sharing of ideas, experiences, projects and studies while providing opportunities to try-out and assess tools or didactical proposals during times of hands-on work. The ICTMT 12 had this same ambition, when embracing and welcoming just over 120 delegates who actively and enthusiastically contributed to a very packed program of scientific proposals and sessions on various topics

Predicting the Outcome of Cognitive Training in Parkinson’s Disease using Magnetic Resonance Imaging

Motivation: Cognitive impairment is an important symptom of Parkinson’s Disease (PD), usually having a substantial negative impact on the quality of life of patients, families, and caregivers. Cognitive Training (CT) have been proven effective in halting the process of cognitive decline in PD. However, the efficacy of CT is unpredictable from subject to subject. Objective: Investigate the possibility of predicting the outcome of CT in PD patients with Mild Cognitive Impairment using structural and functional Magnetic Resonance Imaging (MRI) data. Methods: Before CT, a sample of 42 PD patients underwent structural and functional MRI. Graph measures were then extracted from their structural and functional con nectomes and used as features for random forest (RFo) and decision tree (DT) machine learning (ML) regression algorithms with and without prior latent component analysis (LCA). CT response was evaluated by assessing the outcomes of the Tower of London task pre- and post-treatment. Finally, the 4 ML models were used to predict CT response and their performances were assessed. Post hoc analyses were conducted to investigate whether these algorithms could predict age using connectomic measures on a sample of 80 PD patients. Results: The performances of the aforementioned algorithms did not differ signifi cantly from the baseline performance predicting the subject-specific CT outcome. The performance of the RFo without LCA differed significantly from the baseline performance in the age prediction task for the sample of 80 patients. Conclusion: Notwithstanding the lack of statistical significance in predicting our xicognitive outcomes, the relative success of the age prediction task points towards the potential of this approach. We hypothesise that bigger sample sizes are needed in order to predict the outcome of CT using ML

Technology and Testing

From early answer sheets filled in with number 2 pencils, to tests administered by mainframe computers, to assessments wholly constructed by computers, it is clear that technology is changing the field of educational and psychological measurement. The numerous and rapid advances have immediate impact on test creators, assessment professionals, and those who implement and analyze assessments. This comprehensive new volume brings together leading experts on the issues posed by technological applications in testing, with chapters on game-based assessment, testing with simulations, video assessment, computerized test development, large-scale test delivery, model choice, validity, and error issues. Including an overview of existing literature and ground-breaking research, each chapter considers the technological, practical, and ethical considerations of this rapidly-changing area. Ideal for researchers and professionals in testing and assessment, Technology and Testing provides a critical and in-depth look at one of the most pressing topics in educational testing today

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Pilot study for subgroup classification for autism spectrum disorder based on dysmorphology and physical measurements in Chinese children

Poster Sessions: 157 - Comorbid Medical Conditions: abstract 157.058 58BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder affecting individuals along a continuum of severity in communication, social interaction and behaviour. The impact of ASD significantly varies amongst individuals, and the cause of ASD can originate broadly between genetic and environmental factors. Objectives: Previous ASD researches indicate that early identification combined with a targeted treatment plan involving behavioural interventions and multidisciplinary therapies can provide substantial improvement for ASD patients. Currently there is no cure for ASD, and the clinical variability and uncertainty of the disorder still remains. Hence, the search to unravel heterogeneity within ASD by subgroup classification may provide clinicians with a better understanding of ASD and to work towards a more definitive course of action. METHODS: In this study, a norm of physical measurements including height, weight, head circumference, ear length, outer and inner canthi, interpupillary distance, philtrum, hand and foot length was collected from 658 Typical Developing (TD) Chinese children aged 1 to 7 years (mean age of 4.19 years). The norm collected was compared against 80 ASD Chinese children aged 1 to 12 years (mean age of 4.36 years). We then further attempted to find subgroups within ASD based on identifying physical abnormalities; individuals were classified as (non) dysmorphic with the Autism Dysmorphology Measure (ADM) from physical examinations of 12 body regions. RESULTS: Our results show that there were significant differences between ASD and TD children for measurements in: head circumference (p=0.009), outer (p=0.021) and inner (p=0.021) canthus, philtrum length (p=0.003), right (p=0.023) and left (p=0.20) foot length. Within the 80 ASD patients, 37(46%) were classified as dysmorphic (p=0.00). CONCLUSIONS: This study attempts to identify subgroups within ASD based on physical measurements and dysmorphology examinations. The information from this study seeks to benefit ASD community by identifying possible subtypes of ASD in Chinese population; in seek for a more definitive diagnosis, referral and treatment plan.published_or_final_versio

Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education

International audienceThis volume contains the Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education (ERME), which took place 9-13 February 2011, at Rzeszñw in Poland