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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72217

    Title: 結合類神經網路及Kinect深度攝影機之跌倒偵測系統;A Fall Detect System Based on Neural Networks with Kinect Depth-Camera
    Authors: 廖家偉;Liao,Jia-Wei
    Contributors: 資訊工程學系
    Keywords: Kinect;跌倒偵測;類神經網路;影像監控;Kinect;fall detect system;video surveilleance;eldercare;neural netwroks
    Date: 2016-08-08
    Issue Date: 2016-10-13 14:33:03 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來社會面臨老年化日趨嚴重的問題,老人照護議題也越發重要;老
    偵測的相關研究越來越蓬勃發展。本論文開發一套基於結合Kinect 及類神
    實生活。本系統使用Kinect 所提供之深度資訊,找出場景中的地面資訊,
    能因素。在全部的情境中總共有168 次跌倒事件以及168 次未跌倒事件,
    Kappa 值為0.84,證明系統有幾乎與事實吻合的程度。;Recently, society is faced with the problematic issue of an aging population.
    The eldercare issue is extremely important. The frequency of falls in the elderly
    is higher than in younger people with a greater risk caused by treatment delay.
    Therefore, the research of fall detection systems has been increasing drastically.
    This thesis proposes to develop a fall detection system based on neural networks
    with Kinect depth-camera. We hope it can operate reliable in a complex
    environment or in multi-person scenarios. The system uses raw data of Kinect
    depth images to locate the ground in the scene, identify the foreground pixels with
    a background subtraction algorithm, and then tracked the foreground for analysis.
    Last, the system will judge whether the fall events occurred by using its welltrained
    neural networks model and the specified features. When fall events are
    detected, the system would record the image and time immediately, then report to
    caregivers for efficient aid. Additionally, this thesis will discuss the reasons for
    rule decision system’s misjudgment and the advantages of using neural networks.
    The performance of the proposed system was verified by six experimental
    scenarios. There are three single person and for multi-person experimental
    scenarios. After these experiments, we would compare the result of rule decision
    system with the proposed system and discuss the difference and the reason of
    misjudgment between both of them. Among all of these experimental scenarios:
    168 are fall events and 168 are not fall events. The results show the sensitivity
    rate and the specificity rate were 97% and 90%, respectively. And the Kappa value
    of the proposed system is 0.84 which is higher than 0.80, showing that we have a
    reliable system that accurately reflects reality in terms of fall events.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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