| 摘要: | 自閉症譜系障礙 (ASD) 是一種複雜的神經發育障礙，會嚴重影響兒童的生活，尤 其是在社交、溝通和行為方面。早期診斷和干預對於改善自閉症患者的預後至關重 要。本文提出了一個評估模組，其靈感來自廣受認可的自閉症診斷觀察表 (ADOS) 評 估工具，該工具利用虛擬現實 (VR) 技術和多模態神經數據來全面評估 ASD 症狀。 評估模塊包括三個階段：拼圖遊戲、氣球遊戲和蛋糕遊戲。每一個階段都針對 ASD 症狀的特定方面，例如凝視持續時間、共同住注意力和談話技巧。為了捕獲不同的神 經和行為數據，評估模組結合了多模態傳感設備，包括腦電 (EEG)、眼動追踪和語音 辨識。這些設備提供了對與 ASD 相關的潛在生理特徵，增強了 ASD 評估中常用的 傳統觀察方法。在評估過程中，多模態傳感設備不斷地收集來自於受測者的生理數 據，腦電圖用來記錄大腦活動，眼動追踪用來監測凝視模式，語音辨識用來分析對話 技巧。最後，對收集到的數據進行綜合統計分析，結合臨床量表、任務表現分數和多 模態神經數據。這種綜合分析方法可以進一步了解受測者表現出來有關 ASD 的特徵， 使臨床醫生和研究人員能夠做出更明智的診斷並製定個性化的治療計劃。評估模組展 示了其在支持臨床決策、促進準確的 ASD 診斷和優化治療結果方面的有效性。;Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that significantly affects children′s lives, particularly in the areas of social interaction, communication, and behavior. Early diagnosis and intervention are crucial for improving outcomes in individuals with ASD. This paper presents an Evaluation Module inspired by the widely recognized Autism Diagnostic Observation Schedule (ADOS) assessment tool, which utilizes virtual reality (VR) technology and multi-modal neurosensing to comprehensively assess ASD symptoms. The Evaluation Module consists of three stages: the Puzzle Game, Balloon Game, and Cake Game. Each stage targets specific aspects of ASD symptoms, such as gaze duration, joint attention, and conversation skills. To capture diverse neural and behavioral data, the Evaluation Module incorporates digital sensing devices, including electroencephalography (EEG), eye-tracking, and speech recognition. These devices offer valuable insights into the underlying physiological processes associated with ASD, enhancing the traditional observational methods commonly used in ASD assessments. During the assessment, the digital sensing devices continuously collect data from the participants. EEG records brain activity, eye-tracking monitors gaze patterns, and speech recognition analyzes conversational skills.The collected data then undergoes comprehensive statistical analysis, combining clinical scales, task performance measures, and neurophysiological data. This integrated analysis approach provides a comprehensive understanding of the participants′ ASD symptoms, enabling clinicians and researchers to make more informed diagnoses and tailor individualized treatment plans. The Evaluation Module demonstrates its effectiveness in supporting clinical decision-making, facilitating accurate ASD diagnoses, and optimizing treatment outcomes. |
