博碩士論文 102522077 詳細資訊




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姓名 呂哲光(Jhe-guang Lyu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 中風復健遊戲之上肢運動指標設計與成效評估
(The Design of Motion Indexes and Performance Analysis of Stroke Rehabilitation Games)
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摘要(中) 根據衛生署統計過去十幾年來醫療數據發現中風是目前台灣主要疾病之一,中風的發生主要是因為腦部受到損害而使腦神經衰退,近而造成身體活動不方便,也影響到日常生活狀況。所以最近中風的議題不斷被醫學界討論、研究,企圖尋找良好、有效率的醫療復健方式以及能反映出與中風病患身體狀況有相關性的臨床評估、復健成效。
本研究主要針對中風上肢復健遊戲設計各項運動指標,希望透過這些指標能顯示出病患目前動作能力、反應程度,並且分析運動指標是否有明顯效果。另外,目前醫院流行的傳統評估量表: FMA、TEMPA、WMFT有些缺點,不是每一次運動復健都會進行傳統臨床評估,只有第一次復健、最後一次復健以及復健完經過一個月後追蹤評估,而且每次都花費很長時間。還有病患想要知道自己與其他人的差別、在有玩過此系統的人群中屬於哪種等級程度,因此建立新型評估方式希望能減輕物理治療師負擔、滿足病人需求。
實驗結果顯示,部分運動指標對於中風復健遊戲有相關的影響性,透過運動評估與統計分析數據能了解病患有明確的進步程度以及與傳統評估量表分數有相關性,另外以類神經網路、支持向量機驗證新型評估方式的建立是有可行性。
摘要(英) According to the past decades of medical data, Ministry of Health and Welfare has found that stroke was one of Taiwan′s major diseases. The occurrence of stroke because of brain injury causing nerve cells are declined. Stroke seriously affects physical movement and the quality of daily life. So the medical profession have discussed and researched the issues of stroke in recent years, trying to find some well efficiency of medical rehabilitations, and performance analysis which can reflect conditions of the stroke patients.
This study is focused on the design of motion indexes which are about rehabilitation systems of upper limbs, hoping to indicating abilities of stroke patients at present and wondering the performance analysis of these systems whether is useful or not. In addition, the hospital use currently traditional assessment scales that have some problems and need to spend long time. Some stroke patients also want to know what differences with other people when playing rehabilitation systems. Therefore, establishing new medical assessment methods to solve these problems, reduce burdens of Physiotherapist and fulfill expectations of stroke patient.
The results of this study shows that part of the motion indexes for assessment method have considerable influences and stroke patients also have definite progress. Thus, using neural network and SVM to verify new medical assessment methods that is a certain degree of feasibility.

關鍵字(中) ★ 中風疾病
★ 復健
★ 運動指標
★ 成效評估
★ 虛擬實境
關鍵字(英) ★ stroke
★ rehabilitation
★ motion indexes
★ performance analysis
★ virtual reality
論文目次 目錄
中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 vi
表目錄 viii
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 7
1-3 研究目的 9
第二章 文獻回顧 12
2-1 現代科技結合中風復健相關研究 12
2-2 臨床評估相關研究 15
2-3 類神經網路、支持向量機應用於復健相關研究 16
第三章 研究方法 18
3-1 系統設計 18
3-1-1 籃球遊戲 19
3-1-2 拋接球遊戲 22
3-1-3 擦拭玻璃遊戲 24
3-2 實驗步驟與流程 27
3-2-1 病人收案標準 27
3-2-2 實驗流程 29
3-2-3 傳統評估量表 29
3-3 資料測量與分析 30
3-3-1 運動指標 30
3-4 分析方法 43
3-4-1 分析復健成效、驗證新型運動指標 43
3-4-2 K-means群聚分析 44
3-4-3 多層感知機(Multilayer Perceptron, MLP) 45
3-4-4 放射狀基底函數網路(radial basis function network, RBFN) 46
3-4-5 支持向量機(Support Vector Machine,SVM) 47
3-5 分析步驟流程 47
第四章 分析結果與討論 49
4-1 籃球遊戲 49
4-1-1 無母數統計 49
4-1-2 與傳統評估量表「前後測」相關分析 51
4-1-3 與傳統評估量表「後測-前測」相關分析 54
4-1-4 群聚分析 56
4-1-5 對K-Means群聚做相關分析 57
4-1-6 比較各種分類器之間差異性 59
4-2 拋接球遊戲 62
4-2-1 無母數統計 62
4-2-2 與傳統評估量表「前後測」相關分析 65
4-2-3 與傳統評估量表「後測-前測」相關分析 69
4-2-4 群聚分析 71
4-2-5 對K-Means群聚做相關分析 73
4-2-6 比較各種分類器之間差異性 75
4-3 擦拭玻璃遊戲 78
4-3-1 無母數統計 78
4-3-2 與傳統評估量表「前後測」相關分析 80
4-3-3 與傳統評估量表「後測-前測」相關分析 82
4-3-4 群聚分析 83
4-3-5 對K-Means群聚做相關分析 85
4-3-6 比較各種分類器之間差異性 87
第五章 結論 94
參考文獻 95


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指導教授 蘇木春、葉士青(Mu-Chun Su Shih-Ching Yeh) 審核日期 2015-7-30
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