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研究生: 吳宥橙
Wu, Yu-Cheng
論文名稱: 使用深度學習促進電腦使用者之眼睛保健
Using Deep Learning to Improve Vision Health Care of Computer Users
指導教授: 侯廷偉
Hou, Ting-Wei
學位類別: 碩士
Master
系所名稱: 工學院 - 工程科學系碩士在職專班
Department of Engineering Science (on the job class)
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 48
中文關鍵詞: 電腦工作者數位眼疲勞電腦視覺綜合症坐姿深度學習
外文關鍵詞: digital eye strain, computer vision syndrome, sitting posture, deep learning
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  • 根據研究指出,頭部長時間處於不當姿勢,將導致肩頸僵硬,而久坐對於腰椎、眼睛的不適,有很大的影響,另外,導致電腦視覺綜合症(Computer vision syndrome, CVS),或數位眼疲勞(digital eye strain, DES)的原因,可能包含了光線不好、坐姿不良、觀看距離不當等因素,美國眼科學會建議讓眼睛定期休息、有意識的多眨眼、調整電腦位置等方式,來緩解眼睛疲勞。
    因此,本研究蒐集了人體工學的建議坐姿,以及眼科醫師對於如何正確使用電腦的建議,整理出需求,以提醒使用者。本研究使用筆電鏡頭,採用深度學習的方法,偵測使用者的頭部姿勢、使用時間、距離以及眼睛注視的方向,以偵測使用者是否不當使用電腦,也偵測眨眼頻率,用以提醒使用者適時休息或眨眼來滋潤眼睛。

    According to research findings, maintaining an improper posture of the head for prolonged periods can lead to stiffness in the neck and shoulders. Prolonged sitting also significantly affects the discomfort in the lumbar spine and eyes. Additionally, the causes of Computer Vision Syndrome (CVS) or Digital Eye Strain (DES) may encompass factors such as poor lighting, poor posture, and improper viewing distance. The American Academy of Ophthalmology recommends practices like regular eye breaks, conscious blinking, and adjusting computer positions to alleviate eye fatigue.
    Therefore, this study collected ergonomic recommendations for proper sitting posture and advices from ophthalmologists on correct computer usage. These insights were organized to serve as reminders for users. The study employed a laptop camera and employed deep learning techniques to detect users' head posture, usage time, distance, and the direction of their gaze. This allowed for the identification of improper computer usage and the measurement of blink frequency to prompt users to take breaks or blink more frequently to refresh their eyes.

    摘要 I Extended Abstract II 誌謝 XIX 目錄 XX 表目錄 XXII 圖目錄 XXIII 第壹章 緒論 1 第一節 研究動機與背景 1 第二節 研究目的及目標 3 第三節 貢獻 3 第四節 相關研究 4 第五節 論文架構 6 第貳章 文獻探討 7 第一節 CVS介紹與症狀 7 第二節 造成CVS的因素 9 第三節 人體工學 11 第四節 深度學習與CNN簡介 12 第五節 Key point偵測介紹 15 第六節 Head pose estimation方法介紹 17 第參章 系統流程與架構 18 第一節 系統流程 18 第二節 矩形檢測 21 第三節 相機焦距與距離偵測 23 第四節 人臉辨識 24 第五節 Head pose estimation模型架構分析 25 第六節 眼睛相關的偵測方法 26 第肆章 實驗結果 28 第一節 矩形偵測結果 28 第二節 距離偵測結果 29 第三節 久坐偵測 30 第四節 歪頭偵測結果 31 第五節 眼睛相關的偵測結果 33 第六節 其他功能 35 第七節 與他篇效能比較 37 第八節 使用滿意度調查 38 第伍章 結論與展望 40 第一節 結論 40 第二節 展望 41 參考文獻 42

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