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研究生:蕭詠田
研究生(外文):XIAO, YONG-TIAN
論文名稱:基於三軸加速度計步態特徵的老年人衰弱篩查
論文名稱(外文):Frail Screening of Elderly Based on Gait Features in Triaxial Accelermetry
指導教授:王文楓王文楓引用關係
指導教授(外文):WANG, WAN-FONG
口試委員:王文楓林昭維黃胤傅
口試委員(外文):WANG, WAN-FONGLIN, JOU-WEIHUANG, YIN-FU
口試日期:2020-07-16
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊工程系
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:2020
畢業學年度:108
語文別:中文
論文頁數:48
中文關鍵詞:衰弱三軸加速度訊號分析處理機器學習
外文關鍵詞:Frailtytriaxial accelerationsignal analysissupport vector machine
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現今在臨床醫學上,判讀一位受試者是否具有身體的衰弱傾向時,通常都是透過問卷、量表、簡易身體數據等等來進行鑑定,而在不同問卷,其評分出來的標準皆為該問卷的主觀判讀,鮮少有相對應的實際身體數值於受試者身體狀況上,目前針對於此塊的研究領域仍少人涉略。
在本篇研究上,與專業護理人員配合,針對臺灣社區與醫院院區進行年長者衰弱資料蒐集,透過年長者之三軸加速度資訊進行訊號分析,提取各項特徵參數,將數據與特徵參數進行支援向量機進行人群分類學習,其分類標準以問卷篩檢與量表答案為主,將受試者依照標準分類,使支援向量機學習如何進行衰弱分類篩檢,達到問卷量表與身體數據的相互對應,使檢測結果可以更加準確。
透過以不同問卷作為分類標準,能夠有效使機器學習在判讀特徵時,能夠更完整性的妥善分類受試者,不同的問卷在判斷受試者狀況時,都會有獨特的分類依據,透過這些來補足受試者在判定衰弱時的正確定位。

Nowadays, in the clinical medicine, we usually use questionnaire, scale or simple body data to interpret the subject has frailty tendency or not. In different questionnaires, its standards are come from its subjective interpretation. There are few questionnaires which can correspond the subject`s body situation. Currently, few people are involved in the research area of this area.
In this search, we cooperate with professional nursing staff. We collect information on the elders` frailty in Taiwan`s communities and hospitals. Extract each feature parameter in triaxial acceleration by signal analysis. Putting data and feature parameters into the support vector machine to learn crowd classification. The classification criteria are based on questionnaire screening and scale answers. Classify the subjects according to the standard, and enable the support vector machine to learn how to conduct the classification screening for frailty. It achieves the correspondence between the questionnaire scale and the body data. It will make the test results can be more accurate.
By using different questionnaires as classification criteria, the machine learning can effectively classify the subjects more completely when interpreting the features. Different questionnaires will have unique classification basis when judging the condition of the subjects. Complement the correct positioning of the subjects when determining frailty.

摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vii
一、介紹 1
1.1研究背景 1
1.2相關研究 1
1.3研究動機及目的 2
1.4問題描述和重要性 2
二、材料 3
2.1機器學習概述 3
2.1.1 KNN演算法(K nearest neighbor ,近鄰演算法) 3
2.1.2 SVM(Support vector machine,支援向量機 ) 3
2.2步態概述 3
2.3衰弱概述 4
2.4實驗器具、材料 6
2.4.1自製三軸加速度感測模組 7
2.5實驗相關問卷、指標、工具 9
2.5.1自擬式結構問卷 10
2.5.2察爾森共病症指標(Charlson Comorbidity Index,CCI) 10
2.5.3營養不良篩檢工具(Malnutrition Universal Screening Tool,MUST) 11
2.5.4日常生活功能量表(Activities of Daily Living,ADL) 12
2.5.5跌倒功效量表(Falls Efficacy Scale,FES) 12
2.5.6臨床衰弱量表(Clinical Frailty Scale,CFS) 12
2.5.7簡易認知功能評估量表(Short Portable Mental Status Questionnaire,SPMSQ) 13
2.5.8高齡者衰弱量表(Kihon Checklist,KCL) 14
2.6實驗相關測驗方式 14
2.6.1計時起走測試(Time up and Go,TUG) 14
2.6.2十公尺行走測試(Timed 10-Meter Walk Test,10MWT) 15
2.7檢測流程 15
三、方法 18
3.1訊號前置處理 18
3.2訊號計算處理 20
3.3訊號特徵設計、提取方法 22
3.3.1平均速率 23
3.3.2平均步頻 23
3.3.3平均步伐站立百分比 23
3.3.4平均步伐擺盪百分比 24
3.3.5左右腳步伐相關性 24
3.4機器學習模型 27
四、結果 28
4.1受試者 28
4.2問卷最佳特徵篩檢 29
4.3步態最佳特徵篩檢 32
4.4綜合特徵 34
五、討論 37
參考文獻 38

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