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

• Main Authors: Pei-Hsu Sung, 宋佩栩
• Other Authors: Shaou-Gang Miaou
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/22546835967301344668

碩士 === 中原大學 === 電子工程研究所 === 94 === 　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.