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An Augmentative System with Facial and Emotion Recognition for Improving the Skills of Children with Autism Spectrum Disorders
Title
An Augmentative System with Facial and Emotion Recognition for Improving the Skills of Children with Autism Spectrum Disorders.
Creator
Alharbi, Mohammed N., Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Autism spectrum disorders (ASDs) are one of the complex, pervasive, and multifactorial neurodevelopmental conditions which affect one in 68 children. Scientific research has proven the efficiency of using technologies to improve communication and social skills of autistic children. The use of technological devices, such as mobile applications and multimedia, increase the interest of autistic children to learn while playing games. This thesis presents the re-engineering, extension, and...
Show moreAutism spectrum disorders (ASDs) are one of the complex, pervasive, and multifactorial neurodevelopmental conditions which affect one in 68 children. Scientific research has proven the efficiency of using technologies to improve communication and social skills of autistic children. The use of technological devices, such as mobile applications and multimedia, increase the interest of autistic children to learn while playing games. This thesis presents the re-engineering, extension, and evolution of an existing prototype Windows-based mobile application called Ying to become an Android mobile application which is augmented with facial and emotion recognition. This mobile app complements different approaches of traditional therapy, such as Applied Behavior Analysis (ABA). Ying integrates different computer-assisted technologies, including speech recognition, audio and visual interaction, and mobile applications to enhance autistic children’s social behavior and verbal communication skills. An evaluation of the efficacy of using Ying has been conducted and its results are presented in the thesis.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00005981
Subject Headings
Autism spectrum disorders, Human-computer interaction, Mobile apps
Format
Document (PDF)
A PROBABILISTIC CHECKING MODEL FOR EFFECTIVE EXPLAINABILITY BASED ON PERSONALITY TRAITS
Title
A PROBABILISTIC CHECKING MODEL FOR EFFECTIVE EXPLAINABILITY BASED ON PERSONALITY TRAITS.
Creator
Alharbi, Mohammed N., Huang, Shihong, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
It is becoming increasingly important for an autonomous system to be able to explain its actions to humans in order to improve trust and enhance human-machine collaboration. However, providing the most appropriate kind of explanations – in terms of length, format, and presentation mode of explanations at the proper time – is critical to enhancing their effectiveness. Explanation entails costs, such as the time it takes to explain and for humans to comprehend and respond. Therefore, the actual...
Show moreIt is becoming increasingly important for an autonomous system to be able to explain its actions to humans in order to improve trust and enhance human-machine collaboration. However, providing the most appropriate kind of explanations – in terms of length, format, and presentation mode of explanations at the proper time – is critical to enhancing their effectiveness. Explanation entails costs, such as the time it takes to explain and for humans to comprehend and respond. Therefore, the actual improvement in human-system tasks from explanations (if any) is not always obvious, particularly given various forms of uncertainty in knowledge about humans. In this research, we propose an approach to address this issue. The key idea is to provide a structured framework that allows a system to model and reason about human personality traits as critical elements to guide proper explanation in human and system collaboration. In particular, we focus on the two concerns of modality and amount of explanation in order to optimize the explanation experience and improve overall system-human utility. Our models are based on probabilistic modeling and analysis (PRISM-games) to determine at run time what the most effective explanation under uncertainty is. To demonstrate our approach, we introduce a self-adaptative system called Grid – a virtual game – and the Stock Prediction Engine (SPE), which allows an automated system and a human to collaborate on the game and stock investments. Our evaluation of these exemplars, through simulation, demonstrates that a human subject’s performance and overall human-system utility is improved when considering the psychology of human personality traits in providing explanations.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013894
Subject Headings
Human-computer interaction, Probabilistic modelling, Human-machine systems, Affective Computing
Format
Document (PDF)