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研究生:陳思穎
研究生(外文):Szu-Ying Chen
論文名稱:基於區間第二型模糊理論之異常行走步態分析
論文名稱(外文):Abnormal gait analysis based on interval type-2 fuzzy theory
指導教授:黃有評黃有評引用關係
口試委員:劉珣瑛張玉山曾傳蘆姚立德
口試日期:2013-07-22
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:58
中文關鍵詞:第二型模糊理論步態判別智慧型手機三軸加速度計
外文關鍵詞:interval type-2 fuzzy theorygait analysissmart phonetriaxial accelerometer
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帕金森氏症、中風及癡呆症並列為老年人三大疾病,帕金森氏症主要症狀有四肢無力、站立困難、動作困難、步伐短促,晚期患者更可能出現四肢癱瘓的情形。中風主要症狀為手腳或臉部突然發麻或無力,尤其是身體的單側,突然舉步困難、覺得昏眩、失去平衡或協調。本系統欲針對這些疾病在步伐上異常之症狀來做檢測,判別受測者在行走時身體之平衡與協調能力是否為正常,使用這些較具代表性之特徵來做步態是否異常之判別。先前的許多研究都是針對使用者之日常活動進行判別,且需要配戴許多感測器,但是較缺乏便利性,因此本研究使用智慧型手機內建之三軸加速度計,設計一個結合計步器與異常步態判別之第二型模糊推論系統,針對使用者整個行走期間之身體平均傾斜角度以及身體平均傾斜角度變化來進行步態之分析與判別,並使用第二型模糊理論來做推論,以考慮各種不明確性的影響來得到較可靠之結果。本系統之判斷結果分為正常、異常,這些結果會儲存在資料庫中讓醫療人員可以做後續的觀察與分析。本研究針對正常行走和身體傾斜狀態之行走進行各種模擬與測試,並觀察將裝置配戴於腰間之不同側對結果之影響,實驗結果顯示,正常行走判別之準確率為95%,而異常行走判別之準確率為85%,驗證本研究所提出之步態異常判別系統的可行性。

The three most commonly diseases of elderly include Parkinson''s disease, Stroke and Dementia. The main symptoms of Parkinson’s disease are weakness of the limbs, difficult to stand or move and fast-short pace. In advanced stage, it may even become quadriplegia. The main symptoms of Stroke are limbs, face sudden numb or weakness, which always happened on one side of the body, sudden trouble walking, feeling dizziness, and loss of balance or coordination. Our system is devised to detect the symptoms of abnormal pace, identify whether the subject''s body balance and coordination while walking are normal. Many previous studies are aimed at daily activity identification. However, it is inconvenient as they require some sensors to be attached on the body. Therefore, our study chose to use the triaxial accelerometer built-in smart phone to design a system that combined the pedometer with abnormal gait analysis systems based on interval type-2 fuzzy theory. Using two parameters, average body tilt angle and average difference of body tilt angle, to analyze and identify user’s gait. The type-2 fuzzy theory considers the impact of a variety of ambiguity to get more reliable results than type-1 fuzzy theory, and this is the reason why we design our system based on the type-2 fuzzy theory. The determination result of this system is divided into normal and abnormal. The results will be stored in the database so that medical professionals can do the follow-up observation and analysis. In this study, users wear the mobile device on the waist, and simulate the situation of normal walking and tilt the body during walking. We also observe the effect of wearing device on the opposite side of waist. The experimental results show that the accuracy of normal walking identification is about 95%, and the accuracy of abnormal walking identification is about 85%. The results verify that the proposed abnormal gait analysis systems are effective.

摘 要 i
ABSTRACT ii
致 謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究方法 3
1.4 論文架構 3
第二章 相關技術及運用探討 4
2.1 模糊理論 4
2.1.1 模糊集合 5
2.1.2 模糊推論方法 5
2.1.3 解模糊方法 7
2.2 第二型模糊邏輯系統 8
2.2.1 區間第二型模糊邏輯系統 9
2.2.2 KM演算法 10
2.2.3 第二型解模糊方法 12
2.3 微機電系統(MEMS) 13
2.3.1 加速度感測器原理 13
2.3.2 加速度感測器應用 15
2.4 iOS作業系統 17
2.4.1 系統架構 17
2.4.2 程式狀態 18
2.4.3 生命週期 19
2.5 步態分析 23
第三章 步態異常判別系統架構與設計 26
3.1 系統架構 26
3.1.1 硬體架構 26
3.1.2軟體架構 30
3.2系統流程與設計 31
3.2.1系統執行流程 31
3.2.2 步態異常判別系統之設計 32
3.3開發環境 43
3.3.1硬體版本 43
3.3.2軟體版本 43
第四章 實驗結果與分析 45
4.1 實驗環境 45
4.2 系統介面 46
4.2.1 手機介面 46
4.2.2 資料表設計 48
4.3 實驗結果 48
第五章 結論與未來展望 53
5.1 結論 53
5.2 未來展望 54
參考文獻 55


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