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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/87936

    Title: 骨盆底肌肉訓練之數據分群
    Other Titles: The data clustering of pelvic floor muscle training
    Authors: 林恪任;Lin, Ko-Jen
    Contributors: 淡江大學資訊工程學系碩士班
    Keywords: 集群分析;骨盆底肌肉運動;K-means;DBSCAN;SLINK;Clustering Analysis;Pelvic Floor Muscle Training (PFMT)
    Date: 2012
    Issue Date: 2013-04-13 11:53:06 (UTC+8)
    Abstract:       透過安裝於骨盆底肌肉訓練輔助器上的感測器,可以取得如施力、時間等資訊,分析這些資料並將其分群,提供給醫師判斷,賦予其專業認定的特性給各cluster。這些學習後的資料便成為患者的個人化參考依據,讓病人不在醫院時也能正確練習。
          By attaching a pressure sensor on the device of pelvic floor muscle training (PFMT), we can collect data such as force and time. After data clustering, the proposed system can provide the PFMT data to the doctor. The doctor identifies the cluster by his/her professional knowledge. This identified data becomes the personal training data of the patient.
          The purpose of this thesis is studying three kinds of clustering algorithms, and implementing it for clustering the data of PFMT. In centroid-based, we reference "k-means". In density-based, we propose "density-split" which is inspired by DBSCAN. Finally, in connectivity- based, we propose "chain" which based on the concept of SLINK. Besides, in order to solve the problem caused by noise. We propose a method that excludes noise before clustering which can improve clustering result, and provide more accurate training data for clinical use.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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