應用於醫療環境的全方位視覺監控系統; An Omni-vision Surveillance System for Medical Environment

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题名:	應用於醫療環境的全方位視覺監控系統;An Omni-vision Surveillance System for Medical Environment
作者:	詹朝壹;Chao-yi Chan
贡献者:	生物醫學工程研究所
关键词:	監控系統;全方位攝影機;surveillance system;omni-directional camera
日期:	2011-07-21
上传时间:	2012-01-05 14:33:14 (UTC+8)
摘要:	由於經濟的發展，帶動了生活水平的提升，使得人們的平均壽命逐漸提高，因而對於健康照護及醫療品質更加重視。然而在一般醫療機構中，往往因為看護人員的疏失或病人本身的問題，發生意外而導致病人病情加重。在本研究中，我們提出一套適合醫療環境的監控系統，即時監視周遭人員以減少上述意外所造成的後果。監視內容包含人員跌倒與蹲下、病人擅離病房與病床、及人員軌跡紀錄。在硬體架構上，本實驗所採用的取像設備為全方位攝影機 (omnidirectional camera)，能拍攝到360度的環場影像，比一般攝影機能取得更廣的影像範圍。在人員跌倒與蹲下偵測上，我們提出以主成份分析(principal component analysis, PCA) 為基準及以梯形區域高度變化為基準的偵測方法。以PCA為基準的方法是先用PCA找出人體主軸方向，同時透過主軸方向來計算人體在主軸方向上的長度。利用主軸的方向與長度，判斷人員是否跌倒與蹲下。而以梯形區域高度為基準的偵測方法主要是利用框到人體的梯型區域高度來做跌倒與蹲下判斷。在病人擅離病床偵測方面，首先框選出病床區域，當系統偵測到病人不在此區域時，即判斷為病人離開。不過單純使用這樣的判斷準則，容易造成誤判；因此當系統偵測到病人在病床區域時，病人也必須是呈現躺平狀態才能真正判斷在病床上。偵測病人為躺平狀態所用的判斷方法和人員跌倒與蹲下的偵測方法相同。在病人擅離病房的偵測上，先標示出門底所在位置，接著計算代表此門底線的直線方程式；當應用時，先偵測出病人腳點所在位置，將每個腳點所在的座標值，代入直線方程式來判斷病人進出病房的狀況。在人員軌跡紀錄方面，先將扭曲的全方位影像做扭曲校正，得到了扭曲校正後的影像，再取出影像中人的腳點位置來定義軌跡。由實驗結果顯示，以主成份分析為基準及以梯形區域高度變化為基準的人員跌倒與蹲下偵測率分別為93% 與92%；病人擅離病床偵測率為 95%；而病人擅離病房的偵測率為95%。 In these decades, the economic situation and the living quality of human are quickly improved. Human life is consequentially lengthened. The long life brings a side effect; it is that the old-man disease is heavily increased. Thus people pay more attention to these kinds of health care. However, some kinds of accidents are occurred due to the negligence of nurses, the patient's conditions are more deteriorated. To reduce the deterioration, we propose an environmental monitoring system for such kinds of health care in this thesis. There are four kinds of monitoring in the proposed system : (i) patient fall down and crouch, (ii) leaving bed without permission, (iii) going out ward without permission, and (iv) walking trajectory. In this study, we use an omni-directional camera to capture images for monitoring. The camera can easily take 360-degree surround images. In the fall down and crouch detection, we use principal component analysis to detect the main direction and length of the personal body, and then determine the patient situation. Another method is using the change of the height of the patient trapezoidal bounding box. In the leaving bed detection, we detect whether the patient leaves the pre-defined region or not. However, the criterion tends to result in a unstable judgment; thus we need to check whether the patient really lies flat on the bed. In the leaving ward detection, we first define a linear equation to represent the bottom of a door; if the foot point of a patient is passing through the boundary, the patient is judged leaving ward. In the walking trajectory detection, the foot point of a patient is recorded frame after frame to construct the walking trajectory on the calibrated omni-directional images. The detection rate of the fall down and crouch detection using principal component analysis is 93%, and the detection rate of that using the height of the patient trapezoidal bounding box is 92%; the detection rate of leaving bed is 95%; and the detection rate of leaving ward is 95%.
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