自閉症譜系障礙虛擬實境訓練系統的開發與驗證;Development and Validation of a Virtual Reality based training System for Autism Spectrum Disorder

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/95661

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/95661

Title:	自閉症譜系障礙虛擬實境訓練系統的開發與驗證;Development and Validation of a Virtual Reality based training System for Autism Spectrum Disorder
Authors:	胡琇媛;Hu, Hsiu-Yuan
Contributors:	資訊工程學系
Keywords:	自閉症類群障礙;虛擬實境;多模態傳感器;眼球追蹤;數位醫療;共享式注意力;腦電;機器學習;社交行為;Autism spectrum disorder (ASD);virtual reality (VR);Multi-sensor;Eye tracking;Joint attention (JA);Digital Health;electroencephalography (EEG);Machine Learning (ML);Social skills
Date:	2024-07-29
Issue Date:	2024-10-09 17:07:52 (UTC+8)
Publisher:	國立中央大學
Abstract:	自閉症譜系障礙（Autism Spectrum Disorder, ASD）是一種複雜的神經發育障礙，許多ASD患者在注意力和社交方面存在缺陷。本文介紹了一個基於自閉症診斷觀察量表（Autism Diagnostic Observation Schedule, ADOS）設計的社交訓練模組，透過虛擬實境（VR）技術和多模態神經感測技術，有效提高自閉症兒童的參與度。透過不同難易度的設計，使患者能夠重複練習，逐漸克服對人群的恐懼，願意嘗試進行社交互動。該模組採用腦電圖（EEG）、眼動追蹤、頭部轉動和語音辨識等數位感測設備，在訓練過程中不斷收集數據。這些數據經過切割與統計分析，結合任務表現、臨床量表和神經生理數據，觀察患者在八週訓練中的改善情況。通過治療前後的評估模組進行相關分析，驗證社交訓練系統的有效性，並更深入了解患者症狀的改善程度，從而幫助臨床醫生制定更有效的治療方案。此外，本研究可以降低對人力醫師的需求和成本，增加偏遠地區獲得有效治療的可能性。事實證明，該模組在幫助臨床決策、改善ASD患者的症狀和治療方面具有顯著成效。;Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, with many individuals exhibiting deficits in attention and social interaction. This paper introduces a social training module designed based on the Autism Diagnostic Observation Schedule (ADOS), utilizing virtual reality (VR) technology and multimodal neuro-sensing techniques to effectively increase engagement in children with autism.The module includes four tasks: ball throwing and catching, card games, block stacking, and Monopoly, each targeting different social skills. Each task is divided into beginner and advanced levels, allowing patients to practice repeatedly with varying degrees of difficulty, gradually overcoming their fear of social interactions and becoming more willing to engage.This module employs digital sensing devices such as electroencephalography (EEG), eye tracking, head movement tracking, and voice recognition. Data collected during training are continuously segmented and statistically analyzed, combined with task performance, clinical scales, and neurophysiological data to observe improvements over an eight-week training period. Pre- and post-treatment evaluations verify the effectiveness of the social training system, providing deeper insights into symptom improvement, thereby aiding clinicians in making more informed treatment decisions.Furthermore, this research can reduce the need for human therapists and associated costs, increasing the accessibility of effective treatments in rural areas. The results demonstrate that this module is highly effective in assisting clinical decision-making and improving symptoms and treatment outcomes for individuals with ASD.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	41	View/Open

社群 sharing

Loading...