English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49287/83828 (59%)
Visitors : 7156260      Online Users : 73
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/87958


    Title: 應用分類於尿失禁病人骨盆肌肉訓練回饋機制
    Other Titles: Applying classification to the feedback of pelvic floor muscle training for urinary incontinence patients
    Authors: 賴聲宇;Lai, Sheng-Yu
    Contributors: 淡江大學資訊工程學系碩士班
    陳瑞發;Chen, Jui-Fa
    Keywords: 骨盆底肌肉訓練;K-means;分類演算法;PFMT;k-Means Clustering;k-Nearest Neighbor Algorithm;Support Vector Machine;classification
    Date: 2012
    Issue Date: 2013-04-13 11:54:04 (UTC+8)
    Abstract: 尿失禁患者骨盆底肌肉訓練過程中,簡稱kegel運動,患者自行施作時,容易訓練到錯誤的肌肉部位,導致復健效果不彰,望能提出一套改善骨盆底肌肉訓練的療程,使用醫療輔具將復健器材與感測器結合,並透過無線模組將數據傳輸到行動載具上,進行數據的量化、分群與分類,將復健療程延伸至家中,如醫療人員監督復健的情境。
    本文著重在如何正確判斷患者施力部位是否正確,望能透過k-Nearest Neighbor Algorithm、k-Nearest Neighbor-weight Algorithm、Regression Analysis、Support Vector Machine進行分類演算法分析並以k-Means演算法進行評估分類正確率與適用性,協助並判斷患者復健施力情況,以達快速提供回饋資訊予患者與醫療人員實現醫學復健,正確地改善療程。
    In pelvic floor muscle training(PFMT) of urinary incontinence(UI), patients would train the wrong muscle and come out results.
    In this thesis we proposed a treatment for pelvic floor muscle training through the medical aids, we combine with rehabilitation equipment and sensors. The medical equipment is used for transfer data between the wireless module and mobile device. Moreover, the system executes -the process of quantified data, clustering data and classification for extending the rehabilitation program to patient''s’ home, just like medical staffs supervised at patient’s side.
    This thesis focuses on how to properly judge the force posture of patients with the k-Nearest Neighbor algorithm, k-Nearest Neighbor-weight algorithm, Regression Analysis and Support Vector Machine. We classify and evaluate the classification accuracy and applicability to help and determine patients’ rehabilitation situation. Furthermore, we provide feedback information quickly to improve the treatment for patients and medical personnel.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML62View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback