一個使用環場攝影機在真實環境下的跌倒偵測系統

近年來老年人口的急劇增加，使得老年人的照護與安養問題越來越受到重視，需要投入大量的人力與物力資源才得以維護其醫療與照護的品質。本論文提出一套以視覺為基礎，使用環場攝影機且能應用於居家與醫療照護機構的跌倒偵測系統，希望當發生跌倒事件時，系統能迅速自動通知他人前往並給予最即時完善的救護，以免延誤就醫發生危險。為了讓系統能更符合真實環境中的應用，因此考量日常生活中可能會發生的真實環境因素，例如：發生光源閃爍與開關燈以及在環境中遺留靜態物品造成多重物件。前者可利用偵測亮度變化程度的大小來解決，而後者則可利用遺留物體的靜態特性加以移除。此外，依人物軀幹線角度的大小，將環場影像中的跌倒模式，區分為非輻射狀跌倒與輻射狀跌倒，在輻射狀跌倒方面更進一步再細分為向內與向外跌倒，並且對此三類跌倒模式抽取適當的特徵，包括軀幹線角度大小與長度變化量以及移動歷史軌跡等特徵，再搭配簡單的門檻值設定與決策樹進行跌倒偵測。實驗結果顯示，本系統成功的解決了光源閃爍與靜態遺留物等真實環境因素問題，大大的增加系統在真實環境中的實用性。在跌倒辨識方面，由於考量了多種跌倒模式使得系統辨識率由先前的0.73提昇至0.87，系統信賴度Kappa值由0.47提升至0.75，因此可以證明本論文提出一個能有效偵測跌倒的辨識系統。

關鍵字

居家照護；真實環境；環場攝影機；跌倒偵測；遺留物

並列摘要

In recent years, the number of the elderly increases rapidly. Consequently the health care of the elderly attracts more and more attention and we need to invest a lot of resources to maintain its quality. This thesis proposes a vision-based fall detection system using an omni-directional camera for the elderly and patients at home and in health-care institutions, hoping to detect the fall accident immediately when it happens and notify the medical personnel to provide the emergency care in time. In order to make the system more practical in real environments, we consider the practical environmental factors that may take place in our daily life. For instance, the occurrence of light source glimmer and turning a light on and off and leaving over static abandoned objects in the environment and resulting in multiple targets. The former can be solved by detecting the degree of luminance changes and the latter can be solved by using the static characteristic of abandoned objects. In addition we divide the fall down patterns in omni-directional images into non-radial and radial directions according to the angle associated with a body line. We further categorize the radial fall down patterns into inward and outward directions. We extract suitable features for these three different fall down patterns, including angle and length variation associated with the body line and Motion History Images. Given these features, a simple thresholding and decision tree technique is adopted for fall detection. Experimental results show that the proposed system has overcome the practical environmental factors of light source glimmer and static abandoned objects, increasing the practicability of the system in a real-world environment. In fall-down detection, since multiple fall-down patterns are considered, the recognition accuracy of the fall down system is improved from 0.73 to 0.87 and the Kappa value is improved from 0.47 to 0.75. These results show that we have proposed an effective fall down detecting system in this thesis.

並列關鍵字

Home Health-Care ； Abandoned Objects ； Practical Environments ； Fall Detection ； Omni-directional Camera (MapCam)

參考文獻

[2] 林茂榮以及王夷暐，社區老人跌倒的危險因子與預防，台灣公共衛生雜誌第23卷4期，民國九十三年。

[26] G. D. Hong, The Study of Integrating RFID Technology with Image Analysis in the Elders’ Falling Detection, Master Thesis, Department of Information Management, Fu Jen Catholic University, Taiwan, 2006.

[5] H. Seki, A. Kiso, and S. Tadakuma, “Omni-directional Vision Sensor Based Behavior Monitoring System Using Bayesian Network,” in Proc. of IEEE Annual Conf. on SICE, pp. 764 - 769, 2007.

[6] H. Seki and S. Tadakuma, “Abnormality Detection Monitoring System for Elderly People in Sensing and Robotic Support Room,” in Proc. of IEEE Inter. Workshop on Advanced Motion Control, pp. 56 - 61, 2008.

[7] M. L. Wang, C. C. Huang, and H. Y. Lin, “An Intelligent Surveillance System Based on an Omnidirectional Vision Sensor,” in Proc. of IEEE Conf. on Cybernetics and Intelligent Systems, pp. 1-6, 2006.

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一個使用環場攝影機在真實環境下的跌倒偵測系統

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