一個使用環場攝影機並結合個人資訊的客製化跌倒偵測系統

A Customized Human Fall Detection System Using an Omni-Directional Camera and Personal Information

宋佩栩(Pei-Hsu Sung)

指導教授 ： 繆紹綱

中原大學/工學院/電子工程研究所/碩士(2006年)

https://doi.org/10.6840/cycu200600448

摘要

由於科技發展與醫療技術的精進，大家對病人醫療之照護品質也日益重視。據相關研究顯示院內跌倒約占醫院意外事件的80%，且跌倒將導致病人病情惡化、產生併發症或延長住院時間，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醫療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，且可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，我們使用環場攝影機來擷取看護環境的影像輸入，其優點在於可360度看護無死角；另外本系統結合了個人資訊(可包含基本資料、危險因子等之電子病歷)，在偵測跌倒的過程當中，本系統會按照個人資訊進行跌倒偵測過程中之臨界值的選取與敏感度的調整，以達到本跌倒偵測看護系統客制化的目的，藉此減少不必要的警報且將資源有效應用在需要的人身上。論文中也提出另一個跌倒偵測方法，適用於各種環境下不同路徑與跌倒方向的偵測。
　　實驗結果驗證，在特定環境下使用簡單的偵測演算法搭配簡單個人資訊可有效提高系統偵測的正確率與可靠度，但當演算法本身已夠強健時，可能需要搭配生醫訊號才能有效提高辨識率。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，加入個人資訊後系統具有0.92之辨識正確率與0.92之系統可靠度。

關鍵字

環場攝影機 ； 跌倒偵測 ； 個人資訊

並列摘要

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 80% of all accidents in a hospital. The fall accident may cause the condition of a patient deteriorated, producing complications and extending the patient’s stay in the hospital. 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 MapCam to capture 360∘scense simultaneously and eliminate any blind spot. Furthermore, personal information is integrated into the system and makes it smarter by customizing the system for each individual. With personal information (including basic personal data, danger factor, electronic health history, etc), 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. We also propose another fall detection algorithm for various falling directions and walking paths.
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. When the algorithm itself is robust enough, perhaps the detection accuracy can be increased only if biomedical signals are considered as well. The experimental results also show that the new fall detection algorithm proposed here can do a good job in an indoor environment for all fall cases (different walking paths and falling directions).
　The successful recognition rate and kappa value of our system with personal information are 0.92 and 0.92, respectively, showing that we have a reliable system.

並列關鍵字

personal information ； omni-directional camera (MapCam) ； Fall detection

參考文獻

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