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系統識別號 U0026-2904201915050100
論文名稱(中文) 開發一套中風病人復健評估系統及生物回饋訓練
論文名稱(英文) Development of a rehabilitation assessment system and biofeedback training for the stroke patients
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 張雅婷
研究生(英文) Ya-Ting Chang
學號 P86064085
學位類別 碩士
語文別 英文
論文頁數 43頁
口試委員 指導教授-陳天送
口試委員-陳培展
口試委員-閻漢琳
口試委員-陳啓杰
口試委員-黃至誠
中文關鍵字 中風病人  慣性測量元件  穿戴式裝置  Android  步態分析 
英文關鍵字 stroke patient  inertial measurement component  wearable device  Android  gait analysis 
學科別分類
中文摘要 隨著現今社會大眾生活作息的轉變,慢性病的案例與日俱增,中風在台灣的盛行率除了逐年升高,也出現年輕化的趨勢。而中風後產生的後遺症更是不勝枚舉,其中約有88%的患者在中風後表現出不同嚴重程度的偏癱症狀,患者可以藉由長期的復健療程來改善病況。而復健的成效可以藉著雙腳間的步態特徵來評估。但現今大多的步態分析系統只能在生物力學實驗室或醫療相關機構裡才得以進行,由於器材昂貴又占空間及所需時間長的原因已被證實這並不適用於居家應用,因此開發一套簡易的復健評估系統成為重要的臨床議題。
本研究將兩個慣性測量元件分別固定於雙腳的腳背上,並擷取出慣性元件中的加速度與角速度做為量化步態特徵的依據。由於加速度計與陀螺儀本身的限制,本研究透過感測器融合的技術,讓兩個元件互相進行校正,並整合研究中所開發的演算法盡可能估算出正確的步態特徵。為了讓患者方便審視及評估受測結果,本研究於Android平台上進行系統的開發,將慣性測量元件的原始資料進行計算後藉由藍牙4.0的技術, 以無線傳輸的方式傳遞到智慧型手機上,讓患者可以在手機應用程式上即時觀看行走的步態量化狀況,也得以在行走後得知經過計算後的步態分析。
本研究設計了一套實驗,受測者會被要求分別在雙腳正常和單腳異常步態的模擬下,採取自行選擇且舒適自然的速度進行直線步行,並讓受測者藉由智慧型手機即時觀看量化的步態特徵,並在實驗結束後獲得相關的步態特徵的比值,藉此來評估受測者步行對步態特徵的影響。由實驗結果可得知,對於雙腳正常的受測者而言,在雙腳的步態特徵中呈現約為1.0的對稱比例。而對於單腳步行困難的受測者,其對稱比例則為大於1.0或小於1.0,代表任何一隻腳在某些步態特徵中會明顯不同於另一隻腳。由結果可得知,正常步態及異常步態雙腳間的步態特徵的相關性也與實驗前假設的一致。
英文摘要 Due to the changes of lifestyle today, the number of chronic diseases is increasing day by day. The prevalence of stroke in Taiwan has increased year by year, and the age has also become younger. The aftereffects of stroke are numerous, and about 88% of patients show hemiplegia with different severity after stroke. Patients can improve their condition through long-term rehabilitation. The effectiveness of rehabilitation can be assessed by the gait characteristics between the feet. However, most of today's gait analysis systems can only be carried out in biomechanical laboratories or medical related institutions. It has been proven that the equipment is expensive and takes up a long time and is not suitable for home care applications. A simple rehabilitation assessment system has become an important clinical issue.
In this study, two inertial measurement components are respectively fixed on the instep of the two feet, then the acceleration and angular velocity in the inertial component are extracted as the basis for quantifying the gait characteristics. Due to the limitations of the accelerometer and the gyroscope itself, this study allows the two components to be calibrated to each other through the sensor fusion technique, and integrates the algorithms developed in the study to estimate the correct gait characteristics as much as possible. In order to allow patients to easily review and evaluate the test results, this study develops the system on the Android platform, calculates the original data of the inertial measurement component, and transmits it to the smart phone by wireless transmission through the technology of Bluetooth 4.0. To allow the patient to instantly view the gait quantification of walking on the smart phone's app, and to learn the calculated gait analysis after walking.
This study designed a set of experiments which subjects are required to perform a straight walk at a comfortable, natural speed under the simulation of normal two-legged gait and abnormal one-leg gait. Subjects are also allowed to see the quantitative gait characteristics on a smart phone, instantly. The ratio of the relevant gait characteristics was obtained at the end of the experiment to assess the effect of the subject's walking on the gait characteristics. It can be known from the experimental results that for a normal subject with both feet, the symmetrical ratio presented in the gait characteristics of both feet is about 1. For subjects with difficulty walking on one foot, the symmetrical ratio is greater than 1 or less than 1, indicating that any one foot will be significantly different from the other in some gait characteristics. It can be seen from the results that the correlation of gait characteristics between the normal and abnormal gait feet is consistent with the pre-experiment hypothesis.
論文目次 摘要 I
Abstract III
致謝 V
List of Figures VII
List of Tables IX
Chapter 1 Introduction 1
1.1 Background of Stroke 1
1.1.1 The conditions of Post-Stroke 2
1.2 Gait Analysis 5
1.2.1 Normal Gait 5
1.2.2 Hemiplegic Gait 8
1.3 Devices of Gait Assessment 9
1.3.1 Visual Gait Analysis: Camera and Video 10
1.3.2 Motion Capture System 11
1.3.3 Force Sensor System 13
1.3.4 Inertial Measurement Units 14
1.4 Literature Review 17
1.5 Motivation and Aims 20
Chapter 2 Material and Methods 21
2.1 Experimental Design 21
2.2 System Architecture 22
2.2.1 Inertial Measurement Unit 22
2.2.2 Bluetooth Low Energy 23
2.2.3 Mobile Application 26
2.3 Algorithm 27
2.3.1 Sensor fusion 27
2.3.2 Remove gravity 28
Chapter 3 Results and Discussion 29
3.1 Mobile Application User Interface 29
3.2 Gait characteristics analysis 31
3.3 Influence of Characteristics on Abnormal Gait 34
Chapter 4 Conclusion 40
References 41
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