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研究生:黃建華
研究生(外文):Chien-Hua Huang
論文名稱:老人跌倒預防:視覺刺激影響及視覺化分析跌倒風險
論文名稱(外文):Fall Prevention of the Elderly Adult : The Effect of theVisual Stimulation and Visual Analysis of Fall Risk
指導教授:孫天龍孫天龍引用關係
指導教授(外文):Tien-Lung Sun
口試委員:蘇傳軍蔡篤銘裴駿賴仲亮
口試委員(外文):Chuan-Jun SuDu-Ming TsaiChun PeiChun-Liang Lau
口試日期:2018-1-5
學位類別:博士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:91
中文關鍵詞:虛擬實境資料視覺化機器學習主成分分析線性判別分析
外文關鍵詞:Virtual RealityInformation VisualizationPrinciple Component AnalysisFisher's Discriminant Analysis
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本論文共分為兩個主題針對老人跌倒預防方法進行研究。
第一個研究主題針對臨床復健應用的體感控制虛擬實境遊戲,考慮其必須個人化的虛擬場景設計,發現視野及抬腿時間可以做為個人化調整參數,但是某些參數則可能需要更正。
第二個研究主題主要探討以視覺化分析方式針對社區老人跌倒風險評估資料進行分類,一共使用四種分類方法,使用老人跌倒資訊包括三公尺行走秒數及簡易伯格平衡量表分數這兩個連續屬性的特徵值建立X-Y散布圖,然後以文獻建議的閾值(Threshold)作為第一種分類方法;以統計盒鬚圖(boxplot)的上下界異常值為第二種分類方法;以線性回歸之趨勢線進行視覺化分析作為第三種分類方法,接下來以主成分分析(PCA)降維方法處理所蒐集到的四個老人跌倒特徵值後進行視覺化分析做為第四種分類方法,結果顯示方法三有最高的AUC(Area Under Curve)為0.87(95%信賴區間為0.80-0.94);方法一次之,其AUC為0.81(95%信賴區間為0.71-0.90);方法二之AUC為0.80(95%信賴區間為0.70-0.90),方法四的AUC最低為0.78(95%信賴區間為0.68-0.88)。
互動式資料視覺化為臨床專家提供了一個了解數據意義的平台。通過使用數據過濾,分組和其他數據分析等技術,信息可以高效地呈現,不過到目前為止,尚未有針對老人跌倒風險完美的預測精確評估工具。
This thesis is divided into two main topics to study the prevention of falls in the elderly.
The first study focused on somatosensory virtual reality games for clinical recuperation, considering the need for a personalized virtual scene design, finding field of view and leg-lifting time as personal adjustment parameters, but some parameters may require correct.
The second research theme focuses on the visual analysis of risk of the elderly community to fall risk assessment data classification, using a total of four classification methods, the use of information on the elderly fell to include the three-meter walking seconds and the simple Berger Balance Scale scores of these two Then, the Threshold as the first classification method was proposed based on the eigenvalues ​​of continuous attributes. The second classification was based on the upper and lower bounds of the boxplot, Visual analysis of the trend line as the third classification method, and then use PCA dimensionality reduction method to deal with the collected four elderly fall characteristic value visual analysis as the fourth classification method, the result For the three methods, the highest AUC (Area Under Curve) was 0.87 (95% confidence interval 0.80-0.94); the first time, the AUC was 0.81 (95% confidence interval 0.71-0.90); the AUC of the second method was 0.80 (95% confidence interval 0.70-0.90). The lowest AUC for method four was 0.78 (95% confidence interval 0.68-0.88).
The visualization of interactive data provides a platform for clinical experts to understand the meaning of data. Information can be efficiently presented using technologies such as data filtering, grouping and other data analysis, but so far no accurate forecasting accuracy tool has been put in place to address the risk of falling old.
目錄
圖目錄
表目錄
第一章 緒論
1.1 研究背景
1.2 研究動機
1.3 研究目的
第二章 文獻探討
2.1虛擬實境場景對姿勢控制影響
2.2視覺化分析應用於風險評估
第三章 視覺刺激對姿勢控制影響之研究
3.1 研究背景
3.2研究之理論架構
3.3 實驗設計
3.4 實驗對象
3.5 實驗設備
3.6 實驗流程
3.7 實驗結果
3.8 討論
第四章 視覺化分析老人跌倒風險之研究
4.1 研究背景
4.2 研究架構
4.3 實驗流程與材料
4.4 視覺化分析工具
4.5 原始資料維度視覺化分析老人跌倒風險
4.6 以主成分分析降維處理後進行視覺化跌倒風險分析
4.7 以費雪判別分析找出最佳投影軸進行視覺化跌倒風險分析
4.8結果與討論
4.8.1原始資料維度視覺化之討論
4.8.2以主成分分析進行視覺化之討論
4.8.3以費雪判別分析進行視覺化之討論
第五章 討論與結論
5.1研究限制
5.2臨床應用
5.3未來研究方向
參考文獻
附件一:人體試驗委員會同意書
附件二:柏格氏平衡量表(Berg Balance Test)
附件三:人體實驗計劃受測者同意書
附件四﹕主成分分析3D視覺化
附件五﹕SNQ國家品質標章證書
附件六﹕“機器學習”應用於腦中風病人返家後跌倒風險預測
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