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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/82321


    題名: 結合虛擬實境與多模態神經行為感測的注意力不足/過動症之智慧型輔助評估方法研究;A Study Integrating Vi Rtual Reality with Multi-Model Neuro-Sensing for Adhd’S Intelligent Attention Assessment
    作者: 葉士青;陳牧宏
    貢獻者: 國立中央大學資訊工程學系
    關鍵詞: 虛擬實境;注意力;過動;人工智慧;診斷評估;神經行為;可穿戴感測;Virtual Reality;Attention;Hyperactivity;Artificial Intelligence;Assessment;Diagnose;Neuro Behavior;Wearable Sensing
    日期: 2020-01-13
    上傳時間: 2020-01-13 14:40:20 (UTC+8)
    出版者: 科技部
    摘要: 注意力缺陷多動障礙(ADHD)是兒童期常見的神經行為障礙,由於對生活品質的長期負面影響和自發緩解的困難,及時診斷和治療尤為重要。現行的評估工具主要有量表和神經心理測驗,填寫量表時帶有主觀性,不容易量化兒童的行為,神經心理測驗通常為透過電腦進行的測驗,仍然存在生態有效性的問題,所以,ADHD的診斷和評估仍然面臨許多的挑戰。本研究運用VR技術,結合可穿戴神經行為感測技術,包括腦波、眼球軌跡追蹤、頭部轉動以及肢體動作,研發以台灣兒童教室為背景的注意力測試系統:VR虛擬教室,內置測試任務涵蓋選擇性注意力、持續性注意力以及執行功能。然後,運用人工智慧的機器學習學習方法,整合測試任務表現(遺漏錯誤率、替代性錯誤率、反應時間)、神經行為(腦波、眼球軌跡追蹤、頭部轉動、肢體動作)等多模態數據以及多個評估量表(CONNERS,SNAP-IV,Weiss’s),建立注意力缺陷以及多動障礙的自動化評估/輔助診斷模型。另外,將進一步探索教室環境不同形態的干擾源,包括視覺干擾、聽覺干擾、嗅覺干擾、綜合性干擾等,對於兒童注意力影響的量化分析。 ;Attention deficit hyperactivity disorder (ADHD) is a common neurobehavioral disorder in childhood. Due to the long-term negative effects on quality of life and the difficulty of spontaneous remission, early diagnosis and treatment are particularly important. The current assessment tools mainly include scales and neuropsychological tests. Scales are subjective and are not easy to quantify children's behaviors. Neuropsychological tests are usually performed by computers. There are still problems of ecological validity. Therefore, the diagnosis or evaluation of ADHD still faces many challenges. This study integrates VR technology with wearable neurobehavioral sensing technology, including brain waves, eye trajectory tracking, head rotation and limb movements, to develop an attention assessment system. Based on the Taiwanese children's classroom, a virtual classroom is developed. Tasks, in regard to selective attention, continuous attention, and executive function, are embedded into the virtual classroom. Machine learning methods are used to perform multi-modal analysis on the data of task performance (missing error rate, alternative error rate, response time), neural behavior (brain wave, eye trajectory tracking, head rotation, limb movement) and assessment scales (CONNERS, SNAP-IV, Weiss's) in order to establish an automated assessment/ diagnostic model for attention deficit and hyperactivity disorder. In addition, we will further explore the impact of a variety of distractions, including visual interference, auditory interference, smell interference, on the distraction of children's attention.
    關聯: 財團法人國家實驗研究院科技政策研究與資訊中心
    顯示於類別:[資訊工程學系] 研究計畫

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