Analyse statistique de la communication par le système perceptif d'un bébé (de 3 à 9 mois) avec sa mère

PILE (Programme International pour le Langage de l'Enfant, 2004--2008), s'est donné comme objectif de décrire par des techniques statistiques les processus chez le bébé qui participent à l'émergence de la parole. Une base de données qualitative a été élaborée à partir de séquences vidéo de bébés de 3 mois à 9 mois en interaction avec leur mère. Nous présentons une étude statistique de cette base, à l'aide de techniques inférentielles (tests de comparaisons non paramétriques) et d'analyses factorielles. Certains de nos résultats confirment ou précisent les hypothèses cliniques relativement à l'effet de certains facteurs tels que cohortes, âge calendaire, sexe des bébés. Les résultats qui décrivent statistiquement le comportement du bébé en interaction avec sa mère indiquent que la mise en place du système perceptif chez le bébé est centrale dans la construction des précurseurs du langage

Statistical Analysis of Mother-infant (3 to 9 months) Perceptive System Communication

PILE (Programme International pour le Langage de l'Enfant), the International Program for Child Language aims to describe the processes leading to the emergence of speech in infants thanks to statistic techniques. A qualitative data base was established on the basis of one minute video sequences (the statistical units) of babies aged from 3 to 9 months interacting with their mothers. The phenomenon under study is the process of speech construction. The 110 infants are belonging to seven cohorts: infants without disorders, hospitalized infants, premature infants, infants of visually deficient mothers, infants of blind mothers, infants with neurological disorders. We present a statistical study of this data base through inferential techniques (non parametric comparison tests) and factorial analyses. Some of our findings confirm or bring additional precision to clinical hypotheses concerning the impact of certain factors, such as age cohort, calendar age, infant's sex. The statistical results describing the infant's behavior while interacting with its mother indicate that the development of the baby's perceptive system is central in the construction of language precursors

Навчання розробці WebAR з інтегрованим машинним навчанням: методика для імерсивного та інтелектуального навчального досвіду

Augmented reality (AR) and machine learning (ML) are rapidly growing technologies with immense potential for transforming education. Web-based augmented reality (WebAR) provides a promising approach to delivering immersive learning experiences on mobile devices. Integrating machine learning models into WebAR applications can enable advanced interactive effects by responding to user actions, thus enhancing the educational content. However, there is a lack of effective methodologies to teach students WebAR development with integrated machine learning. This paper proposes a methodology with three main steps: (1) Integrating standard TensorFlow.js models like handpose into WebAR scenes for gestures and interactions; (2) Developing custom image classification models with Teachable Machine and exporting to TensorFlow.js; (3) Modifying WebAR applications to load and use exported custom models, displaying model outputs as augmented reality content. The proposed methodology is designed to incrementally introduce machine learning integration, build an understanding of model training and usage, and spark ideas for using machine learning to augment educational content. The methodology provides a starting point for further research into pedagogical frameworks, assessments, and empirical studies on teaching WebAR development with embedded intelligence.Доповнена реальність (AR) і машинне навчання (ML) — це технології, що швидко розвиваються, і мають величезний потенціал для трансформації освіти. Веб-доповнена реальність (WebAR) забезпечує багатообіцяючий підхід до захоплюючого навчання на мобільних пристроях. Інтеграція моделей машинного навчання в додатки WebAR може увімкнути розширені інтерактивні ефекти, реагуючи на дії користувача, таким чином покращуючи навчальний контент. Однак бракує ефективних методик навчання студентів розробці WebAR з інтегрованим машинним навчанням. У цьому документі пропонується методика з трьох основних етапів: (1) Інтеграція стандартних моделей TensorFlow.js, таких як handpose, у сцени WebAR для жестів і взаємодії; (2) Розробка користувацьких моделей класифікації зображень за допомогою Teachable Machine та експорт до TensorFlow.js; (3) Модифікація додатків WebAR для завантаження та використання експортованих користувацьких моделей, відображення результатів моделей як вміст доповненої реальності. Запропонована методика розроблена для поступового запровадження інтеграції машинного навчання, формування розуміння навчання та використання моделі та зародження ідей щодо використання машинного навчання для розширення навчального контенту. Методологія є відправною точкою для подальших досліджень педагогічних рамок, оцінок та емпіричних досліджень навчання розробки WebAR із вбудованим інтелектом

Virtual Reality as a Tool for Evaluation of Repetitive Rhythmic Movements in the Elderly and Parkinson's Disease Patients

This work presents an immersive Virtual Reality (VR) system to evaluate, and potentially treat, the alterations in rhythmic hand movements seen in Parkinson's disease (PD) and the elderly (EC), by comparison with healthy young controls (YC). The system integrates the subjects into a VR environment by means of a Head Mounted Display, such that subjects perceive themselves in a virtual world consisting of a table within a room. In this experiment, subjects are presented in 1st person perspective, so that the avatar reproduces finger tapping movements performed by the subjects. The task, known as the finger tapping test (FT), was performed by all three subject groups, PD, EC and YC. FT was carried out by each subject on two different days (sessions), one week apart. In each FT session all subjects performed FT in the real world (FTREAL) and in the VR (FTVR); each mode was repeated three times in randomized order. During FT both the tapping frequency and the coefficient of variation of inter-tap interval were registered. FTVR was a valid test to detect differences in rhythm formation between the three groups. Intra-class correlation coefficients (ICC) and mean difference between days for FTVR (for each group) showed reliable results. Finally, the analysis of ICC and mean difference between FTVR vs FTREAL, for each variable and group, also showed high reliability. This shows that FT evaluation in VR environments is valid as real world alternative, as VR evaluation did not distort movement execution and detects alteration in rhythm formation. These results support the use of VR as a promising tool to study alterations and the control of movement in different subject groups in unusual environments, such as during fMRI or other imaging studies

Robust presurgical functional MRI at 7 T using response consistency

Functional MRI is valuable in presurgical planning due to its non-invasive nature, repeatability, and broad availability. Using ultra-high field MRI increases the specificity and sensitivity, increasing the localization reliability and reducing scan time. Ideally, fMRI analysis for this application should identify unreliable runs and work even if the patient deviates from the prescribed task timing or if there are changes to the hemodynamic response due to pathology. In this study, a model-free analysis method-UNBIASED-based on the consistency of fMRI responses over runs was applied, to ultra-high field fMRI localizations of the hand area. Ten patients with brain tumors and epilepsy underwent 7 Tesla fMRI with multiple runs of a hand motor task in a block design. FMRI data were analyzed with the proposed approach (UNBIASED) and the conventional General Linear Model (GLM) approach. UNBIASED correctly identified and excluded fMRI runs that contained little or no activation. Generally, less motion artifact contamination was present in UNBIASED than in GLM results. Some cortical regions were identified as activated in UNBIASED but not GLM results. These were confirmed to show reproducible delayed or transient activation, which was time-locked to the task. UNBIASED is a robust approach to generating activation maps without the need for assumptions about response timing or shape. In presurgical planning, UNBIASED can complement model-based methods to aid surgeons in making prudent choices about optimal surgical access and resection margins for each patient, even if the hemodynamic response is modified by pathology. Hum Brain Mapp 38:3163-3174, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc

Advances and Insights into Neurological Practice 2016-17

Papers published by the European Journal of Neurology reflect the broad interest of practicing neurologists in advances in the aetiology, diagnosis and management of neurological disorders. As a general journal, the proportion of papers in the different subject areas reasonably reflects the case load of a practising neurologist. Stroke represents the largest proportion of papers published, including those on pathophysiology (1-23), acute stroke management (24-47) and the outcome of patients who have suffered stroke (48-72). This article is protected by copyright. All rights reserved