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系統識別號 U0026-0309201422031900
論文名稱(中文) 運用基於慣性測量單元的動作捕捉系統發展自動化步態事件偵測方法
論文名稱(英文) Development of the automated gait event detection method using motion capture system based on inertial monitoring unit
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 102
學期 2
出版年 103
研究生(中文) 鍾季衡
研究生(英文) Chi-Heng Chung
學號 R76011090
學位類別 碩士
語文別 中文
論文頁數 68頁
口試委員 指導教授-林明毅
口試委員-王振興
口試委員-成戎珠
中文關鍵字 步態事件偵測  穿戴式裝置  支援向量回歸 
英文關鍵字 Gait event detection  Inertial monitoring unit  Support vector regression 
學科別分類
中文摘要 行走是人類生活中最基本的活動,步態分析是加以量化這些運動資訊來描述人行走時的狀態,在許多的生活應用、醫療照護應用上以及物理治療上,步態分析都是重要的議題。在步態分析中,步態事件的偵測是必要的過程。經由步態事件的偵測,研究者可將數個連續的步態週期切割成為單一的步態週期後,再進一步的進行步態分析。一般而言,如果沒有力感應設備的話便無法進行自動化的步態事件偵測,只能依靠事後人工的方式找出步態事件,這種透過人工的方式通常會造成主觀的偏差,而且當有大量的步態資料時,便會耗費許多時間。
目前被提出的步態事件偵測方法大多僅使用單一特徵指標作為判斷依據,缺乏足夠的穩定性,且在時間精確度上仍然有改進的空間。因此本研究期望結合支援向量回歸(Support Vector Regression, SVR) 方法,配合過去學者所發展之演算法以及運用多項特徵指標,提升步態事件偵測在偵測上的時間精確度。
本研究舉行步態資料蒐集實驗,招收 30 位受試者,男女各半。實驗穿著穿戴式儀器,蒐集於平地行走時,身體各個肢段的的運動資料,並同時以電子步道蒐集步態事件時間做為驗證。在實際進行方法驗證後,結果顯示在偵測 Initial Contact 事件上可以達到 12.34 ± 4.58 ms 的準確度,在偵測 Toe Off 事件上可以達到 16.11 ± 3.61 ms 的時間精確度,其結果優於其它以功能性演算法進行偵測的時間差異,證明以機器學習方法進行步態事件偵測能夠達到更佳的時間精確度。
英文摘要 Walking is the basic activity in human life and Gait Analysis is a theory to analyze human daily walking status. Gait event detection is a necessary step in Gait Analysis, through the gait event detection, researcher can divide continuous gait cycles into individual gait cycle then look more detail into each gait cycle. In this paper, we use inertial monitoring unit to collect 30 adults (15 male, 15 female) walking data and give a method using Support Vector Regression (SVR) to detect gait event.
Our gait event detection method result compared to gold standard GAITRite find the average absolute difference on initial contact detection is 14.78 ms ± 9.22 ms and 16.92 ms ± 5.14 ms on toe off event detection. Furthermore, the average absolute difference on stance phase is 21.33 ms ± 9.59ms and 23.7 ms ± 7.58ms on swing phase. The result shows more accurate than previous method.
論文目次 摘要 I
Extended Abstract II
誌謝 V
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與限制 4
1.4 研究流程與規劃 5
第二章 文獻探討 6
2.1 步態分析 6
2.2 步態事件偵測 8
2.2.1 資料蒐集階段 10
2.2.2 指標處理階段 13
2.2.3 事件偵測階段 14
2.3 使用儀器 MVN BIOMECH 探討 15
2.4 基於慣性測量單元的步態事件偵測演算法 18
2.4.1 以裝設於腰間的慣性測量單元進行偵測 18
2.4.2 以裝設於大腿的慣性測量單元進行偵測 20
2.4.3 以裝設於小腿的慣性測量單元進行偵測 22
2.4.4 以裝設於腳上的慣性測量單元進行偵測 23
2.5 類神經網路 24
2.6 Support Vector Machine (SVM) 26
2.7 Support Vector Regression (SVR) 31
2.8 小結 33
第三章 研究方法 34
3.1 步態資料蒐集 34
3.1.1 步態資料蒐集實驗流程 36
3.1.2 實驗設備 MVN 39
3.1.3 實驗設備 GAITRite 40
3.2 應用 SVR 於步態事件偵測演算法 42
3.2.1 資料前處理 42
3.2.2 訓練樣本特徵選擇與樣本準備 47
3.2.3 以 SVR 進行訓練與預測 48
3.2.4 結果分析方法 49
第四章 結果 51
4.1 絕對時間差異比較 51
4.2 時間步態參數差異比較 55
第五章 討論 59
第六章 結論與未來建議 62
參考文獻 63
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