閱讀障礙，是指在閱讀和寫作方面有困難，但沒有明顯的智力缺陷，也沒有 相關的視覺或聽覺障礙的病症。症狀的嚴重程度因文化和個人因素而異。其他可 能的症狀包括拼寫單詞困難，朗讀速度較慢，以及無法在頭腦中說出單詞。然而， 要完全診斷出閱讀障礙是很困難的，因為當事人必須首先證明學習成績低下，而 文化剝奪是導致成績低下的一種環境學習形式。 因此，有必要提供一種快速有效的診斷輔助和學習幫助。隨著虛擬現實、眼 動和機器學習的快速發展，我們建立了一個虛擬閱讀環境，並從收集到的眼動生 理信息中計算出三個特徵集，包括眼動特徵、句子和視覺顯著圖。我們還提出了 一個融合模型，整合了多個機器學習模型，通過評估相關數據來評估閱讀障礙， 並利用從用戶反應中獲得的生理數據，建立一個基於真實數據的更客觀的自動評 估模型。 ;Dyslexia, or Reading Disorder, is a condition in which a person has difficulty reading and writing without significant intellectual deficits and without associated visual or auditory impairments. The severity of symptoms varies depending on cultural and personal factors. Other possible symptoms include difficulty spelling words, slower reading aloud, and inability to say words in the head. However, it is difficult to fully diagnose dyslexia because the person must first demonstrate low academic achievement, and cultural deprivation is a form of environmental learning that causes low achievement. Therefore, there is a need to provide a fast and effective diagnostic aid and learning aid. With the rapid development of virtual reality, eye-movement and machine learning, we build a virtual reading environment and compute three feature sets, including eye-movement features, word vectors and saliency maps, from the collected eye-movement physiological information. We also propose a fusion model that integrates several machine learning models to assess dyslexia by evaluating relevant data, and build a more objective automatic assessment model based on real data by using physiological data obtained from users′ responses are able to provide a more effective system in Methamphetamine treatment.
