研究生: |
陳佳鈺 |
---|---|
論文名稱: |
結合地板壓力與紅外線影像之跌倒偵測系統 Design of Fall Detection System with Floor Pressure and Infrared Image |
指導教授: | 曾煥雯 |
學位類別: |
碩士 Master |
系所名稱: |
電機工程學系 Department of Electrical Engineering |
論文出版年: | 2008 |
畢業學年度: | 96 |
語文別: | 中文 |
論文頁數: | 91 |
中文關鍵詞: | 紅外線 、跌倒偵測 、壓力感測 、專家系統 |
英文關鍵詞: | Infrared, Fall detection, Pressure sensor, Expert system |
論文種類: | 學術論文 |
相關次數: | 點閱:698 下載:36 |
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由於科技發展與醫療技術的精進,大家對病人照護品質也日益重視。據相關研究顯示跌倒住院約占意外事件的60%,且跌倒將導致老人健康惡化、產生併發症,除了增加家庭負擔也嚴重耗用社會醫療成本,因此跌倒意外的防範與即時偵測是提升現代生活品質的重要課題之一。本文提出一套可應用於醫療照護機構或居家的跌倒偵測系統,旨在即時偵測出跌倒行為並通報醫護人員,避免延誤就醫,減輕醫護人員及醫療成本的負擔。
本論文不同於一般的看護系統在於,使用紅外線攝影機來擷取看護環境的影像輸入,其優點在於黑暗中也可正常運作;另外本系統結合了地板壓力感測的輔助,藉此減少不必要的警報。
實驗結果驗證,在正常環境下使用簡單的偵測演算法搭配地板感測效果可提高系統偵測的正確率與可靠度。實驗結果也證實本論文所提出之跌倒偵測法,在室內環境中且允許各種路徑以及跌倒方向下,有不錯的成效。
Due to the advancement of technology and medicine, people begin to pay more attention to the quality improvement of health care. Many researches show that the fall accident occupies 60% of all accidents in a home. The fall accident may cause the condition of an elder deteriorated or producing complications. As a result, it increases the burden of a family and seriously wastes medical resources from the society. Thus, preventing the fall accident and detect it immediately is one of the important topics regarding the quality improvement of health care. This thesis proposes a reliable tele-care system that can detect the fall accident immediately, notify medical personnel when the accident occurs, prevent the patient’s condition from deteriorating due to late treatment, and reduce the burden of medical personnel.
A unique feature of the proposed system is that we use a Infrared Camera to capture images simultaneously and eliminate any blind spot. We can adjust the detection sensitivity on a case by case basis to reduce unnecessary alarms, and put more attention on the elderly with special diseases or conditions.
The experimental results show that using a simple fall detection algorithm and combining it with simple personal information can raise fall detection accuracy and reliability effectively in a particular environment. The experimental results also show that the fall detection algorithm proposed here can do a good job in an indoor environment for all fall cases.
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