Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/39710
Title: การตรวจจับการล้มโดยใช้การวิเคราะห์องค์ประกอบหลักและเทคนิคการจัดกลุ่ม
Other Titles: Fall detection using principal component analysis and clustering techniques
Authors: ศันสนีย์ เอื้อพันธ์วิริยะกุล
ฤทธิพงศ์ วงค์เขื่อนแก้ว
Keywords: การล้ม
การจัดกลุ่ม
Issue Date: 2557
Publisher: เชียงใหม่ : บัณฑิตวิทยาลัย มหาวิทยาลัยเชียงใหม่
Abstract: This thesis proposes the techniques, methodologies and processes to detect the fall with Kinect camera. The proposed method depends on the shape of human actions. The process is divided into 2 phases, i.e., the recognition phase and the fall detection phase. In the recognition phase, we use 4 fuzzy clustering algorithms, i.e., Fuzzy C-Means Clustering (FCM), Gustafson Kessel Clustering (GK), Gath and Geva Clustering (GG), and Possibilistic C-Means Clustering (PCM) to cluster the action groups. The utilized features are generated from the Hu moment invariants combining with Principal Component Analysis (PCA). Then K-Nearest Neighbor (KNN) and Fuzzy K-Nearest Neighbor (FKNN) with the ellipse approximation of shape orientation and the fuzzy rules are used in assigning an action to each frame. Then the action of several consecutive frames is used to determine whether there is the fall occurring or not. From the experiment, the best recognition method is from GG clustering with 80 prototypes per clusters with 7 Principal Component (7PCs) and 3 FKNN. This method yields 93.80% accuracy. For the fall detection phase with the same scheme, the best result of user dependent and user independent are 92.05% and 92.65% respectively. In addition, there are 3 cameras set up in the experiments. We found that the result of 1 from 3 cameras and 2 from 3 cameras that can detect the fall are around 93.05%-99.03% and 94.32%-98.66% for user dependent and user independent data sets, respectively.
URI: http://repository.cmu.ac.th/handle/6653943832/39710
Appears in Collections:ENG: Theses

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Appendix.pdfAPPENDIX246.94 kBAdobe PDFView/Open    Request a copy
Chapter1.pdfCHAPTER 1562.31 kBAdobe PDFView/Open    Request a copy
Chapter2.pdfCHAPTER 21.14 MBAdobe PDFView/Open    Request a copy
Chapter3.pdfCHAPTER 3498.4 kBAdobe PDFView/Open    Request a copy
Chapter4.pdfCHAPTER 4360.41 kBAdobe PDFView/Open    Request a copy
Chapter5.pdfCHAPTER 54.01 MBAdobe PDFView/Open    Request a copy
Content.pdfCONTENT356.81 kBAdobe PDFView/Open    Request a copy
Cover.pdfCOVER1.31 MBAdobe PDFView/Open    Request a copy
Reference.pdfREFERENCE252 kBAdobe PDFView/Open    Request a copy


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