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研究生:石柏良
研究生(外文):Bo-liang Shih
論文名稱:利用灰關聯分析法建立即時手部動作辨識系統
論文名稱(外文):Using Grey Relational Analysis to Develop a Real-time Hand Motion Identification System
指導教授:陳天送陳天送引用關係
指導教授(外文):Tain-song Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:醫學工程研究所碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:110
中文關鍵詞:手部動作辨識系統表面肌電圖(SEMG)灰關聯分析法(GRA)資料手套
外文關鍵詞:Hand Motion Identification (HMI)Grey Relational Analysis (GRA)Data GloveSurface electromyography (SEMG)
相關次數:
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近幾年來,手部動作辨識在工程及臨床領域上均有很多的貢獻,而表面肌電圖(Surface electromyography)為一常用於辨識手部動作的方法,此方法的優點在於具有非侵入式且簡單擺放即可偵測到人體活動的情況。但是,對於輕微動作的辨識是較為不好的,特別是手指動作。針對此種情況,本研究將利用資料手套的方式加以改善,此方法是將感測元件安置於手套上,當穿戴手套時即可偵測到當手指發生彎曲及移動時相對的電壓變化。為了增加可辨識的手部動作數量以及系統的準確性,本研究將建立一套整合表面肌電訊號及資料手套的可攜式手部動作辨識系統。本研究將分為三個部份,第一個部份:手部動作擷取系統的建立,包含肌電訊號腕套及資料手套的設計與製作,第二個部份:訊號前置處理及特徵值的擷取,於表面肌電訊號我們使用六種參數,分別為IEMG, WL, VAR, ZC, SSC, 及WAMP,而在資料手套上則使用三種參數,分別為Area, LC, 及MV,用於代表這些動作的特徵圖像。第三個部份:建立辨識系統核心利用灰關聯分析法;此外,本研究將實際測試十位正常受測者於二十種手部動作的辨識情況及�S定度,並與過去常應用於辨識決策系統的類神經網路做一比較。由實驗的結果證明,我們所提議出來的非侵入式手部動作辨識系統方法,比較於過去傳統的類神經網路演算法,具有高的辨識率,好的適應性及快速的運算效率。
In recent years, researches and the applications of hand motion identification (HMI) have been a major contribution in both engineering and clinical medicine. Surface electromyography (SEMG) is one of the main methods of HMI. One of the major advantages of SEMG is noninvasive and simple detection for human body. However, it is difficult to identify minute hand motions by pure SEMG, e.g. finger motion. In addition, data glove, a device with the sensor embedded, is used to detect the relative signal changes while fingers or hands move. In order to increase the number of recognizable hand motions and improve the accuracy and discriminatory power, this study established a portable HMI System, combined the SEMG and data glove. This system composed of three parts: The first part is the extracting system, which combines SEMG with data glove. The second is the part for preprocessing signals and extracting features of hand motion. We used six methods for SEMG, including IEMG, WL, VAR, ZC, SSC, and Willison Amplitude (WAMP). We also used three methods for data glove, including Area, LC, and MV. The third part is the one for utilizing Gray relational analysis (GRA). The results were processed with neural network method to evaluate the recognition rate and the stability of algorithm by testing ten normal subjects in twenty different hand motions. These demonstrated that our establishment was efficient as a non-invasive method of hand motion recognition. Compared to artificial neural network, our results also showed that HMI had higher accurate recognition rate, better adaptability, and shorter processing time.
中文摘要…………………………………………………………………I
英文摘要…………………………………………………………………Ⅱ
誌謝………………………………………………………………………Ⅲ
目錄………………………………………………………………………Ⅳ
圖目錄……………………………………………………………………Ⅶ
表目錄…………………………………………………………………ⅩⅠ
第一章 緒論………………………………………………………………1
第1-1節 研究動機………………………………………………………1
第1-2節 文獻回顧………………………………………………………4
第1-3節 研究目的………………………………………………………9
第1-4節 論文架構………………………………………………………12
第二章 研究原理與方法…………………………………………………13
第2-1節 表面肌電訊號與手部動作之間的發生機制…………………13
第2-1-1節 肌電細胞與神經連結………………………………………13
第2-1-2節 肌肉活化過程………………………………………………14
第2-1-3節 表面肌電訊號的加總效應…………………………………16
第2-2節 非侵入式電極的分類與特性…………………………………18
第2-3節 資料手套與資料手套之間的關係……………………………20
第2-3-1節 資料手套的種類與特性……………………………………20
第2-4節 訊號處理與特徵擷取的方法…………………………………22
第2-4-1節 訊號處理與特徵值的計算…………………………………22
第2-4-2節 肌電訊號特徵化的方法……………………………………23
第2-4-3節 資料手套特徵化的方法……………………………………25
第2-5節 辨識系統之演算法……………………………………………26
第2-5-1節 倒傳遞式類神經網路………………………………………26
第2-5-2節 灰關聯分析法………………………………………………32
第三章 系統實現…………………………………………………………38
第3-1節 系統架構………………………………………………………38
第3-2節 表面肌電訊號擷取系統………………………………………39
第3-2-1節 主動式電極…………………………………………………39
第3-2-2節 主動式電極腕套……………………………………………43
第3-2-3節 PCMCIA A/D電源轉接介面………………………………44
第3-3節 類比轉數位訊號系統…………………………………………46
第3-3-1節 資料擷取卡…………………………………………………46
第3-4節 類比轉數位訊號系統…………………………………………47
第3-4-1節 元件介紹……………………………………………………47
第3-4-2節 Flex Sensor 訊號轉換擷取模組……………………………48
第3-4-3節 即時手部動作辨識系統……………………………………50
第3-5節 手部動作之設計………………………………………………51
第3-6節 軟體部份………………………………………………………55
第3-6-1節 離線(off-line)分析…………………………………………57
第3-6-1-1節 前置訊號處理……………………………………………57
第3-6-1-2節 特徵化處理………………………………………………58
第3-6-1-3節 倒傳遞類神經網路………………………………………58
第3-6-1-4節 灰關聯分析法……………………………………………59
第3-6-2節 線上(on-line)分析…………………………………………60
第3-6-2-1節 手部動作訓練系統………………………………………60
第3-6-2-2節 手部動作辨識系統………………………………………62
第四章 實驗結果與討論………………………………………………64
第4-1節 訊號擷取系統測試結果………………………………………64
第4-1-1節 實測表面肌電訊號…………………………………………64
第4-1-2節 資料手套之測試……………………………………………65
第4-2節 軟體演算法效能的測試………………………………………70
第4-2-1節 實驗設計……………………………………………………70
第4-2-2節 動作區間之判定……………………………………………71
第4-2-3節 特徵化參數分類效果之評估………………………………73
第4-3節 辨識結果分析與比較…………………………………………76
第4-3-1節 決策矩陣比較………………………………………………76
第4-3-2節 運算時間的比較……………………………………………81
第4-3-3節 系統訓練次數的評估………………………………………82
第4-3-3-1節 辨識率的評估……………………………………………83
第4-3-3-2節 重覆錯誤率的評估………………………………………86
第4-3-4節 灰關聯分析法熵值的調整…………………………………89
第4-3-5節 動作快慢與辨識率之間的關係……………………………90
第4-3-6節 系統之外觀…………………………………………………91
第4-4節 問題與討論……………………………………………………92
第五章 結論與未來展望…………………………………………… 93
第5-1節 結論……………………………………………………………93
第5-2節 未來展望………………………………………………………94
參考文獻…………………………………………………………………96
附錄………………………………………………………………………99
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