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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張璞曾 | |
dc.contributor.author | Bo-Tang Hsiao | en |
dc.contributor.author | 蕭柏堂 | zh_TW |
dc.date.accessioned | 2021-06-16T17:32:58Z | - |
dc.date.available | 2014-08-19 | |
dc.date.copyright | 2012-08-19 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-15 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64165 | - |
dc.description.abstract | 心血管疾病患者常常需要經由醫師來確定患者的安全可運動強度與設計運動處方;肥胖者需要透過自主管理來控制體重。對於具有以上需求的使用者,發展一套可長時間監控的個人化運動強度紀錄系統,有其存在之必要性。
本文提出一個可行的個人化步態辨識與行走運動強度估測系統。在步態辨識部分,本文使用αβ濾波器得到精確的運動姿態,並將該姿態經過經驗模態分解(Empirical Mode Decomposition,EMD)濾除雜訊後提取傅利葉能量譜,進行線性鑑別式分析(Linear Discriminant Analysis,LDA)訓練與辨識。當辨識為走路狀態時,即進行行走運動強度估測。本文同時探討走路時物理慣性做功與運動強度的相關性。使用二次逼近與經驗模態分解(Empirical Mode Decomposition,EMD)之殘餘函數濾除加速規本身的基線飄移對於積分的嚴重影響,得到準確的瞬時速度,進而提升運動強度計算的準確性。 本文針對5男5女共10位受試者,測試四種步態(上樓、下樓、走路、跑步)之個人步態辨識在30%訓練樣本時,剩餘70%作驗證之平均準確率可達90%以上。每位受試者並進行10次不同行走速度的測試,在步數計算之平均準確率可達95%,並改善Y. Kurihara計算運動強度的方式,其迴歸方程式之相關性可由原本的0.55提升至0.81,證明本研究所採用之方法確實可提高運動強度估測之準確性。最後實作於智慧型手機,並於PC端開發使用者介面,協助了解運動中的姿態變化與演算法驗證,日後可以進一步達到遠端監控、雲端儲存、自主管理等目的。 | zh_TW |
dc.description.abstract | Cardiovascular disease patients need suggestions from doctors for an acceptable level of exercise intensity and exercise prescription. And over-weight patients control their weight by self-management. Developing a monitoring system which can record personalized exercise intensity is necessary for above patients.
This thesis proposes a feasible system for personalized gait recognition and walking exercise intensity estimation. On analyzing gait recognition, this thesis uses α-β filters to obtain better athletic attitudes, and further uses Empirical Mode Decomposition (EMD) to filter noise of athletic attitude to acquire Fourier Transform energy spectrum. Thus, the Linear Discriminant Analysis (LDA) can apply to this energy spectrum for training and recognition. When the motion is recognized as walking, the walking exercise intensity is estimated. This thesis also discusses the correlation between inertia work and exercise intensity by using residual function of Empirical Mode Decomposition (EMD) and quadratic approximation to filter the baseline shift of acceleration sensor to reduce the serious integral effect. And further we are can derive better exercise intensity and instantaneous speed. This thesis uses measured 10 subjects including 5 males and 5 females to recognize four types of gait from upstairs, downstairs, walking, and running. For gait recognition, 30% of collected raw data is used for training samples, and recognition rate of verifying the 70% data can reach more than 90%. After applying our method to ten different walking speeds from each subject, we found that step calculation shows 95% accuracy, and Y.Kurihara’s exercise intensity method can be enhanced, and the regression equation correlation can be increased from 0.55 to 0.81. These results prove our method can improve exercise intensity estimation. The proposed method has been implemented on smart phones and graphic user interface on personal computers to help understanding the change of athletic stance and to verify the proposed algorithm for further application on remote monitoring, cloud computing, and self-management. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T17:32:58Z (GMT). No. of bitstreams: 1 ntu-101-P99945001-1.pdf: 20769011 bytes, checksum: e9ac07e389fffb89d0f1783b84f36ef3 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii 總目錄 v 圖目錄 viii 表目錄 x 第一章 緒論 1 1.1 前言 1 1.2 研究背景與文獻探討 2 1.3 研究動機與目的 6 1.4 論文架構 9 第二章 系統架構與設計 10 2.1 系統架構介紹 10 2.2 Client端系統架構 11 2.3 Server端系統架構 14 第三章 研究方法與原理 16 3.1 研究方法介紹 16 3.2 姿態擷取 17 3.2.1 姿態表示法 17 3.2.2 αβ濾波器與演算法簡介 20 3.2.3 姿態擷取程式介面 21 3.3 步態訓練與辨識 22 3.3.1 步態向量前處理流程 22 3.3.2 線性鑑別式分析(LDA)簡介 27 3.3.3 步態辨識決策方式 32 3.3.4 步態辨識程式介面 33 3.4 步數與跨步週期計算 35 3.4.1 演算法簡介 35 3.4.2 經驗模態分解法(EMD)與正交性指數(OI)簡介 36 3.4.3 演算法流程 37 3.5 行走運動強度估測 39 3.5.1 運動強度估測簡介 39 3.5.2 物理慣性作功與運動強度的關係 39 3.5.3 慣性元件的誤差問題 43 3.5.4 慣性元件的誤差改善方式 44 3.5.5 零速更新法 46 3.5.6 運動強度計算相關文獻比較 48 第四章 系統驗證與受試者實驗 50 4.1 驗證與實驗介紹 50 4.2 演算法驗證 50 4.3 受試者實驗 60 4.3.1 受試者測試之探討項目 60 4.3.2 受試者資料與測試流程 61 第五章 受試者實驗結果與討論 63 5.1 受試者實驗項目介紹 63 5.2 受試者步數與跨步頻率實驗 63 5.3 步態辨識實驗 65 5.3.1 受試者步態辨識實驗 65 5.3.2 辨識效能分析 68 5.4 受試者行走運動強度實驗 72 第六章 結論與未來工作 78 6.1 結論 78 6.2 未來工作 80 參考文獻 81 | |
dc.language.iso | zh-TW | |
dc.title | 步態辨識與行走運動強度估測系統之研究 | zh_TW |
dc.title | Gait Recognition and Walking Exercise Intensity Estimation System | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林伯星,陸哲駒,詹曉龍 | |
dc.subject.keyword | 運動強度,步態辨識,線性鑑別式分析,經驗模態分解, | zh_TW |
dc.subject.keyword | Exercise Intensity,Gait Recognition,Linear Discriminant Analysis(LDA),Empirical Mode Decomposition(EMD), | en |
dc.relation.page | 85 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-08-15 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
顯示於系所單位: | 生醫電子與資訊學研究所 |
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