Wyniki wyszukiwania - BazTech

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Wytyczne projektowe dla osób w spektrum autyzmu - identyfikacja potrzeb, barier i ograniczeń na przykładzie szkolnych przestrzeni edukacyjnych

Matuszewska Joanna

Builder

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2023

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R.27, nr 12

18--21

PL

Artykuł stanowi próbę określenia wytycznych projektowych przestrzeni szkolnych dla użytkowników o szczególnych potrzebach, na przykładzie osób będących w spektrum autyzmu. Tekst prezentuje specyfikę percepcji przestrzeni tej grupy odbiorców pod kątem identyfikacji ich potrzeb oraz wskazania istniejących barier i ograniczeń w obiektach o funkcji edukacji.

EN

The article is an attempt to define design guidelines for school spaces for users with special needs, using the example of people on the autism spectrum. The text presents the specificity of the perception of the space of this group of recipients in terms of identifying their needs and showing the existing barriers and limitations in facilities with an educational function.

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E-technologie wspomagające terapię dzieci z autyzmem

Landowska Agnieszka, Pietkun Szymon, Pykała Małgorzata

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2022

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nr 3

82--87

PL

Autyzm jest zaburzeniem rozwojowym, w którym deficytowe są umiejętności komunikacyjne oraz możliwości uczenia. Edukacja i terapia dzieci z autyzmem wymaga zindywidualizowanego podejścia, gdyż występowanie i nasilenie objawów jest u nich różnorodne. Rozwiązania technologiczne, wspierające procesy terapeutyczne w autyzmie, muszą spełniać wiele kryteriów, oprócz ogólnej użyteczności czy też dopasowania do wieku dzieci. Projektowanie i specyfika rozwiązań informatycznych wspomagających terapię była i nadal jest przedmiotem badań na Politechnice Gdańskiej. W ich wyniku powstał zestaw aplikacji mobilnych, wspomagających terapię dzieci z autyzmem w nurcie behawioralnym o przełomowym znaczeniu, m. in. ze względu na istotne ograniczenia dostępnych w Polsce rozwiązań. Przyjazne Aplikacje są udostępniane nieodpłatnie w sklepie Google Play i korzystają z nich nie tylko placówki terapeutyczne, ale także indywidualni rodzice.

EN

Autism is a developmental disorder in which communication skills and learning abilities are compromised. Therapy of children with autism requires an individualized approach, as the occurrence and severity of symptoms vary among children. Technological solutions supporting therapeutic processes in autism must meet a number of criteria in addition to general usefulness or adjustment to the age of children. The design and specificity of IT solutions supporting therapy is the subject of research at the Gdansk University of Technology. As a result, a set of mobile applications supporting the behavioral therapy of children with autism was created, which had a significant influence on therapy in Poland, due to severe limitations of the other solutions available. Friendly Applications are available for free in the Google Play store and are used not only by therapeutic facilities but also by individual parents.

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Educational spaces for students with autism spectrum disorders

Pogoda Julia, Majczyk Joanna

Architectus

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2022

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Nr 3 (71)

69--77

EN

This study aims to present the relationship between the architectural surroundings and the needs of children with autism spectrum disorders in the educational environment. Therefore, it began with identifying the special needs of children with ASDs (Autism Spectrum Disorder) and the ways in which these needs are addressed in school designs carried out in the USA and Great Britain. The study also shows a few Polish attempts to create autism-friendly schools. Finally, an attempt was made to extend the paradigms of "universal design" with activities necessary to increase the comfort of using educational institutions by children with autism spectrum disorders.

PL

W niniejszym opracowaniu przedstawiono zależności między otoczeniem architektonicznym a potrzebami dzieci ze spektrum autyzmu w środowisku edukacyjnym. Rozpoczęto od wskazania specjalnych potrzeb dzieci z ASD (Autism Spectrum Disorder) i sposobów, w jaki odpowiada się na te potrzeby w projektach szkół zrealizowanych w USA i w Wielkiej Brytanii. W artykule pokazano też nieliczne polskie próby tworzenia szkół przyjaznych autystykom, a na końcu podjęto próbę rozszerzenia paradygmatów „projektowania uniwersalnego” o działania konieczne do zwiększenia komfortu użytkowania placówek edukacyjnych przez dzieci ze spektrum autyzmu.

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An intelligent multimodal framework for identifying children with autism spectrum disorder

Chen Jingying, Liao Mengyi, Wang Guangshuai, Chen Chang

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 3

435--448

EN

Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.

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A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method

Abdolzadegan Donya, Moattar Mohammad Hossein, Ghoshuni Majid

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

482--493

EN

Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism Spectrum Disorder (ASD). There are different challenges such as feature selection and the existence of artifacts in EEG signals. This article aims to present a robust method for early diagnosis of ASD from EEG signal. The study population consists of 34 children with ASD between 3–12 years and 11 healthy children in the same ranges of age. The proposed approach uses linear and nonlinear features such as Power Spectrum, Wavelet Transform, Fast Fourier Transform (FFT), Fractal Dimension, Correlation Dimension, Lyapunov Exponent, Entropy, Detrended Fluctuation Analysis and Synchronization Likelihood for describing the EEG signal. In addition Density Based Clustering is utilized for artifact removal and robustness. Besides, features selection is applied based on different criterions such as Mutual Information (MI), Information Gain (IG), Minimum-Redundancy Maximum-Relevancy (mRmR) and Genetic Algorithm (GA). Finally, the K-Nearest-Neighbor (KNN) and Support Vector Machines (SVM) classifiers are used for final decision. As a result, the investigation indicates that the classification accuracy of the approach using SVM is 90.57% while for KNN it is 72.77%. Moreover, the sensitivity of the proposed method is 99.91% for SVM and 91.96% for KNN. Also, experiments show that DFA, LE, Entropy and SL features have considerable influence in promoting the classification accuracy.

6

Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis

Ibrahim S., Djemal R., Alsuwailem A.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

16--26

EN

Quantification of abnormality in brain signals may reveal brain conditions and pathologies. In this study, we investigate different electroencephalography (EEG) feature extraction and classification techniques to assist in the diagnosis of both epilepsy and autism spectrum disorder (ASD). First, the EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). Two nonlinear methods were studied, namely, Shannon entropy and largest Lyapunov exponent, which measure complexity and chaoticity in the EEG recording, in addition to the two conventional methods (namely, standard deviation and band power). We also study the use of a cross-correlation approach to measure synchronization between EEG channels, which may reveal abnormality in communication between brain regions. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design exploration techniques: the University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and our own EEG recordings (46 subjects). The combination of DWT, Shannon entropy, and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem. The proposed method obtained better classification accuracy compared to the existing methods and tested using larger and more comprehensive EEG dataset. The proposed method could potentially be used to assist epilepsy and ASD diagnosis therefore improving the speed and the accuracy.

7

Accessible robots for improving social skills of individuals with autism

Tennyson M. F., Kuester D. A., Casteel J., Nikolopoulos C.

Journal of Artificial Intelligence and Soft Computing Research

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2016

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Vol. 6, No. 4

267--277

EN

This paper reports on an ongoing project between members of the computer science and special education departments of Bradley University and Murray State University, detailing the robotic platforms developed and investigated as a potential tool to improve social interactions among individuals with Autism Spectrum Disorders (ASD). Development of a fourth generation robotic agent is described, which uses economically available robotic platforms (Lego NXT) as Socially Assistive Robotics (SAR), combined with direct instruction pedagogy and social scripts to support an alternative educational approach to teaching social behavior. Specifically, in this fourth generation, changes to the physical design of the robots were made to improve the maintainability, reliability, maneuverability, and aesthetics of the robots. The software architecture was designed for modularity, configurability, and reusability of the software.

8

Computer-aided analysis of data from evaluation sheets of subjects with autism spectrum disorders

Pancerz K., Derkacz A., Mich O., Gomula J.

Bio-Algorithms and Med-Systems

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2016

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Vol. 12, no. 3

109--116

EN

In this paper, we deal with the problem of the initial analysis of data from evaluation sheets of subjects with autism spectrum disorders (ASDs). In the research, we use an original evaluation sheet including questions about competencies grouped into 17 spheres. An initial analysis is focused on the data preprocessing step including the filtration of cases based on consistency factors. This approach enables us to obtain simpler classifiers in terms of their size (a number of nodes and leaves in decision trees and a number of classification rules).

9

Simple cyclic movements as a distinct autism feature - computational approach

Dobosz K., Mikołajewski D., Wójcik G. M., Duch W.

Computer Science

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2013

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Vol. 14 (3)

475--489

EN

A diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders (ASD). Each year, the percentage of children diagnosed with ASD is growing. One common diagnostic feature in individuals with ASD is the tendency to exhibit atypical simple cyclic movements.The motor brain activity seems to generate a periodic attractor state that is hard to escape. Despite numerous studies, scientists and clinicians do not know exactly if ASD is a result of a simple yet general mechanism or of a complex set of mechanisms (either on the neural, molecular and system levels). Simulations using the biologically - relevant neural network model presented here may help to reveal the simplest mechanisms that may be responsible for specific behavior. Abnormal neural fatigue mechanisms may be responsible for motor symptoms as well as many (or perhaps all) of the other symptoms observed in ASD.

10

Computational approach to understanding Autism Spectrum Disorders

Duch W., Nowak W., Meller J., Osiński G., Dobosz K., Mikołajewski D., Wójcik G. M.

Computer Science

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2012

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Vol. 13 (2)

47-61

EN

Every year the prevalence of Autism Spectrum of Disorders (ASD) is rising. Is there a unifying mechanism of various ASD cases at the genetic, molecular, cellular or systems level? The hypothesis advanced in this paper is focused on neural dysfunctions that lead to problems with attention in autistic people. Simulations of attractor neural networks performing cognitive functions help to assess system long-term neurodynamics. The Fuzzy Symbolic Dynamics (FSD) technique is used for the visualization of attractors in the semantic layer of the neural model of reading. Large-scale simulations of brain structures characterized by a high order of complexity requires enormous computational power, especially if biologically motivated neuron models are used to investigate the influence of cellular structure dysfunctions on the network dynamics. Such simulations have to be implemented on computer clusters in a grid-based architectures.